From 93382a034ceda0cf19e3ad1349023364306d71bb Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 13:35:02 +0530
Subject: [PATCH 01/11] Create requirements.txt

---
 .../requirements.txt                                       | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /requirements.txt

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /requirements.txt b/Sentiment Analysis for Restaurant Reviews (NLP) /requirements.txt
new file mode 100644
index 000000000..43c58ebc4
--- /dev/null
+++ b/Sentiment Analysis for Restaurant Reviews (NLP) /requirements.txt	
@@ -0,0 +1,7 @@
+numpy==1.19.2
+pandas==1.4.3
+matplotlib==3.7.1
+scikit-learn~=1.0.2
+seaborn==0.10.1
+nltk==3.8.1
+wordcloud==1.9.3

From 1f864cbe17b381b4b0b0468e0c53b9fc026680fb Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 13:35:34 +0530
Subject: [PATCH 02/11] Create h

---
 Sentiment Analysis for Restaurant Reviews (NLP) /Model/h | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Model/h

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /Model/h b/Sentiment Analysis for Restaurant Reviews (NLP) /Model/h
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/Sentiment Analysis for Restaurant Reviews (NLP) /Model/h	
@@ -0,0 +1 @@
+

From c2e9138a45528e72b928ed7336c29c4ea50397d9 Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 13:35:56 +0530
Subject: [PATCH 03/11] Add files via upload

---
 ...nalysis_for_Restaurant_Reviews_(NLP).ipynb | 5647 +++++++++++++++++
 1 file changed, 5647 insertions(+)
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Model/Sentiment_Analysis_for_Restaurant_Reviews_(NLP).ipynb

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /Model/Sentiment_Analysis_for_Restaurant_Reviews_(NLP).ipynb b/Sentiment Analysis for Restaurant Reviews (NLP) /Model/Sentiment_Analysis_for_Restaurant_Reviews_(NLP).ipynb
new file mode 100644
index 000000000..9bee6989f
--- /dev/null
+++ b/Sentiment Analysis for Restaurant Reviews (NLP) /Model/Sentiment_Analysis_for_Restaurant_Reviews_(NLP).ipynb	
@@ -0,0 +1,5647 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **IMPORT MODULES**"
+      ],
+      "metadata": {
+        "id": "AMgMxW9OzcKZ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 315,
+      "metadata": {
+        "id": "KoqngUye8M2Z",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "926deb03-d9b8-4561-b341-62aac548bf29"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
+            "[nltk_data]   Package stopwords is already up-to-date!\n"
+          ]
+        }
+      ],
+      "source": [
+        "#step 1: import modules\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "%matplotlib inline\n",
+        "import warnings\n",
+        "from numpy import math\n",
+        "import nltk\n",
+        "nltk.download('stopwords')\n",
+        "import string\n",
+        "from nltk.corpus import stopwords\n",
+        "from wordcloud import WordCloud\n",
+        "#VECTORIZATION\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.feature_extraction.text import CountVectorizer\n",
+        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.linear_model import LogisticRegression\n",
+        "from sklearn.naive_bayes import GaussianNB\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.tree import DecisionTreeClassifier\n",
+        "from sklearn.svm import SVC\n",
+        "from sklearn.metrics import confusion_matrix\n",
+        "from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score,  roc_auc_score, classification_report, confusion_matrix, roc_curve, auc\n",
+        "warnings.filterwarnings('ignore')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **LOAD DATASETS**"
+      ],
+      "metadata": {
+        "id": "Yw1n9h8jzxUP"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "()#step 2: load datasets\n",
+        "df = pd.read_csv(\"/content/drive/MyDrive/Restaurant_Reviews.tsv\", delimiter='\\t', quoting=3)\n",
+        "#this is the dataset i used, you can click on this link: https://skillsbuild.edunetworld.com/courses/ai/sentiment/story_content/external_files/Restaurant_Reviews.tsv"
+      ],
+      "metadata": {
+        "id": "tlt_2LyfEMCv"
+      },
+      "execution_count": 316,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **EDA**"
+      ],
+      "metadata": {
+        "id": "LVYjTv9t0Qb8"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#prints out first 5 row of df\n",
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "gAlnwraMEXlh",
+        "outputId": "462c0b08-02fd-4b0d-d8cb-ac3987e3c193"
+      },
+      "execution_count": 317,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                              Review  Liked\n",
+              "0                           Wow... Loved this place.      1\n",
+              "1                                 Crust is not good.      0\n",
+              "2          Not tasty and the texture was just nasty.      0\n",
+              "3  Stopped by during the late May bank holiday of...      1\n",
+              "4  The selection on the menu was great and so wer...      1"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-0bc8db4b-95b2-48ed-9d39-6084b47354b3\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
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+              "      <th>Liked</th>\n",
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+              "      <td>Wow... Loved this place.</td>\n",
+              "      <td>1</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Crust is not good.</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Not tasty and the texture was just nasty.</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Stopped by during the late May bank holiday of...</td>\n",
+              "      <td>1</td>\n",
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+              "    <tr>\n",
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+              "      <td>The selection on the menu was great and so wer...</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0bc8db4b-95b2-48ed-9d39-6084b47354b3')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-0bc8db4b-95b2-48ed-9d39-6084b47354b3 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-0bc8db4b-95b2-48ed-9d39-6084b47354b3');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-114c6f11-1400-4eed-90d3-cbbb456bf8fc\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-114c6f11-1400-4eed-90d3-cbbb456bf8fc')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-114c6f11-1400-4eed-90d3-cbbb456bf8fc button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"They were excellent.\",\n          \"Your servers suck, wait, correction, our server Heimer sucked.\",\n          \"Will be back again!\"\n        ],\n        \"num_unique_values\": 996,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Liked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 317
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head().T\n",
+        "#changes configuration of table"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 129
+        },
+        "id": "tY-k-g6aEfLN",
+        "outputId": "43383406-dae5-47dc-9726-efd8ec86c82f"
+      },
+      "execution_count": 318,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                               0                   1  \\\n",
+              "Review  Wow... Loved this place.  Crust is not good.   \n",
+              "Liked                          1                   0   \n",
+              "\n",
+              "                                                2  \\\n",
+              "Review  Not tasty and the texture was just nasty.   \n",
+              "Liked                                           0   \n",
+              "\n",
+              "                                                        3  \\\n",
+              "Review  Stopped by during the late May bank holiday of...   \n",
+              "Liked                                                   1   \n",
+              "\n",
+              "                                                        4  \n",
+              "Review  The selection on the menu was great and so wer...  \n",
+              "Liked                                                   1  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-7e9c524b-cf68-4960-aa51-d004d6f173f0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>0</th>\n",
+              "      <th>1</th>\n",
+              "      <th>2</th>\n",
+              "      <th>3</th>\n",
+              "      <th>4</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Review</th>\n",
+              "      <td>Wow... Loved this place.</td>\n",
+              "      <td>Crust is not good.</td>\n",
+              "      <td>Not tasty and the texture was just nasty.</td>\n",
+              "      <td>Stopped by during the late May bank holiday of...</td>\n",
+              "      <td>The selection on the menu was great and so wer...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Liked</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7e9c524b-cf68-4960-aa51-d004d6f173f0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-7e9c524b-cf68-4960-aa51-d004d6f173f0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-7e9c524b-cf68-4960-aa51-d004d6f173f0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-b9af865c-2be2-4700-b45b-cd4d0e376865\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b9af865c-2be2-4700-b45b-cd4d0e376865')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-b9af865c-2be2-4700-b45b-cd4d0e376865 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"#changes configuration of table\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          1,\n          \"Wow... Loved this place.\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          0,\n          \"Crust is not good.\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 2,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          0,\n          \"Not tasty and the texture was just nasty.\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 3,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          1,\n          \"Stopped by during the late May bank holiday off Rick Steve recommendation and loved it.\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 4,\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          1,\n          \"The selection on the menu was great and so were the prices.\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 318
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape\n",
+        "#decribes the dimension of dataframe"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "symWReMhEkAJ",
+        "outputId": "2331102a-e121-45db-e16e-9e5fb9f62acf"
+      },
+      "execution_count": 319,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1000, 2)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 319
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "0iSUvXkEE6lR",
+        "outputId": "4c3a6f89-b1f4-4bc6-dba0-0ea6f2d81c94"
+      },
+      "execution_count": 320,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "            Liked\n",
+              "count  1000.00000\n",
+              "mean      0.50000\n",
+              "std       0.50025\n",
+              "min       0.00000\n",
+              "25%       0.00000\n",
+              "50%       0.50000\n",
+              "75%       1.00000\n",
+              "max       1.00000"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8d72c286-ee6a-4a80-9912-ce97233a5fd6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Liked</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1000.00000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>0.50000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>0.50025</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.00000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>0.00000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>0.50000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>1.00000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>1.00000</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d72c286-ee6a-4a80-9912-ce97233a5fd6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8d72c286-ee6a-4a80-9912-ce97233a5fd6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8d72c286-ee6a-4a80-9912-ce97233a5fd6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-2bda86f2-9f10-4a9b-bbca-6728a2f4786c\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2bda86f2-9f10-4a9b-bbca-6728a2f4786c')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-2bda86f2-9f10-4a9b-bbca-6728a2f4786c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Liked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 353.3768033931272,\n        \"min\": 0.0,\n        \"max\": 1000.0,\n        \"samples\": [\n          0.5,\n          1.0,\n          0.5002501876563868\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 320
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['Liked'].value_counts()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CdPpnKkPE9pt",
+        "outputId": "91ddbebb-7c19-4861-d835-86878774743a"
+      },
+      "execution_count": 321,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1    500\n",
+              "0    500\n",
+              "Name: Liked, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 321
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "HENCE OUR DATASET HAVE EQUAL NUMBER OF POSITIVE AND NEGATIVE REVIEWS."
+      ],
+      "metadata": {
+        "id": "ZBb0xHQNy38M"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['Liked'].describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CBKP-_6jFH5L",
+        "outputId": "8802a392-03d7-40a1-b341-100c6f1210b8"
+      },
+      "execution_count": 322,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    1000.00000\n",
+              "mean        0.50000\n",
+              "std         0.50025\n",
+              "min         0.00000\n",
+              "25%         0.00000\n",
+              "50%         0.50000\n",
+              "75%         1.00000\n",
+              "max         1.00000\n",
+              "Name: Liked, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 322
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(6, 3))\n",
+        "sns.countplot(data=df, x='Liked', color='red',)  # 'red' is the color\n",
+        "plt.title('Count Positive and Negative Reviews')\n",
+        "plt.xlabel('Review')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 333
+        },
+        "id": "ryqHUtrK4yrw",
+        "outputId": "ece808eb-2c23-4279-b77c-fa63195dc88f"
+      },
+      "execution_count": 323,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x300 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(6, 3))\n",
+        "like_counts=df['Liked'].value_counts()\n",
+        "plt.pie(like_counts, labels=like_counts.index, autopct='%1.1f%%', colors=['pink', 'yellow'])  # 'red' is the color\n",
+        "plt.title('Count Positive and Negative Reviews')\n",
+        "\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 290
+        },
+        "id": "qZL57oNB6hoy",
+        "outputId": "5ff25396-0b1a-4e00-acd7-c674cbf4222b"
+      },
+      "execution_count": 324,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x300 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['length']=df['Review'].apply(len)\n",
+        "#creating a new column called 'length' which gives length of each review"
+      ],
+      "metadata": {
+        "id": "ku5p1kxzFStP"
+      },
+      "execution_count": 325,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "zd_kUxuNGAmq",
+        "outputId": "53d12ca2-7c8d-4ee7-8d72-a258fdb5cc0c"
+      },
+      "execution_count": 326,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                              Review  Liked  length\n",
+              "0                           Wow... Loved this place.      1      24\n",
+              "1                                 Crust is not good.      0      18\n",
+              "2          Not tasty and the texture was just nasty.      0      41\n",
+              "3  Stopped by during the late May bank holiday of...      1      87\n",
+              "4  The selection on the menu was great and so wer...      1      59"
+            ],
+            "text/html": [
+              "\n",
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+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Review</th>\n",
+              "      <th>Liked</th>\n",
+              "      <th>length</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Wow... Loved this place.</td>\n",
+              "      <td>1</td>\n",
+              "      <td>24</td>\n",
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+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Crust is not good.</td>\n",
+              "      <td>0</td>\n",
+              "      <td>18</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Not tasty and the texture was just nasty.</td>\n",
+              "      <td>0</td>\n",
+              "      <td>41</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Stopped by during the late May bank holiday of...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>87</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>The selection on the menu was great and so wer...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>59</td>\n",
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+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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+              "  </style>\n",
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+              "    <script>\n",
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+              "        document.querySelector('#df-dea0d1f9-302b-499f-a5ee-d835dcb727ed button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-dea0d1f9-302b-499f-a5ee-d835dcb727ed');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "\n",
+              "<div id=\"df-847f1e03-92e2-432c-b927-64cf242e4fee\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-847f1e03-92e2-432c-b927-64cf242e4fee')\"\n",
+              "            title=\"Suggest charts\"\n",
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+              "\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-847f1e03-92e2-432c-b927-64cf242e4fee button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"They were excellent.\",\n          \"Your servers suck, wait, correction, our server Heimer sucked.\",\n          \"Will be back again!\"\n        ],\n        \"num_unique_values\": 996,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Liked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32,\n        \"min\": 11,\n        \"max\": 149,\n        \"samples\": [\n          122,\n          146\n        ],\n        \"num_unique_values\": 134,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 326
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.length.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tDIXvidDGDyo",
+        "outputId": "a9286378-24ef-435f-a730-04d3dc19a370"
+      },
+      "execution_count": 327,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "count    1000.000000\n",
+              "mean       58.315000\n",
+              "std        32.360052\n",
+              "min        11.000000\n",
+              "25%        33.000000\n",
+              "50%        51.000000\n",
+              "75%        80.000000\n",
+              "max       149.000000\n",
+              "Name: length, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 327
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets see what's the review with maximum characters i.e 149 which we got from above\n",
+        "df[df['length']==149]['Review'].iloc[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 53
+        },
+        "id": "M3Op4X8EGH07",
+        "outputId": "e00bdd14-2dc4-4625-cb86-3550135ff9df"
+      },
+      "execution_count": 328,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'The problem I have is that they charge $11.99 for a sandwich that is no bigger than a Subway sub (which offers better and more amount of vegetables).'"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
+          },
+          "metadata": {},
+          "execution_count": 328
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "9cslk8X60fyw"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(6,5))\n",
+        "sns.histplot(data=df, x='length', color='purple')\n",
+        "plt.title('Number of characters in each review')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 504
+        },
+        "id": "6E8XVEpXz_2D",
+        "outputId": "7e974265-d84d-4f20-cdaa-4e04ce0d63d7"
+      },
+      "execution_count": 329,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(0.5, 1.0, 'Number of characters in each review')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 329
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 600x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Clean Data by Removing Punctuations and Stopwords**"
+      ],
+      "metadata": {
+        "id": "eDME5Isu0Ya9"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets print list of all stopwords of english language\n",
+        "stopwords.words('english')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "x66po96BJZ3g",
+        "outputId": "862dda8c-04aa-4734-b8d3-8051e3586c5b"
+      },
+      "execution_count": 330,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['i',\n",
+              " 'me',\n",
+              " 'my',\n",
+              " 'myself',\n",
+              " 'we',\n",
+              " 'our',\n",
+              " 'ours',\n",
+              " 'ourselves',\n",
+              " 'you',\n",
+              " \"you're\",\n",
+              " \"you've\",\n",
+              " \"you'll\",\n",
+              " \"you'd\",\n",
+              " 'your',\n",
+              " 'yours',\n",
+              " 'yourself',\n",
+              " 'yourselves',\n",
+              " 'he',\n",
+              " 'him',\n",
+              " 'his',\n",
+              " 'himself',\n",
+              " 'she',\n",
+              " \"she's\",\n",
+              " 'her',\n",
+              " 'hers',\n",
+              " 'herself',\n",
+              " 'it',\n",
+              " \"it's\",\n",
+              " 'its',\n",
+              " 'itself',\n",
+              " 'they',\n",
+              " 'them',\n",
+              " 'their',\n",
+              " 'theirs',\n",
+              " 'themselves',\n",
+              " 'what',\n",
+              " 'which',\n",
+              " 'who',\n",
+              " 'whom',\n",
+              " 'this',\n",
+              " 'that',\n",
+              " \"that'll\",\n",
+              " 'these',\n",
+              " 'those',\n",
+              " 'am',\n",
+              " 'is',\n",
+              " 'are',\n",
+              " 'was',\n",
+              " 'were',\n",
+              " 'be',\n",
+              " 'been',\n",
+              " 'being',\n",
+              " 'have',\n",
+              " 'has',\n",
+              " 'had',\n",
+              " 'having',\n",
+              " 'do',\n",
+              " 'does',\n",
+              " 'did',\n",
+              " 'doing',\n",
+              " 'a',\n",
+              " 'an',\n",
+              " 'the',\n",
+              " 'and',\n",
+              " 'but',\n",
+              " 'if',\n",
+              " 'or',\n",
+              " 'because',\n",
+              " 'as',\n",
+              " 'until',\n",
+              " 'while',\n",
+              " 'of',\n",
+              " 'at',\n",
+              " 'by',\n",
+              " 'for',\n",
+              " 'with',\n",
+              " 'about',\n",
+              " 'against',\n",
+              " 'between',\n",
+              " 'into',\n",
+              " 'through',\n",
+              " 'during',\n",
+              " 'before',\n",
+              " 'after',\n",
+              " 'above',\n",
+              " 'below',\n",
+              " 'to',\n",
+              " 'from',\n",
+              " 'up',\n",
+              " 'down',\n",
+              " 'in',\n",
+              " 'out',\n",
+              " 'on',\n",
+              " 'off',\n",
+              " 'over',\n",
+              " 'under',\n",
+              " 'again',\n",
+              " 'further',\n",
+              " 'then',\n",
+              " 'once',\n",
+              " 'here',\n",
+              " 'there',\n",
+              " 'when',\n",
+              " 'where',\n",
+              " 'why',\n",
+              " 'how',\n",
+              " 'all',\n",
+              " 'any',\n",
+              " 'both',\n",
+              " 'each',\n",
+              " 'few',\n",
+              " 'more',\n",
+              " 'most',\n",
+              " 'other',\n",
+              " 'some',\n",
+              " 'such',\n",
+              " 'no',\n",
+              " 'nor',\n",
+              " 'not',\n",
+              " 'only',\n",
+              " 'own',\n",
+              " 'same',\n",
+              " 'so',\n",
+              " 'than',\n",
+              " 'too',\n",
+              " 'very',\n",
+              " 's',\n",
+              " 't',\n",
+              " 'can',\n",
+              " 'will',\n",
+              " 'just',\n",
+              " 'don',\n",
+              " \"don't\",\n",
+              " 'should',\n",
+              " \"should've\",\n",
+              " 'now',\n",
+              " 'd',\n",
+              " 'll',\n",
+              " 'm',\n",
+              " 'o',\n",
+              " 're',\n",
+              " 've',\n",
+              " 'y',\n",
+              " 'ain',\n",
+              " 'aren',\n",
+              " \"aren't\",\n",
+              " 'couldn',\n",
+              " \"couldn't\",\n",
+              " 'didn',\n",
+              " \"didn't\",\n",
+              " 'doesn',\n",
+              " \"doesn't\",\n",
+              " 'hadn',\n",
+              " \"hadn't\",\n",
+              " 'hasn',\n",
+              " \"hasn't\",\n",
+              " 'haven',\n",
+              " \"haven't\",\n",
+              " 'isn',\n",
+              " \"isn't\",\n",
+              " 'ma',\n",
+              " 'mightn',\n",
+              " \"mightn't\",\n",
+              " 'mustn',\n",
+              " \"mustn't\",\n",
+              " 'needn',\n",
+              " \"needn't\",\n",
+              " 'shan',\n",
+              " \"shan't\",\n",
+              " 'shouldn',\n",
+              " \"shouldn't\",\n",
+              " 'wasn',\n",
+              " \"wasn't\",\n",
+              " 'weren',\n",
+              " \"weren't\",\n",
+              " 'won',\n",
+              " \"won't\",\n",
+              " 'wouldn',\n",
+              " \"wouldn't\"]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 330
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets print all punctuations\n",
+        "[punc for punc in string.punctuation]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "97rhgEH3KXcb",
+        "outputId": "a86528ff-57d1-46a1-807f-41264f6c9b93"
+      },
+      "execution_count": 331,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['!',\n",
+              " '\"',\n",
+              " '#',\n",
+              " '$',\n",
+              " '%',\n",
+              " '&',\n",
+              " \"'\",\n",
+              " '(',\n",
+              " ')',\n",
+              " '*',\n",
+              " '+',\n",
+              " ',',\n",
+              " '-',\n",
+              " '.',\n",
+              " '/',\n",
+              " ':',\n",
+              " ';',\n",
+              " '<',\n",
+              " '=',\n",
+              " '>',\n",
+              " '?',\n",
+              " '@',\n",
+              " '[',\n",
+              " '\\\\',\n",
+              " ']',\n",
+              " '^',\n",
+              " '_',\n",
+              " '`',\n",
+              " '{',\n",
+              " '|',\n",
+              " '}',\n",
+              " '~']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 331
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets create a new function which removes punctuation n stopwords\n",
+        "def text_process(msg):\n",
+        "  nopunc = [char for char in msg if char not in string.punctuation] #keep only thoose characters which are not punctuation\n",
+        "  nopunc = ''.join(nopunc)    #converts characters back to word\n",
+        "  eng_stopwords = set(stopwords.words('english'))\n",
+        "  remove_words = {'not', 'nor','no',\"don't\", \"was'nt\", \"aren't\", \"didn't\"}\n",
+        "  #we need to ensure that we wont remove words such as not, because this words are important as it may give a different meaning to review.\n",
+        "  new_stopwords = eng_stopwords - remove_words\n",
+        "  return ' '.join([word for word in nopunc.split() if word.lower() not in new_stopwords])   #keep only those words which are not stopwords"
+      ],
+      "metadata": {
+        "id": "rzqcnASQK1Kt"
+      },
+      "execution_count": 332,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets create a new column which is review part without ctopwords and punctuation\n",
+        "df['new_review']=df['Review'].apply(text_process)"
+      ],
+      "metadata": {
+        "id": "S0IfPllCMU5T"
+      },
+      "execution_count": 333,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "9VyCXXU5MryX",
+        "outputId": "9413ac60-7cea-4060-92d1-1974df7e9b8c"
+      },
+      "execution_count": 334,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                                              Review  Liked  length  \\\n",
+              "0                           Wow... Loved this place.      1      24   \n",
+              "1                                 Crust is not good.      0      18   \n",
+              "2          Not tasty and the texture was just nasty.      0      41   \n",
+              "3  Stopped by during the late May bank holiday of...      1      87   \n",
+              "4  The selection on the menu was great and so wer...      1      59   \n",
+              "\n",
+              "                                          new_review  \n",
+              "0                                    Wow Loved place  \n",
+              "1                                     Crust not good  \n",
+              "2                            Not tasty texture nasty  \n",
+              "3  Stopped late May bank holiday Rick Steve recom...  \n",
+              "4                        selection menu great prices  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-738a1a4a-3293-4bad-a6f2-183696d358e0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Review</th>\n",
+              "      <th>Liked</th>\n",
+              "      <th>length</th>\n",
+              "      <th>new_review</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Wow... Loved this place.</td>\n",
+              "      <td>1</td>\n",
+              "      <td>24</td>\n",
+              "      <td>Wow Loved place</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Crust is not good.</td>\n",
+              "      <td>0</td>\n",
+              "      <td>18</td>\n",
+              "      <td>Crust not good</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Not tasty and the texture was just nasty.</td>\n",
+              "      <td>0</td>\n",
+              "      <td>41</td>\n",
+              "      <td>Not tasty texture nasty</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Stopped by during the late May bank holiday of...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>87</td>\n",
+              "      <td>Stopped late May bank holiday Rick Steve recom...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>The selection on the menu was great and so wer...</td>\n",
+              "      <td>1</td>\n",
+              "      <td>59</td>\n",
+              "      <td>selection menu great prices</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-738a1a4a-3293-4bad-a6f2-183696d358e0')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-738a1a4a-3293-4bad-a6f2-183696d358e0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-738a1a4a-3293-4bad-a6f2-183696d358e0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-a08880d7-1239-4b53-a921-4105c52d492f\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a08880d7-1239-4b53-a921-4105c52d492f')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-a08880d7-1239-4b53-a921-4105c52d492f button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"Review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"They were excellent.\",\n          \"Your servers suck, wait, correction, our server Heimer sucked.\",\n          \"Will be back again!\"\n        ],\n        \"num_unique_values\": 996,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Liked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32,\n        \"min\": 11,\n        \"max\": 149,\n        \"samples\": [\n          122,\n          146\n        ],\n        \"num_unique_values\": 134,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"new_review\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Wont go back\",\n          \"truly unbelievably good glad went back\"\n        ],\n        \"num_unique_values\": 992,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 334
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Generate Word Cloud**"
+      ],
+      "metadata": {
+        "id": "TZWYDply0xiB"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#generate word cloud\n",
+        "#positive comment wordcloud\n",
+        "word_cloud=df.loc[df['Liked']==1,:]\n",
+        "#create wordcloud for only positive comments\n",
+        "text = ' '.join([text for text in word_cloud['new_review']])\n",
+        "wrdcld = WordCloud(width = 800, height = 400, background_color = 'white').generate(text)\n",
+        "plt.figure(figsize = (10,5))\n",
+        "plt.imshow(wrdcld, interpolation = 'bilinear')\n",
+        "plt.axis('off')\n",
+        "plt.show()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "9AVQn2r5NZ_7",
+        "outputId": "2d05f01f-80f0-4783-e9e4-d37f140ed208"
+      },
+      "execution_count": 335,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#negative reviews\n",
+        "word_cloud=df.loc[df['Liked']==0,:] #create wordcloud for only positive comments\n",
+        "text = ' '.join([text for text in word_cloud['new_review']])\n",
+        "wrdcld = WordCloud(width = 800, height = 400, background_color = 'white').generate(text)\n",
+        "plt.figure(figsize = (10,5))\n",
+        "plt.imshow(wrdcld, interpolation = 'bilinear')\n",
+        "plt.axis('off')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 422
+        },
+        "id": "fOKL8yTJPfMs",
+        "outputId": "972c1829-9a0a-4a8c-af9f-0ff22f519550"
+      },
+      "execution_count": 336,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **VECTORIZATION**"
+      ],
+      "metadata": {
+        "id": "3QmWrzEz05uJ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#text to numerical by CountVectorizer Method\n",
+        "vectorizer = CountVectorizer(max_df=.9, min_df=4)\n",
+        "X = vectorizer.fit_transform(df['new_review']).toarray()\n",
+        "X"
+      ],
+      "metadata": {
+        "id": "8cMHVwqhEEN9",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a6a25229-2485-4582-d011-0cb535fc7670"
+      },
+      "execution_count": 337,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0, 0, 0, ..., 0, 0, 0],\n",
+              "       [0, 0, 0, ..., 0, 0, 0],\n",
+              "       [0, 0, 0, ..., 0, 0, 0],\n",
+              "       ...,\n",
+              "       [0, 0, 0, ..., 0, 0, 0],\n",
+              "       [0, 0, 0, ..., 0, 0, 0],\n",
+              "       [0, 0, 0, ..., 0, 0, 0]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 337
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train, X_test, y_train, y_test = train_test_split(df['new_review'],df['Liked'],random_state=131, test_size= 0.2)"
+      ],
+      "metadata": {
+        "id": "bozK58L0qeiB"
+      },
+      "execution_count": 338,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "alAy1NXUxMSZ",
+        "outputId": "8c24ac51-a691-4517-e23e-e0e7b70a1291"
+      },
+      "execution_count": 339,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "173    Lordy Khao Soi dish not missed curry lovers\n",
+              "204                      Service friendly inviting\n",
+              "937                             Dont bother coming\n",
+              "891        rotating beers tap also highlight place\n",
+              "218                          small not worth price\n",
+              "Name: new_review, dtype: object"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 339
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_vectorized= vectorizer.transform(X_train)\n",
+        "test_vectorized= vectorizer.transform(X_test)"
+      ],
+      "metadata": {
+        "id": "BMnfOQZExS7y"
+      },
+      "execution_count": 340,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_train_array = train_vectorized.toarray()\n",
+        "X_test_array = test_vectorized.toarray()\n",
+        "X_test_array"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "V016Pa81zpS8",
+        "outputId": "5ca025ca-25e7-4c70-f434-36be8d9ed78f"
+      },
+      "execution_count": 341,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0, 0, 0, ..., 0, 0, 0],\n",
+              "       [0, 0, 0, ..., 0, 0, 0],\n",
+              "       [0, 0, 0, ..., 0, 0, 0],\n",
+              "       ...,\n",
+              "       [0, 0, 0, ..., 0, 0, 0],\n",
+              "       [0, 0, 0, ..., 0, 0, 0],\n",
+              "       [0, 0, 0, ..., 0, 0, 0]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 341
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Logistic Regression Algorithm**"
+      ],
+      "metadata": {
+        "id": "nT31L3Eg1Sz1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#using Logistic REgression algorithm\n",
+        "#create model\n",
+        "lr_model = LogisticRegression()\n",
+        "#train model\n",
+        "lr_model.fit(X_train_array, y_train)"
+      ],
+      "metadata": {
+        "id": "epxTv1aymb0A",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "outputId": "226899a1-c384-492e-e2a2-d05d42fd4e27"
+      },
+      "execution_count": 342,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "LogisticRegression()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-18 {color: black;background-color: white;}#sk-container-id-18 pre{padding: 0;}#sk-container-id-18 div.sk-toggleable {background-color: white;}#sk-container-id-18 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-18 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-18 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-18 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-18 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-18 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-18 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-18 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-18 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-18 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-18 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-18 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-18 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-18 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-18 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-18 div.sk-item {position: relative;z-index: 1;}#sk-container-id-18 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-18 div.sk-item::before, #sk-container-id-18 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-18 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-18 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-18 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-18 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-18 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-18 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-18 div.sk-label-container {text-align: center;}#sk-container-id-18 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-18 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-18\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" checked><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 342
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets make predictions\n",
+        "#we use trained logistic regression model to make predictions on test data\n",
+        "#and also if you want train data\n",
+        "y_train_preds_lr = lr_model.predict(X_train_array)\n",
+        "y_test_preds_lr = lr_model.predict(X_test_array)"
+      ],
+      "metadata": {
+        "id": "s0AAY8Y_p5hW"
+      },
+      "execution_count": 343,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_test"
+      ],
+      "metadata": {
+        "id": "-W0ySHI9qQKR",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7ed66207-84b0-475f-83a9-bd7032b96509"
+      },
+      "execution_count": 344,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "634    0\n",
+              "754    0\n",
+              "219    1\n",
+              "705    0\n",
+              "940    0\n",
+              "      ..\n",
+              "648    1\n",
+              "557    0\n",
+              "606    1\n",
+              "197    0\n",
+              "517    1\n",
+              "Name: Liked, Length: 200, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 344
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_test_preds_lr\n"
+      ],
+      "metadata": {
+        "id": "Wck-ipFMqTRO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "749c3a52-b757-4d44-c2fc-aa337a74e89f"
+      },
+      "execution_count": 345,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0,\n",
+              "       1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0,\n",
+              "       1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
+              "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0,\n",
+              "       1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1,\n",
+              "       1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
+              "       0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
+              "       0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
+              "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1,\n",
+              "       0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 345
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.DataFrame({'actual_y_value':y_test, \"predicted_y_value\":y_test_preds_lr})"
+      ],
+      "metadata": {
+        "id": "k6kyXo9Hqdhf",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "outputId": "1cf90c8b-516a-4145-f2df-1cf50fe76348"
+      },
+      "execution_count": 346,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     actual_y_value  predicted_y_value\n",
+              "634               0                  0\n",
+              "754               0                  1\n",
+              "219               1                  1\n",
+              "705               0                  0\n",
+              "940               0                  0\n",
+              "..              ...                ...\n",
+              "648               1                  1\n",
+              "557               0                  0\n",
+              "606               1                  1\n",
+              "197               0                  0\n",
+              "517               1                  1\n",
+              "\n",
+              "[200 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-54e5fa4f-37c8-45a9-9c54-c6c051df0c9f\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>actual_y_value</th>\n",
+              "      <th>predicted_y_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>634</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>754</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>219</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>705</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>940</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>648</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>557</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>606</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>197</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>517</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>200 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-54e5fa4f-37c8-45a9-9c54-c6c051df0c9f')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-54e5fa4f-37c8-45a9-9c54-c6c051df0c9f button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-54e5fa4f-37c8-45a9-9c54-c6c051df0c9f');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-fb674f07-b4a9-4552-937f-84c3676aa6c1\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb674f07-b4a9-4552-937f-84c3676aa6c1')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-fb674f07-b4a9-4552-937f-84c3676aa6c1 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"actual_y_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_y_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 346
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets create a function that check accuracy and all\n",
+        "def print_metrics(actual,predicted):\n",
+        "  accuracy_scr = accuracy_score(actual, predicted)\n",
+        "  print(\"accuracy score is {}%\".format(round(accuracy_scr*100,2)))\n",
+        "  print(\" precision score is {}%\".format( round((precision_score(actual, predicted))*100,2)))\n",
+        "  print(\"recall score is {}%\".format(round((recall_score(actual, predicted))*100,2)))\n",
+        "  print(\"f1 score is {}%\".format(round((f1_score(actual, predicted))*100,2)))\n",
+        "  print(\"roc auc score is {}%\".format( round((roc_auc_score(actual, predicted))*100,2)))\n",
+        "  print(\"confusion matrix is {}\".format(confusion_matrix(actual, predicted)))\n",
+        "  print(\"classification report is {}\".format(classification_report(actual, predicted)))\n",
+        "  accuracy = round(accuracy_scr*100,2)\n",
+        "  return round(accuracy_scr*100,2),"
+      ],
+      "metadata": {
+        "id": "fYwu-evEqvdK"
+      },
+      "execution_count": 347,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print_metrics(y_train, y_train_preds_lr)\n",
+        "#evaluate training model"
+      ],
+      "metadata": {
+        "id": "n48H-HRZqvlH",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "d9daf3ef-68bf-4921-e1d3-6e41e41ee9e4"
+      },
+      "execution_count": 348,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 90.38%\n",
+            " precision score is 93.51%\n",
+            "recall score is 86.72%\n",
+            "f1 score is 89.99%\n",
+            "roc auc score is 90.37%\n",
+            "confusion matrix is [[377  24]\n",
+            " [ 53 346]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.88      0.94      0.91       401\n",
+            "           1       0.94      0.87      0.90       399\n",
+            "\n",
+            "    accuracy                           0.90       800\n",
+            "   macro avg       0.91      0.90      0.90       800\n",
+            "weighted avg       0.91      0.90      0.90       800\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(90.38,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 348
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print_metrics(y_test, y_test_preds_lr)\n",
+        "#evaluate tseting model"
+      ],
+      "metadata": {
+        "id": "vUYdvD3JtBlj",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "15169aab-1673-471e-e807-5f2110c6a30a"
+      },
+      "execution_count": 349,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 87.0%\n",
+            " precision score is 88.66%\n",
+            "recall score is 85.15%\n",
+            "f1 score is 86.87%\n",
+            "roc auc score is 87.02%\n",
+            "confusion matrix is [[88 11]\n",
+            " [15 86]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.85      0.89      0.87        99\n",
+            "           1       0.89      0.85      0.87       101\n",
+            "\n",
+            "    accuracy                           0.87       200\n",
+            "   macro avg       0.87      0.87      0.87       200\n",
+            "weighted avg       0.87      0.87      0.87       200\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(87.0,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 349
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#store our results in a table\n",
+        "results = pd.DataFrame(columns=['Model', 'accuracy_train(%)', 'precision_train(%)', 'accuracy_test(%)', 'precision_test(%)'])\n",
+        "test_accuracy=round(accuracy_score(y_test, y_test_preds_lr)*100,2)\n",
+        "test_precision=round(precision_score(y_test, y_test_preds_lr)*100,2)\n",
+        "train_accuracy=round(accuracy_score(y_train, y_train_preds_lr)*100,2)\n",
+        "train_precision=round(precision_score(y_train, y_train_preds_lr)*100,2)\n",
+        "results.loc[len(results)] = ['Logistic Regression', train_precision, train_accuracy, test_precision, test_accuracy]\n"
+      ],
+      "metadata": {
+        "id": "pp7g_QrN4pet"
+      },
+      "execution_count": 350,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "id": "dzgiHblrGUqa",
+        "outputId": "cfe26980-758f-4dfe-e6bc-3267453f2d22"
+      },
+      "execution_count": 351,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                 Model  accuracy_train(%)  precision_train(%)  \\\n",
+              "0  Logistic Regression              93.51               90.38   \n",
+              "\n",
+              "   accuracy_test(%)  precision_test(%)  \n",
+              "0             88.66               87.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-be219646-03c9-4f6d-ad28-56ed74808e2b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Model</th>\n",
+              "      <th>accuracy_train(%)</th>\n",
+              "      <th>precision_train(%)</th>\n",
+              "      <th>accuracy_test(%)</th>\n",
+              "      <th>precision_test(%)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Logistic Regression</td>\n",
+              "      <td>93.51</td>\n",
+              "      <td>90.38</td>\n",
+              "      <td>88.66</td>\n",
+              "      <td>87.0</td>\n",
+              "    </tr>\n",
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+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-be219646-03c9-4f6d-ad28-56ed74808e2b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-be219646-03c9-4f6d-ad28-56ed74808e2b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-be219646-03c9-4f6d-ad28-56ed74808e2b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "  <div id=\"id_4215eaff-298a-4c16-ad58-00f5f7bc6778\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_4215eaff-298a-4c16-ad58-00f5f7bc6778 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('results');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "results",
+              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Logistic Regression\"\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy_train(%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 93.51,\n        \"max\": 93.51,\n        \"samples\": [\n          93.51\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision_train(%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 90.38,\n        \"max\": 90.38,\n        \"samples\": [\n          90.38\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy_test(%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 88.66,\n        \"max\": 88.66,\n        \"samples\": [\n          88.66\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision_test(%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 87.0,\n        \"max\": 87.0,\n        \"samples\": [\n          87.0\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 351
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Hyperparameter Tuning of Logistic Regression**"
+      ],
+      "metadata": {
+        "id": "Tiy0ot8CrQ2m"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#hyperparameter tuning of logistic regression\n",
+        "best_accuracy = 0.0\n",
+        "alpha_val = 0\n",
+        "for i in np.arange(0.01,1.1,0.1):\n",
+        "  #.1 is step size and 0.01 is starting value of sequence and 1.1 is ending value\n",
+        "  temp_cls = LogisticRegression(C=i)\n",
+        "  #for logistic regression we have C=1/alpha\n",
+        "  #where C is regularization parameter\n",
+        "  #here we were rounding alpha_val variable to i, which is float\n",
+        "  temp_cls.fit(X_train_array, y_train)\n",
+        "  y_test_preds_lr = temp_cls.predict(X_test_array)\n",
+        "  score = accuracy_score(y_test, y_test_preds_lr)\n",
+        "  print(\"accuracy score for C {} is {}%\".format(round(i,1),round(score*100,2)))\n",
+        "  if score>best_accuracy:\n",
+        "    best_accuracy = score\n",
+        "    alpha_val = i\n",
+        "print(\"best accuracy {}% with C value {}\".format(round(best_accuracy*100,2),round(alpha_val,1)))\n"
+      ],
+      "metadata": {
+        "id": "IfoQDAZR98mz",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "c37d8b5c-6a9a-451e-ced2-bf128eae50e3"
+      },
+      "execution_count": 352,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score for C 0.0 is 81.0%\n",
+            "accuracy score for C 0.1 is 83.0%\n",
+            "accuracy score for C 0.2 is 85.0%\n",
+            "accuracy score for C 0.3 is 85.0%\n",
+            "accuracy score for C 0.4 is 85.5%\n",
+            "accuracy score for C 0.5 is 85.0%\n",
+            "accuracy score for C 0.6 is 86.0%\n",
+            "accuracy score for C 0.7 is 86.5%\n",
+            "accuracy score for C 0.8 is 86.5%\n",
+            "accuracy score for C 0.9 is 87.0%\n",
+            "accuracy score for C 1.0 is 87.0%\n",
+            "best accuracy 87.0% with C value 0.9\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "We can say that even after hyperparameter tuning we got same highest testing accuracy i.e. 87%. So we can use it for our further process like model inference."
+      ],
+      "metadata": {
+        "id": "XzMOuIJPvV1T"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Guassian Naive Bayes Model**"
+      ],
+      "metadata": {
+        "id": "4dTilG-pGm5U"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets make predictions\n",
+        "nb = GaussianNB() #Creates model\n",
+        "nb.fit(X_train_array, y_train)\n",
+        "#we use trained guassian naive bayes model to make predictions on test data and also if you want train data\n",
+        "y_train_preds_nb = nb.predict(X_train_array)\n",
+        "y_test_preds_nb = nb.predict(X_test_array)"
+      ],
+      "metadata": {
+        "id": "nOJf87s-1IB8"
+      },
+      "execution_count": 353,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_test"
+      ],
+      "metadata": {
+        "id": "n6B7Xiua3OGg",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "dbb721d3-ece3-4b23-8c13-c02ff5287a9d"
+      },
+      "execution_count": 354,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "634    0\n",
+              "754    0\n",
+              "219    1\n",
+              "705    0\n",
+              "940    0\n",
+              "      ..\n",
+              "648    1\n",
+              "557    0\n",
+              "606    1\n",
+              "197    0\n",
+              "517    1\n",
+              "Name: Liked, Length: 200, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 354
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_test_preds_nb"
+      ],
+      "metadata": {
+        "id": "gBDMRhTg3QBT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "ef2410b7-ead5-4043-c063-8839076e28f1"
+      },
+      "execution_count": 355,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
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+              "       0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 355
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.DataFrame({'actual_y_value':y_test, \"predicted_y_value\":y_test_preds_nb})"
+      ],
+      "metadata": {
+        "id": "pk0LYbjd5QzR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "outputId": "109e376c-a30d-47b0-cbce-edbd2ef5cd24"
+      },
+      "execution_count": 356,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     actual_y_value  predicted_y_value\n",
+              "634               0                  1\n",
+              "754               0                  0\n",
+              "219               1                  1\n",
+              "705               0                  1\n",
+              "940               0                  1\n",
+              "..              ...                ...\n",
+              "648               1                  1\n",
+              "557               0                  0\n",
+              "606               1                  1\n",
+              "197               0                  0\n",
+              "517               1                  1\n",
+              "\n",
+              "[200 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6aedcb02-4b2c-482a-8407-576416e86878\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
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+              "  <thead>\n",
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+              "      <th></th>\n",
+              "      <th>actual_y_value</th>\n",
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+              "      <th>219</th>\n",
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+              "      <th>557</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
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+              "      <th>606</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>197</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>517</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>200 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6aedcb02-4b2c-482a-8407-576416e86878')\"\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6aedcb02-4b2c-482a-8407-576416e86878 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6aedcb02-4b2c-482a-8407-576416e86878');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
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+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
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+              "\n",
+              "\n",
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+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-51f7e92c-edbc-4017-af56-f26adfa7d444')\"\n",
+              "            title=\"Suggest charts\"\n",
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+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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+              "    <g>\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-51f7e92c-edbc-4017-af56-f26adfa7d444 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"actual_y_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_y_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 356
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#evaluation of training model gor naive bayes algorithm\n",
+        "print_metrics(y_train, y_train_preds_nb)"
+      ],
+      "metadata": {
+        "id": "YNNrjw0x_4dq",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "b7238b05-7034-4e84-e1db-03dad956d73b"
+      },
+      "execution_count": 357,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 77.88%\n",
+            " precision score is 69.61%\n",
+            "recall score is 98.75%\n",
+            "f1 score is 81.66%\n",
+            "roc auc score is 77.93%\n",
+            "confusion matrix is [[229 172]\n",
+            " [  5 394]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.98      0.57      0.72       401\n",
+            "           1       0.70      0.99      0.82       399\n",
+            "\n",
+            "    accuracy                           0.78       800\n",
+            "   macro avg       0.84      0.78      0.77       800\n",
+            "weighted avg       0.84      0.78      0.77       800\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(77.88,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 357
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print_metrics(y_test, y_test_preds_nb)\n",
+        "#evaluate testing model"
+      ],
+      "metadata": {
+        "id": "jwWw5_BAASmM",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "389bcd4f-8f6a-481e-b709-3b79e7932758"
+      },
+      "execution_count": 358,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 75.0%\n",
+            " precision score is 68.61%\n",
+            "recall score is 93.07%\n",
+            "f1 score is 78.99%\n",
+            "roc auc score is 74.82%\n",
+            "confusion matrix is [[56 43]\n",
+            " [ 7 94]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.89      0.57      0.69        99\n",
+            "           1       0.69      0.93      0.79       101\n",
+            "\n",
+            "    accuracy                           0.75       200\n",
+            "   macro avg       0.79      0.75      0.74       200\n",
+            "weighted avg       0.79      0.75      0.74       200\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(75.0,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 358
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#store our results in a table\n",
+        "test_accuracy=round(accuracy_score(y_test, y_test_preds_nb)*100,2)\n",
+        "test_precision=round(precision_score(y_test, y_test_preds_nb)*100,2)\n",
+        "train_accuracy=round(accuracy_score(y_train, y_train_preds_nb)*100,2)\n",
+        "train_precision=round(precision_score(y_train, y_train_preds_nb)*100,2)\n",
+        "results.loc[len(results)] = ['Guassian Naive Bayes', train_precision, train_accuracy, test_precision, test_accuracy]\n"
+      ],
+      "metadata": {
+        "id": "VreqF2gmHssk"
+      },
+      "execution_count": 359,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Hyper Parameter tuning of Guassian Naive Byes Model**"
+      ],
+      "metadata": {
+        "id": "L9SksjTXHL54"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "best_accuracy = 0.0\n",
+        "best_var_smoothing = 0\n",
+        "\n",
+        "# Define a range of values for var_smoothing\n",
+        "var_smoothing_values = np.arange(0.01,1.1,0.1)\n",
+        "\n",
+        "for var_smoothing in var_smoothing_values:\n",
+        "    temp_cls = GaussianNB(var_smoothing=var_smoothing)\n",
+        "    temp_cls.fit(X_train_array, y_train)\n",
+        "    y_test_preds_nb = temp_cls.predict(X_test_array)\n",
+        "    score = accuracy_score(y_test, y_test_preds_nb)\n",
+        "    print(\"Accuracy score for var_smoothing {} is {}%\".format(round(var_smoothing,1), round(score * 100, 2)))\n",
+        "    if score > best_accuracy:\n",
+        "        best_accuracy = score\n",
+        "        best_var_smoothing = var_smoothing\n",
+        "\n",
+        "print(\"Best accuracy {}% with var_smoothing value {}\".format(round(best_accuracy * 100, 2), round(best_var_smoothing,1)))"
+      ],
+      "metadata": {
+        "id": "txgm-bz-FM73",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "06add10c-2bf3-4f11-960f-3e5be019a5cd"
+      },
+      "execution_count": 360,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy score for var_smoothing 0.0 is 77.0%\n",
+            "Accuracy score for var_smoothing 0.1 is 79.0%\n",
+            "Accuracy score for var_smoothing 0.2 is 75.5%\n",
+            "Accuracy score for var_smoothing 0.3 is 71.5%\n",
+            "Accuracy score for var_smoothing 0.4 is 67.5%\n",
+            "Accuracy score for var_smoothing 0.5 is 65.5%\n",
+            "Accuracy score for var_smoothing 0.6 is 64.5%\n",
+            "Accuracy score for var_smoothing 0.7 is 63.5%\n",
+            "Accuracy score for var_smoothing 0.8 is 64.0%\n",
+            "Accuracy score for var_smoothing 0.9 is 63.5%\n",
+            "Accuracy score for var_smoothing 1.0 is 62.5%\n",
+            "Best accuracy 79.0% with var_smoothing value 0.1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Now by hyperparameter tuning we have increased testing accuracy which is 79% for alpha 0.1."
+      ],
+      "metadata": {
+        "id": "_MBp5owrHAFn"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **TFID VECTORIZATION**\n",
+        "\n",
+        "Now lets try with another method using TFID\n"
+      ],
+      "metadata": {
+        "id": "Ho1LlwNZonQz"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "vectorizer_2 = TfidfVectorizer(max_features=45) #trying with tfidvectorizer)"
+      ],
+      "metadata": {
+        "id": "npn9w7YRpdql"
+      },
+      "execution_count": 361,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X_2 = vectorizer_2.fit_transform(df['new_review']).toarray()\n",
+        "X_2"
+      ],
+      "metadata": {
+        "id": "58to8YSdpo-E",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "969bf09f-4445-4957-95d6-d78706353a33"
+      },
+      "execution_count": 362,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
+              "        0.        ],\n",
+              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
+              "        0.        ],\n",
+              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
+              "        0.        ],\n",
+              "       ...,\n",
+              "       [0.        , 0.        , 0.39478847, ..., 0.        , 0.        ,\n",
+              "        0.47208317],\n",
+              "       [0.        , 0.        , 0.        , ..., 0.55499034, 0.        ,\n",
+              "        0.        ],\n",
+              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
+              "        0.        ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 362
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X2_train, X2_test, y2_train, y2_test = train_test_split(df['new_review'],df['Liked'],random_state=131, test_size= 0.2)"
+      ],
+      "metadata": {
+        "id": "lFmLjtuAp6jS"
+      },
+      "execution_count": 363,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "train_vectorized= vectorizer_2.transform(X2_train)\n",
+        "test_vectorized= vectorizer_2.transform(X2_test)"
+      ],
+      "metadata": {
+        "id": "eHKZXTHZqQRu"
+      },
+      "execution_count": 364,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "X2_train_array = train_vectorized.toarray()\n",
+        "X2_test_array = test_vectorized.toarray()\n",
+        "X2_test_array"
+      ],
+      "metadata": {
+        "id": "jm78Nb-4q0K1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0f6efa5e-db30-4a79-a115-e0d0d2b16870"
+      },
+      "execution_count": 365,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[0.      , 0.      , 0.      , ..., 0.721976, 0.      , 0.      ],\n",
+              "       [0.      , 0.      , 0.      , ..., 0.      , 0.      , 0.      ],\n",
+              "       [0.      , 0.      , 0.      , ..., 0.      , 0.      , 0.      ],\n",
+              "       ...,\n",
+              "       [0.      , 0.      , 0.      , ..., 0.      , 0.      , 0.      ],\n",
+              "       [0.      , 0.      , 0.      , ..., 0.      , 0.      , 0.      ],\n",
+              "       [0.      , 0.      , 0.      , ..., 0.      , 0.      , 0.      ]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 365
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#create model\n",
+        "rf_model = RandomForestClassifier(n_estimators=40, random_state = 1)\n",
+        "#train model\n",
+        "rf_model.fit(X2_train_array, y2_train)"
+      ],
+      "metadata": {
+        "id": "DrVwsCHQqTFu",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "outputId": "7e828d4a-34a1-447d-d839-b644b8a43b9e"
+      },
+      "execution_count": 366,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "RandomForestClassifier(n_estimators=40, random_state=1)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-19 {color: black;background-color: white;}#sk-container-id-19 pre{padding: 0;}#sk-container-id-19 div.sk-toggleable {background-color: white;}#sk-container-id-19 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-19 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-19 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-19 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-19 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-19 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-19 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-19 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-19 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-19 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-19 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-19 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-19 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-19 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-19 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-19 div.sk-item {position: relative;z-index: 1;}#sk-container-id-19 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-19 div.sk-item::before, #sk-container-id-19 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-19 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-19 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-19 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-19 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-19 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-19 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-19 div.sk-label-container {text-align: center;}#sk-container-id-19 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-19 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-19\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=40, random_state=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" checked><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=40, random_state=1)</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 366
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets make predictions\n",
+        "#we use trained random forest model to make predictions on test data and also if you want train data\n",
+        "y_train_preds_rf = rf_model.predict(X2_train_array)\n",
+        "y_test_preds_rf = rf_model.predict(X2_test_array)"
+      ],
+      "metadata": {
+        "id": "Vsun0acxqi5J"
+      },
+      "execution_count": 367,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y2_test"
+      ],
+      "metadata": {
+        "id": "Q9AbM-cXqjUf",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "3fc02394-932f-4446-d091-37a316ee4c0b"
+      },
+      "execution_count": 368,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "634    0\n",
+              "754    0\n",
+              "219    1\n",
+              "705    0\n",
+              "940    0\n",
+              "      ..\n",
+              "648    1\n",
+              "557    0\n",
+              "606    1\n",
+              "197    0\n",
+              "517    1\n",
+              "Name: Liked, Length: 200, dtype: int64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 368
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y_test_preds_rf"
+      ],
+      "metadata": {
+        "id": "hXww9JL3sa5Z",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "077f0e3b-4760-40c5-d5e0-356ac40b3351"
+      },
+      "execution_count": 369,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1,\n",
+              "       0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0,\n",
+              "       1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0,\n",
+              "       0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
+              "       0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0,\n",
+              "       0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
+              "       0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
+              "       0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0,\n",
+              "       1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0,\n",
+              "       0, 1])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 369
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pd.DataFrame({'actual_y_value':y2_test, \"predicted_y_value\":y_test_preds_rf})"
+      ],
+      "metadata": {
+        "id": "Jx4UHruUse2H",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "outputId": "b500614c-f8eb-472c-de29-8c2dc32805c6"
+      },
+      "execution_count": 370,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     actual_y_value  predicted_y_value\n",
+              "634               0                  1\n",
+              "754               0                  0\n",
+              "219               1                  0\n",
+              "705               0                  0\n",
+              "940               0                  0\n",
+              "..              ...                ...\n",
+              "648               1                  1\n",
+              "557               0                  0\n",
+              "606               1                  0\n",
+              "197               0                  0\n",
+              "517               1                  1\n",
+              "\n",
+              "[200 rows x 2 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-6bdb448b-02bb-431c-96a8-1960dd99e42b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>actual_y_value</th>\n",
+              "      <th>predicted_y_value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>634</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>754</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>219</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>705</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>940</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>648</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>557</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>606</th>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>197</th>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>517</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>200 rows × 2 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bdb448b-02bb-431c-96a8-1960dd99e42b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-6bdb448b-02bb-431c-96a8-1960dd99e42b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-6bdb448b-02bb-431c-96a8-1960dd99e42b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-464bc5fd-2202-410d-8b83-85749cae9d43\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-464bc5fd-2202-410d-8b83-85749cae9d43')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-464bc5fd-2202-410d-8b83-85749cae9d43 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"actual_y_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_y_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 370
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print_metrics(y2_train, y_train_preds_rf)\n",
+        "#training accuracy for random forest"
+      ],
+      "metadata": {
+        "id": "Ng9xuNVusi4t",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "143cb441-290e-4c68-aa66-627a59cbb7d2"
+      },
+      "execution_count": 371,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 80.88%\n",
+            " precision score is 91.84%\n",
+            "recall score is 67.67%\n",
+            "f1 score is 77.92%\n",
+            "roc auc score is 80.84%\n",
+            "confusion matrix is [[377  24]\n",
+            " [129 270]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.75      0.94      0.83       401\n",
+            "           1       0.92      0.68      0.78       399\n",
+            "\n",
+            "    accuracy                           0.81       800\n",
+            "   macro avg       0.83      0.81      0.81       800\n",
+            "weighted avg       0.83      0.81      0.81       800\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(80.88,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 371
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print_metrics(y2_test, y_test_preds_rf)\n",
+        "#testing accuracy for random forest"
+      ],
+      "metadata": {
+        "id": "IM1VG8Rxsl-P",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "bf33169a-bc81-4adb-ed56-aa82f2055e7b"
+      },
+      "execution_count": 372,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 78.5%\n",
+            " precision score is 87.18%\n",
+            "recall score is 67.33%\n",
+            "f1 score is 75.98%\n",
+            "roc auc score is 78.61%\n",
+            "confusion matrix is [[89 10]\n",
+            " [33 68]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.73      0.90      0.81        99\n",
+            "           1       0.87      0.67      0.76       101\n",
+            "\n",
+            "    accuracy                           0.79       200\n",
+            "   macro avg       0.80      0.79      0.78       200\n",
+            "weighted avg       0.80      0.79      0.78       200\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(78.5,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 372
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#store our results in a table\n",
+        "test_accuracy=round(accuracy_score(y2_test, y_test_preds_rf)*100,2)\n",
+        "test_precision=round(precision_score(y2_test, y_test_preds_rf)*100,2)\n",
+        "train_accuracy=round(accuracy_score(y2_train, y_train_preds_rf)*100,2)\n",
+        "train_precision=round(precision_score(y2_train, y_train_preds_rf)*100,2)\n",
+        "results.loc[len(results)] = ['Random Forest', train_precision, train_accuracy, test_precision, test_accuracy]"
+      ],
+      "metadata": {
+        "id": "i-0HRVaFI1jx"
+      },
+      "execution_count": 373,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#hyperparameter tuning for random forest\n",
+        "best_accuracy = 0.0\n",
+        "best_n_estimators = 0\n",
+        "best_random_state = 0\n",
+        "\n",
+        "for n_estimators in range(10, 101, 10):  # Adjust the range as needed 10, 20 ,30 and so on\n",
+        "    for random_state in [42, 1, 5, 10]:  # Try different random_state values i.e 1, 5 , 10, 42\n",
+        "        temp_cls = RandomForestClassifier(n_estimators=n_estimators, random_state=random_state)\n",
+        "        temp_cls.fit(X2_train_array, y2_train)\n",
+        "        y_test_preds_rf = temp_cls.predict(X2_test_array)\n",
+        "        score = accuracy_score(y2_test, y_test_preds_rf)\n",
+        "        print(\"Accuracy score for n_estimators {} and random_state {} is {}%\".format(n_estimators, random_state, round(score * 100, 2)))\n",
+        "        if score > best_accuracy:\n",
+        "            best_accuracy = score\n",
+        "            best_n_estimators = n_estimators\n",
+        "            best_random_state = random_state\n",
+        "print(\"Best accuracy {}% with n_estimators {} and random_state {}\".format(round(best_accuracy * 100, 2), best_n_estimators, best_random_state))"
+      ],
+      "metadata": {
+        "id": "_RfSRnqHHkFw",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "6f9306bd-4b52-4f13-850a-8bdc2749f478"
+      },
+      "execution_count": 374,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Accuracy score for n_estimators 10 and random_state 42 is 75.5%\n",
+            "Accuracy score for n_estimators 10 and random_state 1 is 77.5%\n",
+            "Accuracy score for n_estimators 10 and random_state 5 is 75.5%\n",
+            "Accuracy score for n_estimators 10 and random_state 10 is 75.5%\n",
+            "Accuracy score for n_estimators 20 and random_state 42 is 77.0%\n",
+            "Accuracy score for n_estimators 20 and random_state 1 is 76.5%\n",
+            "Accuracy score for n_estimators 20 and random_state 5 is 78.0%\n",
+            "Accuracy score for n_estimators 20 and random_state 10 is 76.0%\n",
+            "Accuracy score for n_estimators 30 and random_state 42 is 77.0%\n",
+            "Accuracy score for n_estimators 30 and random_state 1 is 77.0%\n",
+            "Accuracy score for n_estimators 30 and random_state 5 is 76.5%\n",
+            "Accuracy score for n_estimators 30 and random_state 10 is 78.0%\n",
+            "Accuracy score for n_estimators 40 and random_state 42 is 77.0%\n",
+            "Accuracy score for n_estimators 40 and random_state 1 is 78.5%\n",
+            "Accuracy score for n_estimators 40 and random_state 5 is 77.0%\n",
+            "Accuracy score for n_estimators 40 and random_state 10 is 77.0%\n",
+            "Accuracy score for n_estimators 50 and random_state 42 is 76.5%\n",
+            "Accuracy score for n_estimators 50 and random_state 1 is 77.5%\n",
+            "Accuracy score for n_estimators 50 and random_state 5 is 78.5%\n",
+            "Accuracy score for n_estimators 50 and random_state 10 is 77.0%\n",
+            "Accuracy score for n_estimators 60 and random_state 42 is 77.5%\n",
+            "Accuracy score for n_estimators 60 and random_state 1 is 77.5%\n",
+            "Accuracy score for n_estimators 60 and random_state 5 is 78.0%\n",
+            "Accuracy score for n_estimators 60 and random_state 10 is 76.5%\n",
+            "Accuracy score for n_estimators 70 and random_state 42 is 77.0%\n",
+            "Accuracy score for n_estimators 70 and random_state 1 is 77.0%\n",
+            "Accuracy score for n_estimators 70 and random_state 5 is 77.0%\n",
+            "Accuracy score for n_estimators 70 and random_state 10 is 77.5%\n",
+            "Accuracy score for n_estimators 80 and random_state 42 is 78.0%\n",
+            "Accuracy score for n_estimators 80 and random_state 1 is 77.5%\n",
+            "Accuracy score for n_estimators 80 and random_state 5 is 76.5%\n",
+            "Accuracy score for n_estimators 80 and random_state 10 is 77.0%\n",
+            "Accuracy score for n_estimators 90 and random_state 42 is 77.5%\n",
+            "Accuracy score for n_estimators 90 and random_state 1 is 77.5%\n",
+            "Accuracy score for n_estimators 90 and random_state 5 is 77.0%\n",
+            "Accuracy score for n_estimators 90 and random_state 10 is 77.0%\n",
+            "Accuracy score for n_estimators 100 and random_state 42 is 78.0%\n",
+            "Accuracy score for n_estimators 100 and random_state 1 is 77.5%\n",
+            "Accuracy score for n_estimators 100 and random_state 5 is 76.5%\n",
+            "Accuracy score for n_estimators 100 and random_state 10 is 77.0%\n",
+            "Best accuracy 78.5% with n_estimators 40 and random_state 1\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Decision Tree ALgorithm**"
+      ],
+      "metadata": {
+        "id": "lWusrrBFLI4s"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "dt = DecisionTreeClassifier()\n",
+        "dt.fit(X_train_array,y_train)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "id": "2HjNyv3ALjF-",
+        "outputId": "4f565438-efed-4d82-b30e-3ae329f94597"
+      },
+      "execution_count": 375,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "DecisionTreeClassifier()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-20 {color: black;background-color: white;}#sk-container-id-20 pre{padding: 0;}#sk-container-id-20 div.sk-toggleable {background-color: white;}#sk-container-id-20 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-20 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-20 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-20 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-20 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-20 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-20 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-20 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-20 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-20 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-20 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-20 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-20 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-20 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-20 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-20 div.sk-item {position: relative;z-index: 1;}#sk-container-id-20 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-20 div.sk-item::before, #sk-container-id-20 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-20 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-20 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-20 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-20 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-20 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-20 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-20 div.sk-label-container {text-align: center;}#sk-container-id-20 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-20 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-20\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" checked><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 375
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets make predictions\n",
+        "y_train_preds_dt = dt.predict(X_train_array)\n",
+        "y_test_preds_dt = dt.predict(X_test_array)"
+      ],
+      "metadata": {
+        "id": "akza3biNLjIy"
+      },
+      "execution_count": 376,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#evaluation of training model\n",
+        "print_metrics(y_train, y_train_preds_dt)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nC9XDqquLjLV",
+        "outputId": "65941a54-1210-4c34-c971-e307ed455c9c"
+      },
+      "execution_count": 377,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 97.5%\n",
+            " precision score is 99.22%\n",
+            "recall score is 95.74%\n",
+            "f1 score is 97.45%\n",
+            "roc auc score is 97.5%\n",
+            "confusion matrix is [[398   3]\n",
+            " [ 17 382]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.96      0.99      0.98       401\n",
+            "           1       0.99      0.96      0.97       399\n",
+            "\n",
+            "    accuracy                           0.97       800\n",
+            "   macro avg       0.98      0.97      0.97       800\n",
+            "weighted avg       0.98      0.97      0.97       800\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(97.5,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 377
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print_metrics(y_test, y_test_preds_dt)\n",
+        "#evaluate testing model"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "TpWDRfDcLjOy",
+        "outputId": "6344bd9d-6589-44b5-f1f1-d7356e96db12"
+      },
+      "execution_count": 378,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 81.5%\n",
+            " precision score is 79.09%\n",
+            "recall score is 86.14%\n",
+            "f1 score is 82.46%\n",
+            "roc auc score is 81.45%\n",
+            "confusion matrix is [[76 23]\n",
+            " [14 87]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.84      0.77      0.80        99\n",
+            "           1       0.79      0.86      0.82       101\n",
+            "\n",
+            "    accuracy                           0.81       200\n",
+            "   macro avg       0.82      0.81      0.81       200\n",
+            "weighted avg       0.82      0.81      0.81       200\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(81.5,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 378
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#store our results in a table\n",
+        "test_accuracy=round(accuracy_score(y2_test, y_test_preds_dt)*100,2)\n",
+        "test_precision=round(precision_score(y2_test, y_test_preds_dt)*100,2)\n",
+        "train_accuracy=round(accuracy_score(y2_train, y_train_preds_dt)*100,2)\n",
+        "train_precision=round(precision_score(y2_train, y_train_preds_dt)*100,2)\n",
+        "results.loc[len(results)] = ['Decision Tree', train_precision, train_accuracy, test_precision, test_accuracy]"
+      ],
+      "metadata": {
+        "id": "A9mgMVR6N_fc"
+      },
+      "execution_count": 379,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Support Vector Machines (SVM):**"
+      ],
+      "metadata": {
+        "id": "SgYYF4BLOJ-F"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "svm = SVC()\n",
+        "svm.fit(X_train_array, y_train)\n",
+        "predictions = svm.predict(X_test_array)\n",
+        "#prediction\n",
+        "y_train_preds_sv = svm.predict(X_train_array)\n",
+        "y_test_preds_sv = svm.predict(X_test_array)"
+      ],
+      "metadata": {
+        "id": "OfqB9MUKOZrO"
+      },
+      "execution_count": 380,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#evaluation of training model\n",
+        "print_metrics(y_train, y_train_preds_dt)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qnXL9URKOZt1",
+        "outputId": "ea89bfa4-4142-4a34-a61e-0f4ed1bc4ece"
+      },
+      "execution_count": 381,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 97.5%\n",
+            " precision score is 99.22%\n",
+            "recall score is 95.74%\n",
+            "f1 score is 97.45%\n",
+            "roc auc score is 97.5%\n",
+            "confusion matrix is [[398   3]\n",
+            " [ 17 382]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.96      0.99      0.98       401\n",
+            "           1       0.99      0.96      0.97       399\n",
+            "\n",
+            "    accuracy                           0.97       800\n",
+            "   macro avg       0.98      0.97      0.97       800\n",
+            "weighted avg       0.98      0.97      0.97       800\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(97.5,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 381
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print_metrics(y_test, y_test_preds_dt)\n",
+        "#evaluate testing model"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8QHiz_-MOZwb",
+        "outputId": "0d3a7d7d-46af-4dad-c7e5-06c334e2baa7"
+      },
+      "execution_count": 382,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "accuracy score is 81.5%\n",
+            " precision score is 79.09%\n",
+            "recall score is 86.14%\n",
+            "f1 score is 82.46%\n",
+            "roc auc score is 81.45%\n",
+            "confusion matrix is [[76 23]\n",
+            " [14 87]]\n",
+            "classification report is               precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.84      0.77      0.80        99\n",
+            "           1       0.79      0.86      0.82       101\n",
+            "\n",
+            "    accuracy                           0.81       200\n",
+            "   macro avg       0.82      0.81      0.81       200\n",
+            "weighted avg       0.82      0.81      0.81       200\n",
+            "\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(81.5,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 382
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#store our results in a table\n",
+        "test_accuracy=round(accuracy_score(y2_test, y_test_preds_sv)*100,2)\n",
+        "test_precision=round(precision_score(y2_test, y_test_preds_sv)*100,2)\n",
+        "train_accuracy=round(accuracy_score(y2_train, y_train_preds_sv)*100,2)\n",
+        "train_precision=round(precision_score(y2_train, y_train_preds_sv)*100,2)\n",
+        "results.loc[len(results)] = ['SVM', train_precision, train_accuracy, test_precision, test_accuracy]"
+      ],
+      "metadata": {
+        "id": "UvRWI_PiOZzm"
+      },
+      "execution_count": 383,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Sort Our Result Table**"
+      ],
+      "metadata": {
+        "id": "CowmwUnHRsoy"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "results = results.sort_values(by='accuracy_test(%)', ascending=False)\n",
+        "results"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "Z0fP3ENiRP3J",
+        "outputId": "af8236ee-10cd-4c6a-d53e-8850e210a866"
+      },
+      "execution_count": 384,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "                  Model  accuracy_train(%)  precision_train(%)  \\\n",
+              "4                   SVM              97.57               94.25   \n",
+              "0   Logistic Regression              93.51               90.38   \n",
+              "2         Random Forest              91.84               80.88   \n",
+              "3         Decision Tree              99.22               97.50   \n",
+              "1  Guassian Naive Bayes              69.61               77.88   \n",
+              "\n",
+              "   accuracy_test(%)  precision_test(%)  \n",
+              "4             92.86               85.5  \n",
+              "0             88.66               87.0  \n",
+              "2             87.18               78.5  \n",
+              "3             79.09               81.5  \n",
+              "1             68.61               75.0  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-1e073585-671c-4592-b7fa-942c76d10aa0\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Model</th>\n",
+              "      <th>accuracy_train(%)</th>\n",
+              "      <th>precision_train(%)</th>\n",
+              "      <th>accuracy_test(%)</th>\n",
+              "      <th>precision_test(%)</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>SVM</td>\n",
+              "      <td>97.57</td>\n",
+              "      <td>94.25</td>\n",
+              "      <td>92.86</td>\n",
+              "      <td>85.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Logistic Regression</td>\n",
+              "      <td>93.51</td>\n",
+              "      <td>90.38</td>\n",
+              "      <td>88.66</td>\n",
+              "      <td>87.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Random Forest</td>\n",
+              "      <td>91.84</td>\n",
+              "      <td>80.88</td>\n",
+              "      <td>87.18</td>\n",
+              "      <td>78.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Decision Tree</td>\n",
+              "      <td>99.22</td>\n",
+              "      <td>97.50</td>\n",
+              "      <td>79.09</td>\n",
+              "      <td>81.5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Guassian Naive Bayes</td>\n",
+              "      <td>69.61</td>\n",
+              "      <td>77.88</td>\n",
+              "      <td>68.61</td>\n",
+              "      <td>75.0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
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+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
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+              "  </svg>\n",
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+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
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+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
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+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-1e073585-671c-4592-b7fa-942c76d10aa0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-1e073585-671c-4592-b7fa-942c76d10aa0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
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+              "\n",
+              "\n",
+              "<div id=\"df-783e1c19-076f-4502-af51-89e8fee92a2b\">\n",
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+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
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+              "      --bg-color: #3B4455;\n",
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+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
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+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-783e1c19-076f-4502-af51-89e8fee92a2b button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_dc7c33f6-6112-49dd-a79c-7c3c6039bfbd\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
+              "        border-radius: 50%;\n",
+              "        cursor: pointer;\n",
+              "        display: none;\n",
+              "        fill: #1967D2;\n",
+              "        height: 32px;\n",
+              "        padding: 0 0 0 0;\n",
+              "        width: 32px;\n",
+              "      }\n",
+              "\n",
+              "      .colab-df-generate:hover {\n",
+              "        background-color: #E2EBFA;\n",
+              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "        fill: #174EA6;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate {\n",
+              "        background-color: #3B4455;\n",
+              "        fill: #D2E3FC;\n",
+              "      }\n",
+              "\n",
+              "      [theme=dark] .colab-df-generate:hover {\n",
+              "        background-color: #434B5C;\n",
+              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "        fill: #FFFFFF;\n",
+              "      }\n",
+              "    </style>\n",
+              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('results')\"\n",
+              "            title=\"Generate code using this dataframe.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "       width=\"24px\">\n",
+              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_dc7c33f6-6112-49dd-a79c-7c3c6039bfbd button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('results');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "results",
+              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Logistic Regression\",\n          \"Guassian Naive Bayes\",\n          \"Random Forest\"\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy_train(%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11.970365491496072,\n        \"min\": 69.61,\n        \"max\": 99.22,\n        \"samples\": [\n          93.51,\n          69.61,\n          91.84\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision_train(%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.48421003983282,\n        \"min\": 77.88,\n        \"max\": 97.5,\n        \"samples\": [\n          90.38,\n          77.88,\n          80.88\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy_test(%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.60072653500765,\n        \"min\": 68.61,\n        \"max\": 92.86,\n        \"samples\": [\n          88.66,\n          68.61,\n          87.18\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision_test(%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.937104414532874,\n        \"min\": 75.0,\n        \"max\": 87.0,\n        \"samples\": [\n          87.0,\n          75.0,\n          78.5\n        ],\n        \"num_unique_values\": 5,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 384
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Since we got highest accuracy using SVM let's use it for confusion matrix and even for model inference**"
+      ],
+      "metadata": {
+        "id": "ulvVoCplJPUk"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm = confusion_matrix(y_test, y_test_preds_sv)"
+      ],
+      "metadata": {
+        "id": "1EYzNA3_H5Bx"
+      },
+      "execution_count": 385,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(10,6))\n",
+        "sns.heatmap(cm, annot= True, cmap = \"YlGnBu\", xticklabels = ['Negative', 'Positive'], yticklabels = ['Negative', 'Positive'])\n",
+        "plt.xlabel('Predicted Values')\n",
+        "plt.ylabel('Actual Values')"
+      ],
+      "metadata": {
+        "id": "HzDy88ewwEPm",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 559
+        },
+        "outputId": "6d7931e9-69a8-4e3a-b21e-d9711be69501"
+      },
+      "execution_count": 386,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Text(95.72222222222221, 0.5, 'Actual Values')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 386
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# **Model Inference using Logistic Regression Algorithm **"
+      ],
+      "metadata": {
+        "id": "7TaoRez3J11y"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "classifier = SVC()\n",
+        "classifier.fit(X_train_array, y_train)\n"
+      ],
+      "metadata": {
+        "id": "XD2f8BmPwdZY",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 74
+        },
+        "outputId": "44281fa1-3657-4d23-f21b-b4975632672f"
+      },
+      "execution_count": 387,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SVC()"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-21 {color: black;background-color: white;}#sk-container-id-21 pre{padding: 0;}#sk-container-id-21 div.sk-toggleable {background-color: white;}#sk-container-id-21 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-21 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-21 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-21 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-21 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-21 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-21 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-21 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-21 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-21 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-21 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-21 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-21 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-21 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-21 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-21 div.sk-item {position: relative;z-index: 1;}#sk-container-id-21 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-21 div.sk-item::before, #sk-container-id-21 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-21 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-21 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-21 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-21 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-21 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-21 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-21 div.sk-label-container {text-align: center;}#sk-container-id-21 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-21 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-21\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" checked><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 387
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#lets define a function which vectorize our new given sample input and then preict the review\n",
+        "def predict_sentiment(sample):\n",
+        "  tokenized_review = text_process(sample) #removing stopwords nad punctuation from sample review\n",
+        "  vectorized_review = vectorizer.transform([sample]).toarray()\n",
+        "  return classifier.predict(vectorized_review)"
+      ],
+      "metadata": {
+        "id": "gePlEUX0IdkT"
+      },
+      "execution_count": 388,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "#if you want you can give input to sample variable or we can give like this:\n",
+        "def predict(sample):\n",
+        "  if predict_sentiment(sample):\n",
+        "    print(\"it is a positive review\")\n",
+        "  else:\n",
+        "    print(\"it is negative review\")\n",
+        "new_review = \"IT was minblowing, fantastic\"\n",
+        "predict(new_review)"
+      ],
+      "metadata": {
+        "id": "SFQlCY9uP_nU",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1875f50b-9240-4d64-9644-546488cd61a7"
+      },
+      "execution_count": 389,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "it is a positive review\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n",
+        "new_review = \"tasteless\"\n",
+        "predict(new_review)"
+      ],
+      "metadata": {
+        "id": "p_mIJhfsQJCY",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "1d557dc4-c01b-4fa9-bbd6-3cc1dbec90fd"
+      },
+      "execution_count": 390,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "it is negative review\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "new_review = \"it is not too good\"\n",
+        "predict(new_review)"
+      ],
+      "metadata": {
+        "id": "NiUGZVwzSFGa",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e20a8fb8-802d-493c-ce23-8e842c1ca40e"
+      },
+      "execution_count": 391,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "it is negative review\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "new_review = \"fabulous, amazing\"\n",
+        "predict(new_review)"
+      ],
+      "metadata": {
+        "id": "0gZBkRIFSKAL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "790e23de-7363-497e-d56b-e7d4f715f1a7"
+      },
+      "execution_count": 392,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "it is a positive review\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Hence my ML Model predicts sentiments are positive or negative too correctly even if negation words such as not, no, nay are present in our review. Generally negation words opposes positive condition, so considering them is important in order to train our model correctly. Hence I didn't remove negation stopwords."
+      ],
+      "metadata": {
+        "id": "B3KZK_GIWZAe"
+      }
+    }
+  ]
+}
\ No newline at end of file

From 3606fb29e054acbeb2b99977d567d84aa3dd7465 Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 13:36:43 +0530
Subject: [PATCH 04/11] Delete Sentiment Analysis for Restaurant Reviews (NLP)
 /Model/h

---
 Sentiment Analysis for Restaurant Reviews (NLP) /Model/h | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Model/h

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /Model/h b/Sentiment Analysis for Restaurant Reviews (NLP) /Model/h
deleted file mode 100644
index 8b1378917..000000000
--- a/Sentiment Analysis for Restaurant Reviews (NLP) /Model/h	
+++ /dev/null
@@ -1 +0,0 @@
-

From 03256944da8bd1784960e37bdb9323dcde50c80d Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 13:42:17 +0530
Subject: [PATCH 05/11] Create README.md

---
 .../Dataset/README.md                                     | 8 ++++++++
 1 file changed, 8 insertions(+)
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Dataset/README.md

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /Dataset/README.md b/Sentiment Analysis for Restaurant Reviews (NLP) /Dataset/README.md
new file mode 100644
index 000000000..4b1e8995a
--- /dev/null
+++ b/Sentiment Analysis for Restaurant Reviews (NLP) /Dataset/README.md	
@@ -0,0 +1,8 @@
+# Sentiment Analysis for Restaurant Reviews (NLP) Dataset
+
+The Dataset used here is taken from the Kaggle database website. You can download the file from the link given here, Restaurant Reviews Analysis and Prediction.(https://www.kaggle.com/datasets/d4rklucif3r/restaurant-reviews )
+
+## About the dataset
+
+This Dataset contains two COLUMNS Customer Reviews and Liked. It has 1000 rows/entries.
+Customer reviews tells us about the reviews given by the customers for a food in restaurant and liked column tells about whether they liked the food or not.

From 2f96131d8904ed0f7b49641ca5c0dbbc2b357209 Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 13:45:18 +0530
Subject: [PATCH 06/11] Create h

---
 Sentiment Analysis for Restaurant Reviews (NLP) /Images/h | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Images/h

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /Images/h b/Sentiment Analysis for Restaurant Reviews (NLP) /Images/h
new file mode 100644
index 000000000..8b1378917
--- /dev/null
+++ b/Sentiment Analysis for Restaurant Reviews (NLP) /Images/h	
@@ -0,0 +1 @@
+

From 4f71fb8d1cc9528d51782f38eabf5e99e8001354 Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 13:45:46 +0530
Subject: [PATCH 07/11] Add files via upload

---
 .../Number_of_characters_in_each_review.png    | Bin 0 -> 16216 bytes
 .../Images/barchart.png                        | Bin 0 -> 14002 bytes
 .../Images/negative_wordcloud.png              | Bin 0 -> 290359 bytes
 .../Images/piechart.png                        | Bin 0 -> 11676 bytes
 .../Images/wordcloud_positive.png              | Bin 0 -> 303065 bytes
 5 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Images/Number_of_characters_in_each_review.png
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Images/barchart.png
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Images/negative_wordcloud.png
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Images/piechart.png
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Images/wordcloud_positive.png

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /Images/Number_of_characters_in_each_review.png b/Sentiment Analysis for Restaurant Reviews (NLP) /Images/Number_of_characters_in_each_review.png
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diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /Images/barchart.png b/Sentiment Analysis for Restaurant Reviews (NLP) /Images/barchart.png
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From c714566faef9d640ad972d9f3a743cd3d515e413 Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 13:46:57 +0530
Subject: [PATCH 08/11] Delete Sentiment Analysis for Restaurant Reviews (NLP)
 /Images/h

---
 Sentiment Analysis for Restaurant Reviews (NLP) /Images/h | 1 -
 1 file changed, 1 deletion(-)
 delete mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /Images/h

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /Images/h b/Sentiment Analysis for Restaurant Reviews (NLP) /Images/h
deleted file mode 100644
index 8b1378917..000000000
--- a/Sentiment Analysis for Restaurant Reviews (NLP) /Images/h	
+++ /dev/null
@@ -1 +0,0 @@
-

From 6358b378d97774f68877a88f500e7fc1611b0b82 Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 13:51:23 +0530
Subject: [PATCH 09/11] Create README.md

---
 .../README.md                                  | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)
 create mode 100644 Sentiment Analysis for Restaurant Reviews (NLP) /README.md

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /README.md b/Sentiment Analysis for Restaurant Reviews (NLP) /README.md
new file mode 100644
index 000000000..e9853e73c
--- /dev/null
+++ b/Sentiment Analysis for Restaurant Reviews (NLP) /README.md	
@@ -0,0 +1,18 @@
+<h1>Sentiment Analysis for Restaurant Reviews (NLP)</h1>
+
+**GOAL**
+
+To build a machine learning model for predicting the Sentiments of Customer based on their review on a Restaurant.
+
+**DATASET**
+
+[https://www.kaggle.com/datasets/d4rklucif3r/restaurant-reviews]
+
+**DESCRIPTION**
+
+This Dataset contains two COLUMNS Customer Reviews and Liked. It has 1000 rows/entries.
+Customer reviews tells us about the reviews given by the customers for a food in restaurant and liked column tells about whether they liked the food or not.
+
+### Visualization and EDA of different attributes:
+
+<img alt="Distribution" src="./Images/distribution plot 1.png">

From 1e0f04f692240f2a1e15c88bf7da03aa358c574f Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 14:05:48 +0530
Subject: [PATCH 10/11] Update README.md

---
 .../README.md                                 | 58 ++++++++++++++++++-
 1 file changed, 57 insertions(+), 1 deletion(-)

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /README.md b/Sentiment Analysis for Restaurant Reviews (NLP) /README.md
index e9853e73c..b6b2ede30 100644
--- a/Sentiment Analysis for Restaurant Reviews (NLP) /README.md	
+++ b/Sentiment Analysis for Restaurant Reviews (NLP) /README.md	
@@ -15,4 +15,60 @@ Customer reviews tells us about the reviews given by the customers for a food in
 
 ### Visualization and EDA of different attributes:
 
-<img alt="Distribution" src="./Images/distribution plot 1.png">
+<img alt="length_of_review" src="./Images/Number_of_characters_in_each_review.png">
+
+<img alt="barchart" src="./Images/barchart.png">
+
+<img alt="piechart" src="./Images/piechart.png">
+
+**Positive Review WordCloud**
+
+<img alt="wordcloud" src="./Images/wordcloud_positive.png">
+
+**Negative Review WordCloud**
+
+<img alt="wordcloud" src="./Images/negative_wordcloud.png">
+
+**MODELS USED**
+
+| Model                     | accuracy_train(%) | precision_train(%) | accuracy_test(%)  | precision_test(%)   |
+|---------------------------|-------------------|--------------------|-------------------|---------------------|
+|SVM		                    |97.57          	  |94.25	             |92.86	             |85.5                 |
+|Logistic Regression	      |93.51	            |90.38	             |88.66	             |87.0                 |
+|Random Forest	            |91.84	            |80.88	             |87.18	             |78.5                 |
+|Decision Tree	            |99.22	            |97.50	             |79.09	             |81.5                 |
+|Guassian Naive Bayes	      |69.61	            |77.88	             |68.61	             |75.0                 |
+
+
+**WHAT I HAD DONE**
+
+* Load the dataset which is CSV format.
+* It has 1000 entries(Rows), 2 columns.
+* Checked for missing values and cleaned the data accordingly.
+* Analyzed the data, found insights and visualized them accordingly.
+* Found detailed insights of different columns with target variable using plotting libraries.
+* Train the datasets by different models and saves their accuracies into a dataframe.
+
+
+**LIBRARIES NEEDED**
+
+1. Pandas
+2. Matplotlib
+3. Sklearn
+4. NumPy
+5. nltk
+6. Seaborn
+7. wordcloud
+
+**CONCLUSION**
+
+- ML Model predicts sentiments are positive or negative too correctly even if negation words such as not, no, nay are present in our review. Generally negation words opposes positive condition, so considering them is important in order to train our model correctly. Hence I didn't remove negation stopwords.
+- We got highest testing accuracy using SVM algorithm which is around 93%
+- We got good accuracy for other algorithms also
+
+
+**YOUR NAME**
+
+*Ghousiya Begum*
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/ghousiya-begum-a9b634258/)  [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/ghousiya47)

From 10ec6a9d78006ae6be88a8fba39d5a0c2c6d0e27 Mon Sep 17 00:00:00 2001
From: Ghousiya Begum <itzzghousiya04@gmail.com>
Date: Mon, 19 Feb 2024 21:40:05 +0530
Subject: [PATCH 11/11] Update README.md

---
 .../README.md                                 | 20 ++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/Sentiment Analysis for Restaurant Reviews (NLP) /README.md b/Sentiment Analysis for Restaurant Reviews (NLP) /README.md
index b6b2ede30..1403daeab 100644
--- a/Sentiment Analysis for Restaurant Reviews (NLP) /README.md	
+++ b/Sentiment Analysis for Restaurant Reviews (NLP) /README.md	
@@ -29,6 +29,24 @@ Customer reviews tells us about the reviews given by the customers for a food in
 
 <img alt="wordcloud" src="./Images/negative_wordcloud.png">
 
+**NLP TECHNIQUES USED**
+
+1. **Text Cleaning:**
+
+    - **Punctuation Removal:** I have removed punctuation from the text using a list comprehension to keep only non-punctuation characters.
+    - **Stopword Removal:** I have removed common English stopwords using the NLTK library. Stopwords are words that are frequently used but often carry little meaning (e.g., "the," "and," "is").
+    - **Stopword Customization:** I have a set of custom stopwords ({'not', 'nor', 'no', "don't", "was'nt", "aren't", "didn't"}) that I want to exclude from removal. This is important to preserve negation words like "not" which can change the sentiment of a sentence.
+
+2. **Vectorization:**
+
+    - Using CountVectorizer I have represented each review as a bag of its words, ignoring grammar and word order but keeping track of word frequency i.e, Tokenization. And then I have converted this tokenized review into array of Numerical vector which is nothing but Vectorization.
+    - TF-IDF Vectorization (vectorizer.transform): You use the vectorizer.transform method, which suggests the use of TF-IDF vectorization. TF-IDF represents the importance of a term in a document relative to its importance in a collection of documents. It converts the text into a numerical vector.
+
+3. **WORDCLOUD:**
+
+     - Generating a word cloud is a data visualization technique commonly used in NLP to represent the most frequent words in a text corpus. I have generated word cloud for both positive reviews and negative reviews separately as shown in image above.
+
+
 **MODELS USED**
 
 | Model                     | accuracy_train(%) | precision_train(%) | accuracy_test(%)  | precision_test(%)   |
@@ -46,7 +64,7 @@ Customer reviews tells us about the reviews given by the customers for a food in
 * It has 1000 entries(Rows), 2 columns.
 * Checked for missing values and cleaned the data accordingly.
 * Analyzed the data, found insights and visualized them accordingly.
-* Found detailed insights of different columns with target variable using plotting libraries.
+* Tokenized and Vectorized the Review Column, and then used this array of vectorized review to train my model.
 * Train the datasets by different models and saves their accuracies into a dataframe.