diff --git a/Top Foreign Languages Analysis/Dataset/README.md b/Top Foreign Languages Analysis/Dataset/README.md
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+# Top Foreign Languages Dataset
+
+The Dataset used here is taken from the Kaggle database website. You can download the file from the link given here, Top Foreign Languagess Analysis and Prediction.( https://www.kaggle.com/datasets/timmofeyy/top-foreign-languages-preply-tutors)
+
+## About the dataset
+
+The data includes the main languages and the most popular among students. The datasets have 8 csv files for 8 different top foreign languages.
+
+- **columns_description**: Each CSV File contains the description of all the features.
+
+  name: The name of the tutor.
+
+  badge: Any badge or certification associated with the tutor.
+
+  rating: The overall rating of the tutor.
+
+  reviews_number: The number of reviews the tutor has received.
+
+  usd_price: The price charged by the tutor for their services.
+
+  language: The languages spoken by the tutor.
+
+  active_students: The number of active students the tutor is currently teaching.
+
+  lessons_number: The total number of lessons conducted by the tutor.
+
+  speak: The languages spoken by the tutor.
+
+  description: A brief description or snippet provided by the tutor.
+
+  link: The link or URL to the tutor's profile.
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diff --git a/Top Foreign Languages Analysis/Model/Top_Foreign_Language_Analysis.ipynb b/Top Foreign Languages Analysis/Model/Top_Foreign_Language_Analysis.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lIBt_bZ3sSVO"
+      },
+      "outputs": [],
+      "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",
+        "import warnings\n",
+        "from numpy import math\n",
+        "import plotly.express as px\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.preprocessing import LabelEncoder\n",
+        "from sklearn.model_selection import cross_val_score\n",
+        "import missingno as msn\n",
+        "from sklearn.impute import SimpleImputer\n",
+        "from sklearn.preprocessing import OneHotEncoder\n",
+        "from sklearn.compose import ColumnTransformer\n",
+        "from sklearn.pipeline import Pipeline\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from sklearn.neural_network import MLPRegressor\n",
+        "from tensorflow.keras.models import Sequential\n",
+        "from tensorflow.keras.layers import Dense\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+        "from sklearn.tree import DecisionTreeRegressor\n",
+        "from sklearn.linear_model import LinearRegression, Ridge, ElasticNet, SGDRegressor\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "warnings.filterwarnings('ignore')\n",
+        "from zipfile import ZipFile"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oEcadNSzV5PB"
+      },
+      "source": [
+        "As out dataset is a zip file of 8 different CSV files for 8 different Languages let's first unzip it, and concatenate all CSV files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hnB-vseztnJj"
+      },
+      "outputs": [],
+      "source": [
+        "# create an empty list to store the dataframes\n",
+        "li = []\n",
+        "# open the zip file\n",
+        "with ZipFile('/content/drive/MyDrive/archive.zip') as zf:\n",
+        "    # loop through each file in the zip file\n",
+        "    for filename in zf.namelist():\n",
+        "        # check if the file is a csv file\n",
+        "        if filename.endswith('.csv'):\n",
+        "            # read the csv file as a pandas dataframe\n",
+        "            df = pd.read_csv(zf.open(filename))\n",
+        "            # append the dataframe to the list\n",
+        "            li.append(df)\n",
+        "# concatenate all the dataframes in the list\n",
+        "df = pd.concat(li, ignore_index=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5lXJsaGnl09I"
+      },
+      "source": [
+        "Now let's perform some Exploratory Data Analysis."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wS0HoNio3BTG",
+        "outputId": "b563f8a3-cedd-4f9c-9188-89cd77c3a630"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Index(['Unnamed: 0', 'name', 'badge', 'rating', 'reviews_number', 'usd_price',\n",
+              "       'language', 'active_students', 'lessons_number', 'speak', 'description',\n",
+              "       'link'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "execution_count": 93,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.columns"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wBhAu0IwmhVX"
+      },
+      "source": [
+        "The code snippet msn.matrix(df) is using the missingno library to create a matrix visualization of missing values in the DataFrame df. This visualization helps to quickly identify the patterns of missing data across different columns.\n",
+        "\n",
+        "Here's what the visualization indicates:\n",
+        "\n",
+        "Empty White Lines: Each horizontal line represents a row in the DataFrame, and the empty white spaces in these lines indicate missing values in the corresponding columns.\n",
+        "\n",
+        "Dark Bars: The vertical bars on the right side of the matrix represent the completeness of each column. A dark bar means there are no missing values in that column, while a lighter bar indicates the presence of missing values."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 687
+        },
+        "id": "0L3OZyTPHVii",
+        "outputId": "a8154ec8-99b3-4749-f509-1ab260a51f93"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "execution_count": 94,
+          "metadata": {},
+          "output_type": "execute_result"
+        },
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 2500x1000 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "msn.matrix(df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "4SA6jzdFHhq-",
+        "outputId": "9638e186-bc73-4e49-bd7e-ee1b5468b16b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "English     16716\n",
+              "Spanish      6098\n",
+              "Chinese      3144\n",
+              "Arabic       2071\n",
+              "French       2013\n",
+              "Italian      1576\n",
+              "Japanese     1284\n",
+              "German        926\n",
+              "Korean        614\n",
+              "Name: language, dtype: int64"
+            ]
+          },
+          "execution_count": 95,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#let's figure out how many languages do we have in the dataframe,\n",
+        "df['language'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "A_olbHjaIsM1",
+        "outputId": "0fba7383-17d3-4990-82d1-e567836dacec"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Super tutor    9284\n",
+              "Name: badge, dtype: int64"
+            ]
+          },
+          "execution_count": 96,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#let's find number of tutors who have super tutor badge\n",
+        "df['badge'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 414
+        },
+        "id": "e433yQBmJBqB",
+        "outputId": "872afb69-51d2-4967-a8b3-d95fd8661b23"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-78bd2dea-86d8-4b2e-8ff4-75ae5b995227\" 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>Unnamed: 0</th>\n",
+              "      <th>name</th>\n",
+              "      <th>badge</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>reviews_number</th>\n",
+              "      <th>usd_price</th>\n",
+              "      <th>language</th>\n",
+              "      <th>active_students</th>\n",
+              "      <th>lessons_number</th>\n",
+              "      <th>speak</th>\n",
+              "      <th>description</th>\n",
+              "      <th>link</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Mahmoud H.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>25</td>\n",
+              "      <td>Arabic</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8382.0</td>\n",
+              "      <td>Arabic (Native), English (Advanced)</td>\n",
+              "      <td>َ📜EJAZA For Quran,Arabic,15 years Experience🎓a...</td>\n",
+              "      <td>https://preply.com/en/tutor/61569</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Dr. Abdul M.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>5</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>12</td>\n",
+              "      <td>Arabic</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>1384.0</td>\n",
+              "      <td>Arabic (Proficient), English (Proficient)</td>\n",
+              "      <td>Doctorate in Arabic with 5 years of experience...</td>\n",
+              "      <td>https://preply.com/en/tutor/31502</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Atika M.</td>\n",
+              "      <td>Super tutor</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>101.0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>Arabic</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>4963.0</td>\n",
+              "      <td>Arabic (Native), English (Proficient)</td>\n",
+              "      <td>Certified tutor with 7 years of Online teachin...</td>\n",
+              "      <td>https://preply.com/en/tutor/45713</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Abdelghafour R.</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>18</td>\n",
+              "      <td>Arabic</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>1337.0</td>\n",
+              "      <td>Arabic (Native), English (Advanced)</td>\n",
+              "      <td>Certified Tutor of Arabic and French ,  native...</td>\n",
+              "      <td>https://preply.com/en/tutor/21749</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Muhammad M.</td>\n",
+              "      <td>Super tutor</td>\n",
+              "      <td>5</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>18</td>\n",
+              "      <td>Arabic</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>3039.0</td>\n",
+              "      <td>Arabic (Native), English (Advanced)</td>\n",
+              "      <td>Certified tutor with 8 years of experience tea...</td>\n",
+              "      <td>https://preply.com/en/tutor/39825</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-78bd2dea-86d8-4b2e-8ff4-75ae5b995227')\"\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-78bd2dea-86d8-4b2e-8ff4-75ae5b995227 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-78bd2dea-86d8-4b2e-8ff4-75ae5b995227');\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-175dc9c7-ca34-491c-810f-bf4868171a45\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-175dc9c7-ca34-491c-810f-bf4868171a45')\"\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-175dc9c7-ca34-491c-810f-bf4868171a45 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   Unnamed: 0             name        badge rating  reviews_number  usd_price  \\\n",
+              "0           0       Mahmoud H.          NaN    4.8            28.0         25   \n",
+              "1           1     Dr. Abdul M.          NaN      5            10.0         12   \n",
+              "2           2         Atika M.  Super tutor    4.8           101.0         20   \n",
+              "3           3  Abdelghafour R.          NaN    4.9            19.0         18   \n",
+              "4           4      Muhammad M.  Super tutor      5            38.0         18   \n",
+              "\n",
+              "  language  active_students  lessons_number  \\\n",
+              "0   Arabic              8.0          8382.0   \n",
+              "1   Arabic             24.0          1384.0   \n",
+              "2   Arabic             14.0          4963.0   \n",
+              "3   Arabic              4.0          1337.0   \n",
+              "4   Arabic             17.0          3039.0   \n",
+              "\n",
+              "                                       speak  \\\n",
+              "0        Arabic (Native), English (Advanced)   \n",
+              "1  Arabic (Proficient), English (Proficient)   \n",
+              "2      Arabic (Native), English (Proficient)   \n",
+              "3        Arabic (Native), English (Advanced)   \n",
+              "4        Arabic (Native), English (Advanced)   \n",
+              "\n",
+              "                                         description  \\\n",
+              "0  َ📜EJAZA For Quran,Arabic,15 years Experience🎓a...   \n",
+              "1  Doctorate in Arabic with 5 years of experience...   \n",
+              "2  Certified tutor with 7 years of Online teachin...   \n",
+              "3  Certified Tutor of Arabic and French ,  native...   \n",
+              "4  Certified tutor with 8 years of experience tea...   \n",
+              "\n",
+              "                                link  \n",
+              "0  https://preply.com/en/tutor/61569  \n",
+              "1  https://preply.com/en/tutor/31502  \n",
+              "2  https://preply.com/en/tutor/45713  \n",
+              "3  https://preply.com/en/tutor/21749  \n",
+              "4  https://preply.com/en/tutor/39825  "
+            ]
+          },
+          "execution_count": 97,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.head() #prints first 5 rows"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "RJbuMbYOJaeE",
+        "outputId": "2cc411b7-b703-4728-921a-f4595ddac34b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-530a3a69-87bf-48ac-b679-bf840c40ce60\" 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>Unnamed: 0</th>\n",
+              "      <th>reviews_number</th>\n",
+              "      <th>usd_price</th>\n",
+              "      <th>active_students</th>\n",
+              "      <th>lessons_number</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>34442.000000</td>\n",
+              "      <td>24078.000000</td>\n",
+              "      <td>34442.000000</td>\n",
+              "      <td>28899.000000</td>\n",
+              "      <td>25284.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>4938.282736</td>\n",
+              "      <td>13.918266</td>\n",
+              "      <td>15.761977</td>\n",
+              "      <td>16.454203</td>\n",
+              "      <td>882.523849</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>4852.090132</td>\n",
+              "      <td>18.623789</td>\n",
+              "      <td>10.554000</td>\n",
+              "      <td>53.447999</td>\n",
+              "      <td>1324.630284</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>1010.000000</td>\n",
+              "      <td>3.000000</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>2.000000</td>\n",
+              "      <td>87.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>2912.000000</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>15.000000</td>\n",
+              "      <td>8.000000</td>\n",
+              "      <td>382.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>8104.750000</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>20.000000</td>\n",
+              "      <td>18.000000</td>\n",
+              "      <td>1121.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>16715.000000</td>\n",
+              "      <td>336.000000</td>\n",
+              "      <td>100.000000</td>\n",
+              "      <td>3218.000000</td>\n",
+              "      <td>18192.000000</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-530a3a69-87bf-48ac-b679-bf840c40ce60')\"\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-530a3a69-87bf-48ac-b679-bf840c40ce60 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-530a3a69-87bf-48ac-b679-bf840c40ce60');\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-90c57ca7-1671-4ada-b38e-8a156f524af0\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-90c57ca7-1671-4ada-b38e-8a156f524af0')\"\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-90c57ca7-1671-4ada-b38e-8a156f524af0 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "         Unnamed: 0  reviews_number     usd_price  active_students  \\\n",
+              "count  34442.000000    24078.000000  34442.000000     28899.000000   \n",
+              "mean    4938.282736       13.918266     15.761977        16.454203   \n",
+              "std     4852.090132       18.623789     10.554000        53.447999   \n",
+              "min        0.000000        1.000000      3.000000         1.000000   \n",
+              "25%     1010.000000        3.000000      8.000000         2.000000   \n",
+              "50%     2912.000000        8.000000     15.000000         8.000000   \n",
+              "75%     8104.750000       18.000000     20.000000        18.000000   \n",
+              "max    16715.000000      336.000000    100.000000      3218.000000   \n",
+              "\n",
+              "       lessons_number  \n",
+              "count    25284.000000  \n",
+              "mean       882.523849  \n",
+              "std       1324.630284  \n",
+              "min          1.000000  \n",
+              "25%         87.000000  \n",
+              "50%        382.000000  \n",
+              "75%       1121.000000  \n",
+              "max      18192.000000  "
+            ]
+          },
+          "execution_count": 98,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "df.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7U8E0sHMJgFR",
+        "outputId": "61868064-cc8e-48d5-e3d7-1cc08c6e777a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 34442 entries, 0 to 34441\n",
+            "Data columns (total 12 columns):\n",
+            " #   Column           Non-Null Count  Dtype  \n",
+            "---  ------           --------------  -----  \n",
+            " 0   Unnamed: 0       34442 non-null  int64  \n",
+            " 1   name             34442 non-null  object \n",
+            " 2   badge            9284 non-null   object \n",
+            " 3   rating           34442 non-null  object \n",
+            " 4   reviews_number   24078 non-null  float64\n",
+            " 5   usd_price        34442 non-null  int64  \n",
+            " 6   language         34442 non-null  object \n",
+            " 7   active_students  28899 non-null  float64\n",
+            " 8   lessons_number   25284 non-null  float64\n",
+            " 9   speak            34442 non-null  object \n",
+            " 10  description      34442 non-null  object \n",
+            " 11  link             34442 non-null  object \n",
+            "dtypes: float64(3), int64(2), object(7)\n",
+            "memory usage: 3.2+ MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 718
+        },
+        "id": "UvtmVtYdKwaJ",
+        "outputId": "22348dfe-9a68-4753-8187-5c3946d21b8a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "#Now let's visualize the most common languages for tutors\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "sns.histplot(data=df, x='language', color='red', kde=False)  # 'red' is the color\n",
+        "plt.title('Count of Tutors by Language')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yhe1WlGdhxY8"
+      },
+      "source": [
+        "let's figure out the correlation between price per lesson and amount of lessons the tutor has. Obviously, more lessons a tutor has more more experience and price he|she could gain. However, from the plot below we figured out that this correlation is more stronger for Arabic. And French, for instance, has the weakest correlation. English btw is at the same positions as Chinese.\n",
+        "\n",
+        "sns.scatterplot is used to create a scatter plot with points colored by the 'language' column.\n",
+        "\n",
+        "sns.regplot is then used to add a linear regression trendline to the scatter plot."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 718
+        },
+        "id": "6MkDC-I9hIao",
+        "outputId": "50f63b1b-d7ef-4167-9cdf-12872e7f94b0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 1200x800 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Create the scatter plot with trendline\n",
+        "plt.figure(figsize=(12, 8))\n",
+        "scatter_plot = sns.scatterplot(data=df, x='usd_price', y='lessons_number', hue='language')\n",
+        "sns.regplot(data=df, x='usd_price', y='lessons_number', scatter=False, ax=scatter_plot)\n",
+        "# Set plot title\n",
+        "plt.title('Correlation between amount of lessons and price per lesson by Language')\n",
+        "# Show the plot\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "Qyn5IagDiTrZ",
+        "outputId": "7a6ceb41-68d6-4d6c-9b3c-331843f21519"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
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+              "<body>\n",
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+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "#let us visualize 'Active students amount by Languages'\n",
+        "fig = px.histogram(df, x='language', y='active_students',\n",
+        "                   color_discrete_sequence=['#e377c2'], title='Active students amount by Languages')\n",
+        "fig.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 131,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "z-A37jS9PAyx",
+        "outputId": "3adc3e3b-a462-4b3b-a60d-b3a3068d6fe8"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
+              "    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
+              "        <script charset=\"utf-8\" src=\"https://cdn.plot.ly/plotly-2.24.1.min.js\"></script>                <div id=\"dbd01176-8ba9-404f-b256-3159c0eea23b\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"dbd01176-8ba9-404f-b256-3159c0eea23b\")) {                    Plotly.newPlot(                        \"dbd01176-8ba9-404f-b256-3159c0eea23b\",                        [{\"alignmentgroup\":\"True\",\"bingroup\":\"x\",\"histfunc\":\"sum\",\"hovertemplate\":\"language=%{x}\\u003cbr\\u003esum of 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of usd_price\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Average price for tutor's lesson without any reviews by languages\"},\"barmode\":\"relative\"},                        {\"responsive\": true}                    ).then(function(){\n",
+              "                            \n",
+              "var gd = document.getElementById('dbd01176-8ba9-404f-b256-3159c0eea23b');\n",
+              "var x = new MutationObserver(function (mutations, observer) {{\n",
+              "        var display = window.getComputedStyle(gd).display;\n",
+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
+              "    x.observe(outputEl, {childList: true});\n",
+              "}}\n",
+              "\n",
+              "                        })                };                            </script>        </div>\n",
+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Let us viisualize \"Average price for tutor's lesson without any reviews by languages\" which is nothing but new tutors.\n",
+        "import plotly.express as px\n",
+        "new_tutors_prices = df[df['rating']=='New'][['language','usd_price','lessons_number']].groupby('language').mean().reset_index()\n",
+        "fig = px.histogram(new_tutors_prices, x='language',y='usd_price', color_discrete_sequence=['#e377c2'],\n",
+        "                  title=\"Average price for tutor's lesson without any reviews by languages\")\n",
+        "fig.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EgR4nSMplFf9"
+      },
+      "source": [
+        "The average price for tutor's lesson without any reviews by languages. From this plot we can learn that lesson with 0 reviews in German is twice more expensive that in English."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "M_ysz41elhv2"
+      },
+      "source": [
+        "let's see the correlation between amount of lessons and price that is set by the tutor. In other words - what language tutors are more likely to increase price per lesson. According to the dataset collected, Japanese tutors like to increase the price most of all when they got many students and Arabic tutors, for example, don't usually increase the price for a lesson.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 542
+        },
+        "id": "LNvpkJ99lEBe",
+        "outputId": "f4744543-6c3f-4f25-9639-0f2f94eed872"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<html>\n",
+              "<head><meta charset=\"utf-8\" /></head>\n",
+              "<body>\n",
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+              "var gd = document.getElementById('0fc86926-14f2-4ff6-a53b-c2871bd38560');\n",
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+              "        if (!display || display === 'none') {{\n",
+              "            console.log([gd, 'removed!']);\n",
+              "            Plotly.purge(gd);\n",
+              "            observer.disconnect();\n",
+              "        }}\n",
+              "}});\n",
+              "\n",
+              "// Listen for the removal of the full notebook cells\n",
+              "var notebookContainer = gd.closest('#notebook-container');\n",
+              "if (notebookContainer) {{\n",
+              "    x.observe(notebookContainer, {childList: true});\n",
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+              "\n",
+              "// Listen for the clearing of the current output cell\n",
+              "var outputEl = gd.closest('.output');\n",
+              "if (outputEl) {{\n",
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+              "</body>\n",
+              "</html>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "fig = px.scatter(df, x='lessons_number',y='usd_price', trendline = 'ols', color = 'language',\n",
+        "                title = 'Lesson Price Increasement by Lessons Amount')\n",
+        "fig.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1180
+        },
+        "id": "zX2LKz-ysBOg",
+        "outputId": "95dad2a2-79af-41c3-be43-61baa06d874b"
+      },
+      "outputs": [
+        {
+          "data": {
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+            "text/plain": [
+              "<Figure size 1800x1500 with 9 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "\n",
+        "# Set up the figure and axes\n",
+        "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(18, 15))\n",
+        "\n",
+        "# Plot for 'usd_price'\n",
+        "sns.histplot(df['usd_price'], bins=30, kde=True, color='skyblue', alpha=0.7, ax=axes[0, 0])\n",
+        "axes[0, 0].set_title('Distribution of usd_price')\n",
+        "axes[0, 0].set_xlabel('usd_price')\n",
+        "axes[0, 0].set_ylabel('Count')\n",
+        "\n",
+        "sns.kdeplot(df['usd_price'], color='blue', fill=True, ax=axes[0, 1])\n",
+        "axes[0, 1].set_title('Kernel Density Estimate (KDE) plot of usd_price')\n",
+        "axes[0, 1].set_xlabel('usd_price')\n",
+        "axes[0, 1].set_ylabel('Density')\n",
+        "\n",
+        "sns.boxplot(x=df['usd_price'], color='lightgray', width=0.3, linewidth=2, fliersize=5, ax=axes[0, 2])\n",
+        "axes[0, 2].set_title('Box Plot of usd_price')\n",
+        "axes[0, 2].set_xlabel('usd_price')\n",
+        "axes[0, 2].set_ylabel('Value')\n",
+        "\n",
+        "# Plot for 'rating'\n",
+        "# Convert 'rating' column to numeric data type\n",
+        "dff = pd.DataFrame(df['rating'], columns=['rating'])\n",
+        "dff['rating'] = pd.to_numeric(df['rating'], errors='coerce')\n",
+        "\n",
+        "# Plot for 'rating'\n",
+        "sns.histplot(dff['rating'], bins=30, kde=True, color='skyblue', alpha=0.7, ax=axes[1, 0])\n",
+        "axes[1, 0].set_title('Distribution of rating')\n",
+        "axes[1, 0].set_xlabel('rating')\n",
+        "axes[1, 0].set_ylabel('Count')\n",
+        "\n",
+        "sns.kdeplot(dff['rating'], color='blue', fill=True, ax=axes[1, 1])\n",
+        "axes[1, 1].set_title('Kernel Density Estimate (KDE) plot of rating')\n",
+        "axes[1, 1].set_xlabel('rating')\n",
+        "axes[1, 1].set_ylabel('Density')\n",
+        "\n",
+        "sns.boxplot(x=dff['rating'], color='lightgray', width=0.3, linewidth=2, fliersize=5, ax=axes[1, 2])\n",
+        "axes[1, 2].set_title('Box Plot of rating')\n",
+        "axes[1, 2].set_xlabel('rating')\n",
+        "axes[1, 2].set_ylabel('Value')\n",
+        "\n",
+        "# Plot for 'active_students'\n",
+        "sns.histplot(df['active_students'], bins=30, kde=True, color='skyblue', alpha=0.7, ax=axes[2, 0])\n",
+        "axes[2, 0].set_title('Distribution of active_students')\n",
+        "axes[2, 0].set_xlabel('active_students')\n",
+        "axes[2, 0].set_ylabel('Count')\n",
+        "\n",
+        "sns.kdeplot(df['active_students'], color='blue', fill=True, ax=axes[2, 1])\n",
+        "axes[2, 1].set_title('Kernel Density Estimate (KDE) plot of active_students')\n",
+        "axes[2, 1].set_xlabel('active_students')\n",
+        "axes[2, 1].set_ylabel('Density')\n",
+        "\n",
+        "sns.boxplot(x=df['active_students'], color='lightgray', width=0.3, linewidth=2, fliersize=5, ax=axes[2, 2])\n",
+        "axes[2, 2].set_title('Box Plot of active_students')\n",
+        "axes[2, 2].set_xlabel('active_students')\n",
+        "axes[2, 2].set_ylabel('Value')\n",
+        "\n",
+        "plt.tight_layout()\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "S5fELXmTlgnP"
+      },
+      "source": [
+        "Now Let us prepare a model which is a lesson's price Recommendation System:\n",
+        "\n",
+        "Target Variable: 'usd_variablle'\n",
+        "Create a recommendation system to predict price of lessons for students based on their preferences, tutor availability, language, and other factors..\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1051
+        },
+        "id": "8TxYwWxD1cjh",
+        "outputId": "7e498367-ed3b-4f4c-9d0c-91802053904e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-fb34de2d-f67a-48a0-a13d-d9b721299395\" 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>Unnamed: 0</th>\n",
+              "      <th>name</th>\n",
+              "      <th>rating</th>\n",
+              "      <th>reviews_number</th>\n",
+              "      <th>usd_price</th>\n",
+              "      <th>active_students</th>\n",
+              "      <th>lessons_number</th>\n",
+              "      <th>speak</th>\n",
+              "      <th>description</th>\n",
+              "      <th>link</th>\n",
+              "      <th>badge_Super tutor</th>\n",
+              "      <th>language_Arabic</th>\n",
+              "      <th>language_Chinese</th>\n",
+              "      <th>language_English</th>\n",
+              "      <th>language_French</th>\n",
+              "      <th>language_German</th>\n",
+              "      <th>language_Italian</th>\n",
+              "      <th>language_Japanese</th>\n",
+              "      <th>language_Korean</th>\n",
+              "      <th>language_Spanish</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0</td>\n",
+              "      <td>Mahmoud H.</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>28.0</td>\n",
+              "      <td>25</td>\n",
+              "      <td>8.0</td>\n",
+              "      <td>8382.0</td>\n",
+              "      <td>Arabic (Native), English (Advanced)</td>\n",
+              "      <td>َ📜EJAZA For Quran,Arabic,15 years Experience🎓a...</td>\n",
+              "      <td>https://preply.com/en/tutor/61569</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>1</td>\n",
+              "      <td>Dr. Abdul M.</td>\n",
+              "      <td>5</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>12</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>1384.0</td>\n",
+              "      <td>Arabic (Proficient), English (Proficient)</td>\n",
+              "      <td>Doctorate in Arabic with 5 years of experience...</td>\n",
+              "      <td>https://preply.com/en/tutor/31502</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2</td>\n",
+              "      <td>Atika M.</td>\n",
+              "      <td>4.8</td>\n",
+              "      <td>101.0</td>\n",
+              "      <td>20</td>\n",
+              "      <td>14.0</td>\n",
+              "      <td>4963.0</td>\n",
+              "      <td>Arabic (Native), English (Proficient)</td>\n",
+              "      <td>Certified tutor with 7 years of Online teachin...</td>\n",
+              "      <td>https://preply.com/en/tutor/45713</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>3</td>\n",
+              "      <td>Abdelghafour R.</td>\n",
+              "      <td>4.9</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>18</td>\n",
+              "      <td>4.0</td>\n",
+              "      <td>1337.0</td>\n",
+              "      <td>Arabic (Native), English (Advanced)</td>\n",
+              "      <td>Certified Tutor of Arabic and French ,  native...</td>\n",
+              "      <td>https://preply.com/en/tutor/21749</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>4</td>\n",
+              "      <td>Muhammad M.</td>\n",
+              "      <td>5</td>\n",
+              "      <td>38.0</td>\n",
+              "      <td>18</td>\n",
+              "      <td>17.0</td>\n",
+              "      <td>3039.0</td>\n",
+              "      <td>Arabic (Native), English (Advanced)</td>\n",
+              "      <td>Certified tutor with 8 years of experience tea...</td>\n",
+              "      <td>https://preply.com/en/tutor/39825</td>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34437</th>\n",
+              "      <td>6093</td>\n",
+              "      <td>Fernanda R.</td>\n",
+              "      <td>5</td>\n",
+              "      <td>9.0</td>\n",
+              "      <td>26</td>\n",
+              "      <td>10.0</td>\n",
+              "      <td>1139.0</td>\n",
+              "      <td>Spanish (Native), English (Upper-Intermediate)</td>\n",
+              "      <td>Enjoy and Learn Spanish with a Latina with 7 y...</td>\n",
+              "      <td>https://preply.com/en/tutor/345142</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34438</th>\n",
+              "      <td>6094</td>\n",
+              "      <td>Claudia S.</td>\n",
+              "      <td>5</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>19</td>\n",
+              "      <td>3.0</td>\n",
+              "      <td>103.0</td>\n",
+              "      <td>Spanish (Native), English (Upper-Intermediate)</td>\n",
+              "      <td>Guatemalan Spanish teacher with over 30 years ...</td>\n",
+              "      <td>https://preply.com/en/tutor/1157203</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34439</th>\n",
+              "      <td>6095</td>\n",
+              "      <td>Leidy carolina A.</td>\n",
+              "      <td>New</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>6</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Spanish (Native)</td>\n",
+              "      <td>Dare and learn Spanish with me in dynamic and ...</td>\n",
+              "      <td>/es/profesor/3544272</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34440</th>\n",
+              "      <td>6096</td>\n",
+              "      <td>María Natalia G.</td>\n",
+              "      <td>5</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>6</td>\n",
+              "      <td>2.0</td>\n",
+              "      <td>24.0</td>\n",
+              "      <td>Spanish (Native), English (Intermediate)</td>\n",
+              "      <td>¡Únete! Aprende español con una nativa de Colo...</td>\n",
+              "      <td>/es/profesor/4138301</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>34441</th>\n",
+              "      <td>6097</td>\n",
+              "      <td>Eduardo C.</td>\n",
+              "      <td>New</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>6</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>1.0</td>\n",
+              "      <td>Spanish (Native), English (Upper-Intermediate)</td>\n",
+              "      <td>Quickly and easily learn Spanish, from beginne...</td>\n",
+              "      <td>https://preply.com/en/tutor/4194826</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>34442 rows × 20 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-fb34de2d-f67a-48a0-a13d-d9b721299395')\"\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-fb34de2d-f67a-48a0-a13d-d9b721299395 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-fb34de2d-f67a-48a0-a13d-d9b721299395');\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-bbaaf884-6c78-40ed-b4e9-29b2dbe7a027\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bbaaf884-6c78-40ed-b4e9-29b2dbe7a027')\"\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-bbaaf884-6c78-40ed-b4e9-29b2dbe7a027 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_c2ddf6ed-f3ef-49b1-9d01-874316cb4ef1\">\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('df_encoded')\"\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_c2ddf6ed-f3ef-49b1-9d01-874316cb4ef1 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('df_encoded');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "       Unnamed: 0               name rating  reviews_number  usd_price  \\\n",
+              "0               0         Mahmoud H.    4.8            28.0         25   \n",
+              "1               1       Dr. Abdul M.      5            10.0         12   \n",
+              "2               2           Atika M.    4.8           101.0         20   \n",
+              "3               3    Abdelghafour R.    4.9            19.0         18   \n",
+              "4               4        Muhammad M.      5            38.0         18   \n",
+              "...           ...                ...    ...             ...        ...   \n",
+              "34437        6093        Fernanda R.      5             9.0         26   \n",
+              "34438        6094         Claudia S.      5             2.0         19   \n",
+              "34439        6095  Leidy carolina A.    New             NaN          6   \n",
+              "34440        6096   María Natalia G.      5             1.0          6   \n",
+              "34441        6097         Eduardo C.    New             NaN          6   \n",
+              "\n",
+              "       active_students  lessons_number  \\\n",
+              "0                  8.0          8382.0   \n",
+              "1                 24.0          1384.0   \n",
+              "2                 14.0          4963.0   \n",
+              "3                  4.0          1337.0   \n",
+              "4                 17.0          3039.0   \n",
+              "...                ...             ...   \n",
+              "34437             10.0          1139.0   \n",
+              "34438              3.0           103.0   \n",
+              "34439              NaN             NaN   \n",
+              "34440              2.0            24.0   \n",
+              "34441              1.0             1.0   \n",
+              "\n",
+              "                                                speak  \\\n",
+              "0                 Arabic (Native), English (Advanced)   \n",
+              "1           Arabic (Proficient), English (Proficient)   \n",
+              "2               Arabic (Native), English (Proficient)   \n",
+              "3                 Arabic (Native), English (Advanced)   \n",
+              "4                 Arabic (Native), English (Advanced)   \n",
+              "...                                               ...   \n",
+              "34437  Spanish (Native), English (Upper-Intermediate)   \n",
+              "34438  Spanish (Native), English (Upper-Intermediate)   \n",
+              "34439                                Spanish (Native)   \n",
+              "34440        Spanish (Native), English (Intermediate)   \n",
+              "34441  Spanish (Native), English (Upper-Intermediate)   \n",
+              "\n",
+              "                                             description  \\\n",
+              "0      َ📜EJAZA For Quran,Arabic,15 years Experience🎓a...   \n",
+              "1      Doctorate in Arabic with 5 years of experience...   \n",
+              "2      Certified tutor with 7 years of Online teachin...   \n",
+              "3      Certified Tutor of Arabic and French ,  native...   \n",
+              "4      Certified tutor with 8 years of experience tea...   \n",
+              "...                                                  ...   \n",
+              "34437  Enjoy and Learn Spanish with a Latina with 7 y...   \n",
+              "34438  Guatemalan Spanish teacher with over 30 years ...   \n",
+              "34439  Dare and learn Spanish with me in dynamic and ...   \n",
+              "34440  ¡Únete! Aprende español con una nativa de Colo...   \n",
+              "34441  Quickly and easily learn Spanish, from beginne...   \n",
+              "\n",
+              "                                      link  badge_Super tutor  \\\n",
+              "0        https://preply.com/en/tutor/61569                  0   \n",
+              "1        https://preply.com/en/tutor/31502                  0   \n",
+              "2        https://preply.com/en/tutor/45713                  1   \n",
+              "3        https://preply.com/en/tutor/21749                  0   \n",
+              "4        https://preply.com/en/tutor/39825                  1   \n",
+              "...                                    ...                ...   \n",
+              "34437   https://preply.com/en/tutor/345142                  1   \n",
+              "34438  https://preply.com/en/tutor/1157203                  0   \n",
+              "34439                 /es/profesor/3544272                  0   \n",
+              "34440                 /es/profesor/4138301                  0   \n",
+              "34441  https://preply.com/en/tutor/4194826                  0   \n",
+              "\n",
+              "       language_Arabic  language_Chinese  language_English  language_French  \\\n",
+              "0                    1                 0                 0                0   \n",
+              "1                    1                 0                 0                0   \n",
+              "2                    1                 0                 0                0   \n",
+              "3                    1                 0                 0                0   \n",
+              "4                    1                 0                 0                0   \n",
+              "...                ...               ...               ...              ...   \n",
+              "34437                0                 0                 0                0   \n",
+              "34438                0                 0                 0                0   \n",
+              "34439                0                 0                 0                0   \n",
+              "34440                0                 0                 0                0   \n",
+              "34441                0                 0                 0                0   \n",
+              "\n",
+              "       language_German  language_Italian  language_Japanese  language_Korean  \\\n",
+              "0                    0                 0                  0                0   \n",
+              "1                    0                 0                  0                0   \n",
+              "2                    0                 0                  0                0   \n",
+              "3                    0                 0                  0                0   \n",
+              "4                    0                 0                  0                0   \n",
+              "...                ...               ...                ...              ...   \n",
+              "34437                0                 0                  0                0   \n",
+              "34438                0                 0                  0                0   \n",
+              "34439                0                 0                  0                0   \n",
+              "34440                0                 0                  0                0   \n",
+              "34441                0                 0                  0                0   \n",
+              "\n",
+              "       language_Spanish  \n",
+              "0                     0  \n",
+              "1                     0  \n",
+              "2                     0  \n",
+              "3                     0  \n",
+              "4                     0  \n",
+              "...                 ...  \n",
+              "34437                 1  \n",
+              "34438                 1  \n",
+              "34439                 1  \n",
+              "34440                 1  \n",
+              "34441                 1  \n",
+              "\n",
+              "[34442 rows x 20 columns]"
+            ]
+          },
+          "execution_count": 106,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "#one-hot encode categorical variables in the DataFrame df with the columns 'badge' and 'language'. It transforms categorical variables into binary vectors.\n",
+        "df_encoded = pd.get_dummies(df, columns=['badge', 'language'])\n",
+        "df_encoded"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "RfI5VTFT1qQW",
+        "outputId": "e83835e7-6012-4fcf-a391-fbb134bc5795"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 34442 entries, 0 to 34441\n",
+            "Data columns (total 20 columns):\n",
+            " #   Column             Non-Null Count  Dtype  \n",
+            "---  ------             --------------  -----  \n",
+            " 0   Unnamed: 0         34442 non-null  int64  \n",
+            " 1   name               34442 non-null  object \n",
+            " 2   rating             34442 non-null  object \n",
+            " 3   reviews_number     24078 non-null  float64\n",
+            " 4   usd_price          34442 non-null  int64  \n",
+            " 5   active_students    28899 non-null  float64\n",
+            " 6   lessons_number     25284 non-null  float64\n",
+            " 7   speak              34442 non-null  object \n",
+            " 8   description        34442 non-null  object \n",
+            " 9   link               34442 non-null  object \n",
+            " 10  badge_Super tutor  34442 non-null  uint8  \n",
+            " 11  language_Arabic    34442 non-null  uint8  \n",
+            " 12  language_Chinese   34442 non-null  uint8  \n",
+            " 13  language_English   34442 non-null  uint8  \n",
+            " 14  language_French    34442 non-null  uint8  \n",
+            " 15  language_German    34442 non-null  uint8  \n",
+            " 16  language_Italian   34442 non-null  uint8  \n",
+            " 17  language_Japanese  34442 non-null  uint8  \n",
+            " 18  language_Korean    34442 non-null  uint8  \n",
+            " 19  language_Spanish   34442 non-null  uint8  \n",
+            "dtypes: float64(3), int64(2), object(5), uint8(10)\n",
+            "memory usage: 3.0+ MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "df_encoded.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KYI-kr462JBd",
+        "outputId": "2f65502b-b961-47de-f665-795f3fbf5a41"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "20242"
+            ]
+          },
+          "execution_count": 108,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "num_unique_names = df_encoded['name'].nunique()\n",
+        "num_unique_names"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t3GVg_ArkaNC"
+      },
+      "source": [
+        "# **RANDOM FOREST REGRESSION MODEL**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UthC48krlzUo",
+        "outputId": "a7f2cc33-1e48-4bc0-ff83-f8ad4d1260bc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 34442 entries, 0 to 34441\n",
+            "Data columns (total 16 columns):\n",
+            " #   Column             Non-Null Count  Dtype  \n",
+            "---  ------             --------------  -----  \n",
+            " 0   rating             34442 non-null  float64\n",
+            " 1   reviews_number     34442 non-null  float64\n",
+            " 2   active_students    34442 non-null  float64\n",
+            " 3   lessons_number     34442 non-null  float64\n",
+            " 4   speak              34442 non-null  object \n",
+            " 5   description        34442 non-null  object \n",
+            " 6   badge_Super tutor  34442 non-null  uint8  \n",
+            " 7   language_Arabic    34442 non-null  uint8  \n",
+            " 8   language_Chinese   34442 non-null  uint8  \n",
+            " 9   language_English   34442 non-null  uint8  \n",
+            " 10  language_French    34442 non-null  uint8  \n",
+            " 11  language_German    34442 non-null  uint8  \n",
+            " 12  language_Italian   34442 non-null  uint8  \n",
+            " 13  language_Japanese  34442 non-null  uint8  \n",
+            " 14  language_Korean    34442 non-null  uint8  \n",
+            " 15  language_Spanish   34442 non-null  uint8  \n",
+            "dtypes: float64(4), object(2), uint8(10)\n",
+            "memory usage: 1.9+ MB\n",
+            "None\n",
+            "Mean Absolute Error: 4.4532336719883885\n",
+            "Mean Squared Error: 57.90316258345428\n",
+            "R-squared: 0.5190588719264813\n"
+          ]
+        }
+      ],
+      "source": [
+        "#let's drop unnecessary columns\n",
+        "df_encoded.drop(columns=['Unnamed: 0', 'name', 'link'], inplace=True)\n",
+        "numerical_cols = ['rating','active_students','reviews_number',  'lessons_number']\n",
+        "df_encoded[numerical_cols] = df_encoded[numerical_cols].replace('New', np.nan)\n",
+        "# Convert numerical columns to float and replace NaN with zero\n",
+        "df_encoded[numerical_cols] = df_encoded[numerical_cols].astype(float).fillna(0)\n",
+        "# Handle missing values\n",
+        "numerical_cols = ['reviews_number', 'active_students', 'lessons_number']\n",
+        "categorical_cols = ['speak','description']\n",
+        "\n",
+        "# Fill missing numerical values with mean\n",
+        "numerical_transformer = SimpleImputer(strategy='mean')\n",
+        "\n",
+        "# Fill missing categorical values with mode and encode categorical columns\n",
+        "categorical_transformer = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
+        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
+        "])\n",
+        "\n",
+        "preprocessor = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        ('num', numerical_transformer, numerical_cols),\n",
+        "        ('cat', categorical_transformer, categorical_cols)\n",
+        "    ])\n",
+        "\n",
+        " # Define features (X) and target variable (y)\n",
+        "X = df_encoded.drop(columns=['usd_price'])\n",
+        "y = df_encoded['usd_price']\n",
+        "print(X.info())\n",
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
+        "\n",
+        "# Choose an algorithm and define the model\n",
+        "#first let us choose randam forst algorithm\n",
+        "model = RandomForestRegressor(n_estimators=100, random_state=42)\n",
+        "\n",
+        "# Define the pipeline\n",
+        "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
+        "                           ('model', model)])\n",
+        "\n",
+        "# Fit the model\n",
+        "pipeline.fit(X_train, y_train)\n",
+        "\n",
+        "# Make predictions\n",
+        "y_pred = pipeline.predict(X_test)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "mse_test = mean_squared_error(y_test, y_pred)\n",
+        "r2_test = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_test)\n",
+        "print(\"R-squared:\", r2_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "id": "Yk-A-AaujjYE",
+        "outputId": "e8052870-1fdf-4f3e-b3fd-48d76e6406e0"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b3d7efac-a581-4fc5-a132-47fabce27fde\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "        vertical-align: top;\n",
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+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
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+              "      <th>Model</th>\n",
+              "      <th>MSE_train</th>\n",
+              "      <th>R2_train</th>\n",
+              "      <th>MSE_test</th>\n",
+              "      <th>R2_test</th>\n",
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+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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+              "                                                    [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",
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+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_7a4dd4d8-b88c-4a90-8ba1-c871db054f79 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"
+            ],
+            "text/plain": [
+              "Empty DataFrame\n",
+              "Columns: [Model, MSE_train, R2_train, MSE_test, R2_test]\n",
+              "Index: []"
+            ]
+          },
+          "execution_count": 110,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "results = pd.DataFrame(columns=['Model', 'MSE_train', 'R2_train', 'MSE_test', 'R2_test'])\n",
+        "results"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Z9foxAj_YNrb",
+        "outputId": "cc3f2992-38b0-41d8-e4e9-e518bd1dff1f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 1.5793492918669547\n",
+            "Mean Squared Error: 7.032903684227506\n",
+            "R-squared: 0.9362747886519706\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_train_pred = pipeline.predict(X_train)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_train, y_train_pred)\n",
+        "mse_train = mean_squared_error(y_train, y_train_pred)\n",
+        "r2_train = r2_score(y_train, y_train_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_train)\n",
+        "print(\"R-squared:\", r2_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 89
+        },
+        "id": "SnMQGWgxjytQ",
+        "outputId": "9cefb108-c106-463b-ac1b-efbefa23c854"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-15656b56-3fda-4edc-a3ce-9f885fc004ce\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\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>Model</th>\n",
+              "      <th>MSE_train</th>\n",
+              "      <th>R2_train</th>\n",
+              "      <th>MSE_test</th>\n",
+              "      <th>R2_test</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Random Forest Regression</td>\n",
+              "      <td>7.032904</td>\n",
+              "      <td>0.936275</td>\n",
+              "      <td>57.903163</td>\n",
+              "      <td>0.519059</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-15656b56-3fda-4edc-a3ce-9f885fc004ce')\"\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",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-15656b56-3fda-4edc-a3ce-9f885fc004ce 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-15656b56-3fda-4edc-a3ce-9f885fc004ce');\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_d00519da-b57d-489d-a110-0de85596cb6b\">\n",
+              "    <style>\n",
+              "      .colab-df-generate {\n",
+              "        background-color: #E8F0FE;\n",
+              "        border: none;\n",
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+              "        cursor: pointer;\n",
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+              "        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",
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+              "  </svg>\n",
+              "    </button>\n",
+              "    <script>\n",
+              "      (() => {\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#id_d00519da-b57d-489d-a110-0de85596cb6b 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"
+            ],
+            "text/plain": [
+              "                      Model  MSE_train  R2_train   MSE_test   R2_test\n",
+              "0  Random Forest Regression   7.032904  0.936275  57.903163  0.519059"
+            ]
+          },
+          "execution_count": 112,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "results.loc[len(results)] = ['Random Forest Regression', mse_train, r2_train, mse_test, r2_test]\n",
+        "results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uN_VpX9bktRr"
+      },
+      "source": [
+        "# **NEURAL NETWORK MODEL**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rFbnlfIH1Set",
+        "outputId": "c190588a-a5a1-4990-aac1-503502a90f0b"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Neural Network Model Metrics:\n",
+            "Mean Absolute Error: 5.904655871189666\n",
+            "Mean Squared Error: 79.96729595710991\n",
+            "R-squared: 0.3357951481290745\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Define the MLPRegressor model\n",
+        "model_nn = MLPRegressor(hidden_layer_sizes=(64, 32), activation='relu', solver='adam', random_state=42)\n",
+        "\n",
+        "# Define the pipeline\n",
+        "pipeline_nn = Pipeline(steps=[('preprocessor', preprocessor),\n",
+        "                               ('model', model_nn)])\n",
+        "\n",
+        "# Fit the model\n",
+        "pipeline_nn.fit(X_train, y_train)\n",
+        "\n",
+        "# Make predictions\n",
+        "y_pred_nn = pipeline_nn.predict(X_test)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae_nn = mean_absolute_error(y_test, y_pred_nn)\n",
+        "mse_test_nn = mean_squared_error(y_test, y_pred_nn)\n",
+        "r2_test_nn = r2_score(y_test, y_pred_nn)\n",
+        "\n",
+        "print(\"Neural Network Model Metrics:\")\n",
+        "print(\"Mean Absolute Error:\", mae_nn)\n",
+        "print(\"Mean Squared Error:\", mse_test_nn)\n",
+        "print(\"R-squared:\", r2_test_nn)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MMR_DM484SF4",
+        "outputId": "ff6338e5-51ad-495d-81be-c836314cd428"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 3.420976583848025\n",
+            "Mean Squared Error: 34.24956334942531\n",
+            "R-squared: 0.6896643604099656\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_train_pred = pipeline_nn.predict(X_train)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_train, y_train_pred)\n",
+        "mse_train = mean_squared_error(y_train, y_train_pred)\n",
+        "r2_train = r2_score(y_train, y_train_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_train)\n",
+        "print(\"R-squared:\", r2_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-k8QL_CI4SV7"
+      },
+      "outputs": [],
+      "source": [
+        "results.loc[len(results)] = ['Neural Network', mse_train, r2_train, mse_test_nn, r2_test_nn]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ib2-NlGBneiR"
+      },
+      "source": [
+        "# **Decision Tree Algorithm**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8dU8nvuosfE3",
+        "outputId": "5ae7b41d-95f1-4035-a2cb-8ef275667c17"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 3.7477503628447026\n",
+            "Mean Squared Error: 61.377939042089984\n",
+            "R-squared: 0.49019753110747233\n"
+          ]
+        }
+      ],
+      "source": [
+        "#let us choose Decision Tree algorithm\n",
+        "df_encoded = pd.get_dummies(df, columns=['badge', 'language'])\n",
+        "df_encoded\n",
+        "\n",
+        "df_encoded.drop(columns=['Unnamed: 0', 'name', 'link'], inplace=True)\n",
+        "\n",
+        "numerical_cols = ['rating','active_students','reviews_number',  'lessons_number']\n",
+        "df_encoded[numerical_cols] = df_encoded[numerical_cols].replace('New', np.nan)\n",
+        "\n",
+        "# Convert numerical columns to float and replace NaN with zero\n",
+        "df_encoded[numerical_cols] = df_encoded[numerical_cols].astype(float).fillna(0)\n",
+        "# Handle missing values\n",
+        "numerical_cols = ['reviews_number', 'active_students', 'lessons_number']\n",
+        "categorical_cols = ['speak','description']\n",
+        "\n",
+        "# Fill missing numerical values with mean\n",
+        "numerical_transformer = SimpleImputer(strategy='mean')\n",
+        "\n",
+        "# Fill missing categorical values with mode and encode categorical columns\n",
+        "categorical_transformer = Pipeline(steps=[\n",
+        "    ('imputer', SimpleImputer(strategy='most_frequent')),\n",
+        "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
+        "])\n",
+        "\n",
+        "preprocessor = ColumnTransformer(\n",
+        "    transformers=[\n",
+        "        ('num', numerical_transformer, numerical_cols),\n",
+        "        ('cat', categorical_transformer, categorical_cols)\n",
+        "    ])\n",
+        "\n",
+        "# Define features (X) and target variable (y)\n",
+        "X = df_encoded.drop(columns=['usd_price'])\n",
+        "y = df_encoded['usd_price']\n",
+        "\n",
+        "# Split the data into training and testing sets\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)\n",
+        "model = DecisionTreeRegressor(random_state=42 )\n",
+        "\n",
+        "# Define the pipeline\n",
+        "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
+        "                           ('model', model)])\n",
+        "\n",
+        "# Fit the model\n",
+        "pipeline.fit(X_train, y_train)\n",
+        "\n",
+        "# Make predictions\n",
+        "y_pred = pipeline.predict(X_test)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "mse_test = mean_squared_error(y_test, y_pred)\n",
+        "r2_test = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_test)\n",
+        "print(\"R-squared:\", r2_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3REYffkYn06N",
+        "outputId": "6a0b2dbc-82d8-4ccc-a8e5-283ce8159af8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 0.0\n",
+            "Mean Squared Error: 0.0\n",
+            "R-squared: 1.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_train_pred = pipeline.predict(X_train)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_train, y_train_pred)\n",
+        "mse_train = mean_squared_error(y_train, y_train_pred)\n",
+        "r2_train = r2_score(y_train, y_train_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_train)\n",
+        "print(\"R-squared:\", r2_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4zN-s-gGn17m"
+      },
+      "outputs": [],
+      "source": [
+        "results.loc[len(results)] = ['Decision Tree', mse_train, r2_train, mse_test, r2_test]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-jAIBGVUn-hZ"
+      },
+      "source": [
+        "# **Linear Regression model**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KY5LHXApv_tA",
+        "outputId": "1df0f438-77ae-419a-bf19-f341d2026f9c"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 5.119112915620734\n",
+            "Mean Squared Error: 60.72380606489449\n",
+            "R-squared: 0.4956307309177429\n"
+          ]
+        }
+      ],
+      "source": [
+        "#using Linear Regression model\n",
+        "model = LinearRegression()\n",
+        "\n",
+        "# Define the pipeline\n",
+        "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
+        "                           ('model', model)])\n",
+        "\n",
+        "# Fit the model\n",
+        "pipeline.fit(X_train, y_train)\n",
+        "\n",
+        "# Make predictions\n",
+        "y_pred = pipeline.predict(X_test)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "mse_test = mean_squared_error(y_test, y_pred)\n",
+        "r2_test = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_test)\n",
+        "print(\"R-squared:\", r2_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XtYXhI3IGGmb",
+        "outputId": "5c16b59b-96b1-4f01-a838-124cf47852fb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 2.693502957175476\n",
+            "Mean Squared Error: 17.09859069417602\n",
+            "R-squared: 0.8450694969442767\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_train_pred = pipeline.predict(X_train)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_train, y_train_pred)\n",
+        "mse_train = mean_squared_error(y_train, y_train_pred)\n",
+        "r2_train = r2_score(y_train, y_train_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_train)\n",
+        "print(\"R-squared:\", r2_train)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iMbcxdXSoF-V"
+      },
+      "outputs": [],
+      "source": [
+        "results.loc[len(results)] = ['Linear Regression', mse_train, r2_train, mse_test, r2_test]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ci8NN5iSo6z7"
+      },
+      "source": [
+        "# **Ridge Regression Model**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Rxn01yEXpAft",
+        "outputId": "0baa918b-2944-4a31-984a-b876362d21cf"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 7.020803503452444\n",
+            "Mean Squared Error: 96.16255806101648\n",
+            "R-squared: 0.20127801161734926\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = Ridge()\n",
+        "\n",
+        "# Define the pipeline\n",
+        "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
+        "                           ('model', model)])\n",
+        "\n",
+        "# Fit the model\n",
+        "pipeline.fit(X_train, y_train)\n",
+        "\n",
+        "# Make predictions\n",
+        "y_pred = pipeline.predict(X_test)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "mse_test = mean_squared_error(y_test, y_pred)\n",
+        "r2_test = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_test)\n",
+        "print(\"R-squared:\", r2_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "oL13JClKpPnf",
+        "outputId": "88a7b0aa-9f10-49f9-d999-7362c0168760"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 6.675591927389942\n",
+            "Mean Squared Error: 85.65546411761642\n",
+            "R-squared: 0.22387497410918766\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_train_pred = pipeline.predict(X_train)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_train, y_train_pred)\n",
+        "mse_train = mean_squared_error(y_train, y_train_pred)\n",
+        "r2_train = r2_score(y_train, y_train_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_train)\n",
+        "print(\"R-squared:\", r2_train)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DZZ5VjYIpTOx"
+      },
+      "outputs": [],
+      "source": [
+        "results.loc[len(results)] = ['Ridge Regression', mse_train, r2_train, mse_test, r2_test]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pmosZt9fpUSm"
+      },
+      "source": [
+        "# **Elastic Net Regressor Model**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fbZrPUNDpiPG",
+        "outputId": "e9ae4d0c-06a2-4392-982e-8495b97a550d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 7.877558750602151\n",
+            "Mean Squared Error: 114.71983489354389\n",
+            "R-squared: 0.04714208439670742\n"
+          ]
+        }
+      ],
+      "source": [
+        "model = ElasticNet()\n",
+        "# Define the pipeline\n",
+        "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
+        "                           ('model', model)])\n",
+        "# Fit the model\n",
+        "pipeline.fit(X_train, y_train)\n",
+        "# Make predictions\n",
+        "y_pred = pipeline.predict(X_test)\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "mse_test = mean_squared_error(y_test, y_pred)\n",
+        "r2_test = r2_score(y_test, y_pred)\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_test)\n",
+        "print(\"R-squared:\", r2_test)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "hIDN-e0zphUC",
+        "outputId": "f4cbc25f-917c-481d-cb98-86c25f4203fa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mean Absolute Error: 7.605167008182322\n",
+            "Mean Squared Error: 105.04455219195948\n",
+            "R-squared: 0.0481902511470268\n"
+          ]
+        }
+      ],
+      "source": [
+        "y_train_pred = pipeline.predict(X_train)\n",
+        "\n",
+        "# Evaluate the model\n",
+        "mae = mean_absolute_error(y_train, y_train_pred)\n",
+        "mse_train = mean_squared_error(y_train, y_train_pred)\n",
+        "r2_train = r2_score(y_train, y_train_pred)\n",
+        "\n",
+        "print(\"Mean Absolute Error:\", mae)\n",
+        "print(\"Mean Squared Error:\", mse_train)\n",
+        "print(\"R-squared:\", r2_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6VtTt4aSpdkH"
+      },
+      "outputs": [],
+      "source": [
+        "results.loc[len(results)] = ['Elastic Net Regressor', mse_train, r2_train, mse_test, r2_test]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3T_-Svl9qCTB"
+      },
+      "source": [
+        "# **Hyper Parameter Tuning**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "mmDoYjWWI8ko",
+        "outputId": "6de53b9d-3594-4883-e0dd-da8dc0e06b30"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Best Model Parameters: {'model__max_depth': None, 'model__min_samples_leaf': 1, 'model__min_samples_split': 10}\n",
+            "Mean Absolute Error (Best Model): 4.104776879305182\n",
+            "Mean Squared Error (Best Model): 60.79540401729149\n",
+            "R-squared (Best Model): 0.4950360414663\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Let's do hyper parameter tuning and all ti decision tree regression model, because i have tried with random forest regresoor but it is taking more than an hour to executr.\n",
+        "from sklearn.model_selection import GridSearchCV\n",
+        "# Define the hyperparameters grid for Decision Tree Regressor\n",
+        "param_grid = {\n",
+        "    'model__max_depth': [None, 10, 20, 30],\n",
+        "    'model__min_samples_split': [2, 5, 10],\n",
+        "    'model__min_samples_leaf': [1, 2, 4]\n",
+        "}\n",
+        "\n",
+        "# Create the DecisionTreeRegressor model\n",
+        "model = DecisionTreeRegressor(random_state=42)\n",
+        "\n",
+        "# Define the pipeline\n",
+        "pipeline = Pipeline(steps=[('preprocessor', preprocessor),\n",
+        "                           ('model', model)])\n",
+        "\n",
+        "# Perform GridSearchCV with cross-validation\n",
+        "grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='r2')\n",
+        "grid_search.fit(X_train, y_train)\n",
+        "\n",
+        "# Get the best model\n",
+        "best_model = grid_search.best_estimator_\n",
+        "\n",
+        "# Make predictions with the best model\n",
+        "y_pred = best_model.predict(X_test)\n",
+        "\n",
+        "# Evaluate the best model\n",
+        "mae = mean_absolute_error(y_test, y_pred)\n",
+        "mse = mean_squared_error(y_test, y_pred)\n",
+        "r2 = r2_score(y_test, y_pred)\n",
+        "\n",
+        "print(\"Best Model Parameters:\", grid_search.best_params_)\n",
+        "print(\"Mean Absolute Error (Best Model):\", mae)\n",
+        "print(\"Mean Squared Error (Best Model):\", mse)\n",
+        "print(\"R-squared (Best Model):\", r2)\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true
+        },
+        "id": "eDtVokR8r8of",
+        "outputId": "2dde21bc-c150-414f-a113-2e5ee59d7f77"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "  <div id=\"df-e961527a-a6e6-4418-9e58-7dbe148547c2\" 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>MSE_train</th>\n",
+              "      <th>R2_train</th>\n",
+              "      <th>MSE_test</th>\n",
+              "      <th>R2_test</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Random Forest Regression</td>\n",
+              "      <td>7.032904</td>\n",
+              "      <td>0.936275</td>\n",
+              "      <td>57.903163</td>\n",
+              "      <td>0.519059</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Linear Regression</td>\n",
+              "      <td>17.098591</td>\n",
+              "      <td>0.845069</td>\n",
+              "      <td>60.723806</td>\n",
+              "      <td>0.495631</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Decision Tree</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>61.377939</td>\n",
+              "      <td>0.490198</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Neural Network</td>\n",
+              "      <td>34.249563</td>\n",
+              "      <td>0.689664</td>\n",
+              "      <td>79.967296</td>\n",
+              "      <td>0.335795</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Ridge Regression</td>\n",
+              "      <td>85.655464</td>\n",
+              "      <td>0.223875</td>\n",
+              "      <td>96.162558</td>\n",
+              "      <td>0.201278</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>Elastic Net Regressor</td>\n",
+              "      <td>105.044552</td>\n",
+              "      <td>0.048190</td>\n",
+              "      <td>114.719835</td>\n",
+              "      <td>0.047142</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-e961527a-a6e6-4418-9e58-7dbe148547c2')\"\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",
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+              "\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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+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-e961527a-a6e6-4418-9e58-7dbe148547c2 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-e961527a-a6e6-4418-9e58-7dbe148547c2');\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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+              "        element.appendChild(docLink);\n",
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+              "\n",
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+              "</svg>\n",
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+              "<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",
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+              "\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",
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+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
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+              "\n",
+              "  .colab-df-spinner {\n",
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+              "    border-bottom-color: var(--fill-color);\n",
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+              "\n",
+              "  @keyframes spin {\n",
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+              "      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",
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+              "    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",
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+              "      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-acf41eec-bb03-41ed-bbdc-dd4e3c8c3a97 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "  <div id=\"id_526b9bd8-7e29-44ea-991b-2313d5387d2b\">\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_526b9bd8-7e29-44ea-991b-2313d5387d2b 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"
+            ],
+            "text/plain": [
+              "                      Model   MSE_train  R2_train    MSE_test   R2_test\n",
+              "0  Random Forest Regression    7.032904  0.936275   57.903163  0.519059\n",
+              "3         Linear Regression   17.098591  0.845069   60.723806  0.495631\n",
+              "2             Decision Tree    0.000000  1.000000   61.377939  0.490198\n",
+              "1            Neural Network   34.249563  0.689664   79.967296  0.335795\n",
+              "4          Ridge Regression   85.655464  0.223875   96.162558  0.201278\n",
+              "5     Elastic Net Regressor  105.044552  0.048190  114.719835  0.047142"
+            ]
+          },
+          "execution_count": 129,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "results = results.sort_values(by='MSE_test', ascending=True)\n",
+        "results"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0Wxri-EdVIw1"
+      },
+      "source": [
+        "So I have tried with different Regression Algorithms and got better fitting Model for Random Forest Regression Model With R- squared value \"0.51905\""
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/Top Foreign Languages Analysis/README.md b/Top Foreign Languages Analysis/README.md
new file mode 100644
index 000000000..947cc87d9
--- /dev/null
+++ b/Top Foreign Languages Analysis/README.md	
@@ -0,0 +1,103 @@
+<h1>Top Foreign Language Analysis</h1>
+
+**GOAL**
+
+To build a machine learning model for predicting the usd_price per lesson for a given language and other feature.
+
+**DATASET**
+
+[https://www.kaggle.com/datasets/timmofeyy/top-foreign-languages-preply-tutors]
+
+**DESCRIPTION**
+
+To analyze the dataset of Top Foreign Language and build and train the model on the basis of different features and variables.
+
+The data includes the main languages and the most popular among students. The datasets have 8 csv files for 8 different top foreign languages.
+
+- **columns_description**: Each CSV File contains the description of all the features.
+
+  name: The name of the tutor.
+
+  badge: Any badge or certification associated with the tutor.
+
+  rating: The overall rating of the tutor.
+
+  reviews_number: The number of reviews the tutor has received.
+
+  usd_price: The price charged by the tutor for their services.
+
+  language: The languages spoken by the tutor.
+
+  active_students: The number of active students the tutor is currently teaching.
+
+  lessons_number: The total number of lessons conducted by the tutor.
+
+  speak: The languages spoken by the tutor.
+
+  description: A brief description or snippet provided by the tutor.
+
+  link: The link or URL to the tutor's profile.
+
+
+### Visualization and EDA of different attributes:
+
+<img alt="correlation" src="./Images/correlation between amount of lessons and price per lesson.png">
+
+<img alt="graph" src="./Images/count of tutors.png">
+
+<img alt="graph" src="./Images/distribution plot.png">
+
+
+
+
+**MODELS USED**
+
+| Model                     | MSE_train | R2_train | MSE_test  | R2_test   |
+|---------------------------|-----------|----------|-----------|-----------|
+| Random Forest Regression  | 7.03      | 0.93     | 57.90     | 0.51      |
+| Linear Regression         | 17.09     | 0.84     | 60.72     | 0.49      |
+| Ridge Regression          | 85.65     | 0.22     | 96.16     | 0.20      |
+| Elastic Net Regression    | 105.0     | 0.04     | 114.7     | 0.047     |
+| Decision Tree Regression  | 0.00      | 1.00     | 61.30     | 0.49      |
+| Deep NN                   | 34.29     | 0.04     | 114.7     | 0.0471    |
+
+
+**WHAT I HAD DONE**
+
+* Load the dataset which is in zip file format unzipped it and than concatenated all 8 CSV files.
+* After Concatenation it contains 34442 entries in it and having 47 columns in it.
+* Checked for missing values and cleaned the data accordingly.
+* Analyzed the data, found insights and visualized them accordingly.
+* Plotting heatmap using correlation and checking the relation between different features.
+* 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. XGBoost
+6. Tensorflow
+7. Keras
+8. Sci-py
+9. Seaborn
+10. missingno
+11. plotly
+
+
+**CONCLUSION**
+
+- Random Forest and Linear Regression models show promising performance with lower MSE and higher R2 values.
+- Decision Tree Regression achieved perfect R2 on the training set but performed poorly on the test set, indicating overfitting.
+- Deep Neural Network (NN) has a high MSE and approximately zero R2, suggesting poor performance on both training and test sets.
+
+
+**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)
+
diff --git a/Top Foreign Languages Analysis/requirements.txt b/Top Foreign Languages Analysis/requirements.txt
new file mode 100644
index 000000000..c5d6ed9b3
--- /dev/null
+++ b/Top Foreign Languages Analysis/requirements.txt	
@@ -0,0 +1,11 @@
+numpy==1.19.2
+pandas==1.4.3
+matplotlib==3.7.1
+scikit-learn~=1.0.2
+scipy==1.5.0
+seaborn==0.10.1
+xgboost~=1.5.2
+tensorflow==2.4.1
+keras==2.4.0
+missingno==0.5.2
+plotly==5.15.0