pizza-cost-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5ce58d19",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:06.623590Z",
     "iopub.status.busy": "2021-09-08T04:48:06.622822Z",
     "iopub.status.idle": "2021-09-08T04:48:06.634124Z",
     "shell.execute_reply": "2021-09-08T04:48:06.634845Z",
     "shell.execute_reply.started": "2021-09-08T04:36:18.84062Z"
    },
    "papermill": {
     "duration": 0.047131,
     "end_time": "2021-09-08T04:48:06.635262",
     "exception": false,
     "start_time": "2021-09-08T04:48:06.588131",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/pizza-price-prediction/pizza_v2.csv\n",
      "/kaggle/input/pizza-price-prediction/pizza_v1.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa97f790",
   "metadata": {
    "papermill": {
     "duration": 0.02041,
     "end_time": "2021-09-08T04:48:06.677363",
     "exception": false,
     "start_time": "2021-09-08T04:48:06.656953",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "![](https://www.incimages.com/uploaded_files/image/1920x1080/getty_835271096_410065.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff80856c",
   "metadata": {
    "papermill": {
     "duration": 0.020363,
     "end_time": "2021-09-08T04:48:06.718680",
     "exception": false,
     "start_time": "2021-09-08T04:48:06.698317",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# In this notebook, we will try to predcit Pizza Prices based on the features such as Company, Diameter, Toppings, Cheese Amount, Sauce Amount, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07507543",
   "metadata": {
    "papermill": {
     "duration": 0.020345,
     "end_time": "2021-09-08T04:48:06.759478",
     "exception": false,
     "start_time": "2021-09-08T04:48:06.739133",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Importing Required Libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10760472",
   "metadata": {
    "papermill": {
     "duration": 0.020499,
     "end_time": "2021-09-08T04:48:06.801918",
     "exception": false,
     "start_time": "2021-09-08T04:48:06.781419",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2ef7b745",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:06.853130Z",
     "iopub.status.busy": "2021-09-08T04:48:06.852233Z",
     "iopub.status.idle": "2021-09-08T04:48:09.616057Z",
     "shell.execute_reply": "2021-09-08T04:48:09.615478Z",
     "shell.execute_reply.started": "2021-09-08T04:36:18.858413Z"
    },
    "papermill": {
     "duration": 2.793403,
     "end_time": "2021-09-08T04:48:09.616194",
     "exception": false,
     "start_time": "2021-09-08T04:48:06.822791",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "import sklearn.metrics as metrics\n",
    "import os\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split,RandomizedSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import accuracy_score\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b4706e",
   "metadata": {
    "papermill": {
     "duration": 0.021059,
     "end_time": "2021-09-08T04:48:09.658811",
     "exception": false,
     "start_time": "2021-09-08T04:48:09.637752",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Reading the CSV File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5d617c5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:09.706364Z",
     "iopub.status.busy": "2021-09-08T04:48:09.705736Z",
     "iopub.status.idle": "2021-09-08T04:48:09.719829Z",
     "shell.execute_reply": "2021-09-08T04:48:09.719201Z",
     "shell.execute_reply.started": "2021-09-08T04:36:20.586936Z"
    },
    "papermill": {
     "duration": 0.040207,
     "end_time": "2021-09-08T04:48:09.719982",
     "exception": false,
     "start_time": "2021-09-08T04:48:09.679775",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('/kaggle/input/pizza-price-prediction/pizza_v2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f112a5ec",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:09.773086Z",
     "iopub.status.busy": "2021-09-08T04:48:09.772233Z",
     "iopub.status.idle": "2021-09-08T04:48:09.794751Z",
     "shell.execute_reply": "2021-09-08T04:48:09.795412Z",
     "shell.execute_reply.started": "2021-09-08T04:36:20.605112Z"
    },
    "papermill": {
     "duration": 0.054751,
     "end_time": "2021-09-08T04:48:09.795601",
     "exception": false,
     "start_time": "2021-09-08T04:48:09.740850",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>company</th>\n",
       "      <th>price_rupiah</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp235,000</td>\n",
       "      <td>22 inch</td>\n",
       "      <td>chicken</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp198,000</td>\n",
       "      <td>20 inch</td>\n",
       "      <td>papperoni</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp120,000</td>\n",
       "      <td>16 inch</td>\n",
       "      <td>mushrooms</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>reguler</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp155,000</td>\n",
       "      <td>14 inch</td>\n",
       "      <td>smoked_beef</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>reguler</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>Rp248,000</td>\n",
       "      <td>18 inch</td>\n",
       "      <td>mozzarella</td>\n",
       "      <td>double_signature</td>\n",
       "      <td>jumbo</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  company price_rupiah diameter      topping           variant     size  \\\n",
       "0       A    Rp235,000  22 inch      chicken  double_signature    jumbo   \n",
       "1       A    Rp198,000  20 inch    papperoni  double_signature    jumbo   \n",
       "2       A    Rp120,000  16 inch    mushrooms  double_signature  reguler   \n",
       "3       A    Rp155,000  14 inch  smoked_beef  double_signature  reguler   \n",
       "4       A    Rp248,000  18 inch   mozzarella  double_signature    jumbo   \n",
       "\n",
       "  extra_sauce extra_cheese extra_mushrooms  \n",
       "0         yes          yes              no  \n",
       "1         yes          yes              no  \n",
       "2         yes          yes             yes  \n",
       "3         yes           no             yes  \n",
       "4         yes           no             yes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() #Displaying head of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "36556105",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:09.842679Z",
     "iopub.status.busy": "2021-09-08T04:48:09.842019Z",
     "iopub.status.idle": "2021-09-08T04:48:09.850540Z",
     "shell.execute_reply": "2021-09-08T04:48:09.851094Z",
     "shell.execute_reply.started": "2021-09-08T04:36:20.637406Z"
    },
    "papermill": {
     "duration": 0.033467,
     "end_time": "2021-09-08T04:48:09.851262",
     "exception": false,
     "start_time": "2021-09-08T04:48:09.817795",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/kaggle/input/pizza-price-prediction/pizza_v2.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18c85f71",
   "metadata": {
    "papermill": {
     "duration": 0.020792,
     "end_time": "2021-09-08T04:48:09.894245",
     "exception": false,
     "start_time": "2021-09-08T04:48:09.873453",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "619b23c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:09.939793Z",
     "iopub.status.busy": "2021-09-08T04:48:09.939093Z",
     "iopub.status.idle": "2021-09-08T04:48:10.693135Z",
     "shell.execute_reply": "2021-09-08T04:48:10.693809Z",
     "shell.execute_reply.started": "2021-09-08T04:36:20.646544Z"
    },
    "papermill": {
     "duration": 0.778667,
     "end_time": "2021-09-08T04:48:10.694013",
     "exception": false,
     "start_time": "2021-09-08T04:48:09.915346",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 129 artists>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) #Companies Vs. Prices\n",
    "plt.bar(df.company,df.price_rupiah,color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fcae5720",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:10.744327Z",
     "iopub.status.busy": "2021-09-08T04:48:10.743299Z",
     "iopub.status.idle": "2021-09-08T04:48:11.584739Z",
     "shell.execute_reply": "2021-09-08T04:48:11.584137Z",
     "shell.execute_reply.started": "2021-09-08T04:36:21.272629Z"
    },
    "papermill": {
     "duration": 0.867255,
     "end_time": "2021-09-08T04:48:11.584882",
     "exception": false,
     "start_time": "2021-09-08T04:48:10.717627",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 129 artists>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10)) #Toppings Vs. Prices\n",
    "plt.bar(df.topping,df.price_rupiah,color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56a66a0",
   "metadata": {
    "papermill": {
     "duration": 0.025739,
     "end_time": "2021-09-08T04:48:11.635848",
     "exception": false,
     "start_time": "2021-09-08T04:48:11.610109",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Let's us check for NULL Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ee2e5cea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:11.693886Z",
     "iopub.status.busy": "2021-09-08T04:48:11.693196Z",
     "iopub.status.idle": "2021-09-08T04:48:11.695876Z",
     "shell.execute_reply": "2021-09-08T04:48:11.696455Z",
     "shell.execute_reply.started": "2021-09-08T04:36:22.026805Z"
    },
    "papermill": {
     "duration": 0.035736,
     "end_time": "2021-09-08T04:48:11.696641",
     "exception": false,
     "start_time": "2021-09-08T04:48:11.660905",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "company            0\n",
       "price_rupiah       0\n",
       "diameter           0\n",
       "topping            0\n",
       "variant            0\n",
       "size               0\n",
       "extra_sauce        0\n",
       "extra_cheese       0\n",
       "extra_mushrooms    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum() #No Null values are there"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d3a7229b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:11.750846Z",
     "iopub.status.busy": "2021-09-08T04:48:11.750154Z",
     "iopub.status.idle": "2021-09-08T04:48:11.799159Z",
     "shell.execute_reply": "2021-09-08T04:48:11.799674Z",
     "shell.execute_reply.started": "2021-09-08T04:36:22.035275Z"
    },
    "papermill": {
     "duration": 0.077944,
     "end_time": "2021-09-08T04:48:11.799846",
     "exception": false,
     "start_time": "2021-09-08T04:48:11.721902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>company</th>\n",
       "      <th>price_rupiah</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>5</td>\n",
       "      <td>43</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>E</td>\n",
       "      <td>Rp72,000</td>\n",
       "      <td>12 inch</td>\n",
       "      <td>chicken</td>\n",
       "      <td>classic</td>\n",
       "      <td>medium</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>49</td>\n",
       "      <td>29</td>\n",
       "      <td>30</td>\n",
       "      <td>41</td>\n",
       "      <td>76</td>\n",
       "      <td>86</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       company price_rupiah diameter  topping  variant    size extra_sauce  \\\n",
       "count      129          129      129      129      129     129         129   \n",
       "unique       5           43       12       12       20       6           2   \n",
       "top          E     Rp72,000  12 inch  chicken  classic  medium         yes   \n",
       "freq        30           11       49       29       30      41          76   \n",
       "\n",
       "       extra_cheese extra_mushrooms  \n",
       "count           129             129  \n",
       "unique            2               2  \n",
       "top             yes             yes  \n",
       "freq             86              72  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe() #Basic Info about the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "53dac545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:11.855012Z",
     "iopub.status.busy": "2021-09-08T04:48:11.854339Z",
     "iopub.status.idle": "2021-09-08T04:48:11.861986Z",
     "shell.execute_reply": "2021-09-08T04:48:11.862535Z",
     "shell.execute_reply.started": "2021-09-08T04:36:22.090075Z"
    },
    "papermill": {
     "duration": 0.036593,
     "end_time": "2021-09-08T04:48:11.862721",
     "exception": false,
     "start_time": "2021-09-08T04:48:11.826128",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "data['price_rupiah'] = data['price_rupiah'].apply(lambda x : x.replace(\"Rp\",\"\").replace(\",\",\"\"))\n",
    "# remove rp and comma and store it in new price column\n",
    "data['diameter'] = data['diameter'].apply(lambda x : x.replace(\" inch\",\"\"))\n",
    "#remove inch and store it in new column\n",
    "data[\"diameter\"] = data[\"diameter\"].astype(float)\n",
    "data[\"price_rupiah\"] = data[\"price_rupiah\"].astype(float)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f54571d",
   "metadata": {
    "papermill": {
     "duration": 0.025823,
     "end_time": "2021-09-08T04:48:11.914829",
     "exception": false,
     "start_time": "2021-09-08T04:48:11.889006",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Now, Let's Encode the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ef3dcd2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:11.970285Z",
     "iopub.status.busy": "2021-09-08T04:48:11.969639Z",
     "iopub.status.idle": "2021-09-08T04:48:11.978538Z",
     "shell.execute_reply": "2021-09-08T04:48:11.977839Z",
     "shell.execute_reply.started": "2021-09-08T04:37:02.886635Z"
    },
    "papermill": {
     "duration": 0.03773,
     "end_time": "2021-09-08T04:48:11.978700",
     "exception": false,
     "start_time": "2021-09-08T04:48:11.940970",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "en = LabelEncoder()\n",
    "catCols = ['company','topping','variant','size','extra_sauce','extra_cheese','extra_mushrooms']\n",
    "for cols in catCols:\n",
    "    data[cols] = en.fit_transform(data[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0aa4cfdb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:12.034552Z",
     "iopub.status.busy": "2021-09-08T04:48:12.033801Z",
     "iopub.status.idle": "2021-09-08T04:48:12.048161Z",
     "shell.execute_reply": "2021-09-08T04:48:12.048656Z",
     "shell.execute_reply.started": "2021-09-08T04:36:22.116129Z"
    },
    "papermill": {
     "duration": 0.04398,
     "end_time": "2021-09-08T04:48:12.048840",
     "exception": false,
     "start_time": "2021-09-08T04:48:12.004860",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>company</th>\n",
       "      <th>price_rupiah</th>\n",
       "      <th>diameter</th>\n",
       "      <th>topping</th>\n",
       "      <th>variant</th>\n",
       "      <th>size</th>\n",
       "      <th>extra_sauce</th>\n",
       "      <th>extra_cheese</th>\n",
       "      <th>extra_mushrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>235000.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>198000.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>120000.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>155000.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>248000.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   company  price_rupiah  diameter  topping  variant  size  extra_sauce  \\\n",
       "0        0      235000.0      22.0        2        8     1            1   \n",
       "1        0      198000.0      20.0        7        8     1            1   \n",
       "2        0      120000.0      16.0        5        8     4            1   \n",
       "3        0      155000.0      14.0        9        8     4            1   \n",
       "4        0      248000.0      18.0        4        8     1            1   \n",
       "\n",
       "   extra_cheese  extra_mushrooms  \n",
       "0             1                0  \n",
       "1             1                0  \n",
       "2             1                1  \n",
       "3             0                1  \n",
       "4             0                1  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head() #Displaying head of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30767aff",
   "metadata": {
    "papermill": {
     "duration": 0.026546,
     "end_time": "2021-09-08T04:48:12.102178",
     "exception": false,
     "start_time": "2021-09-08T04:48:12.075632",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# SPLITTING THE DATASET INTO TRAIN AND TEST DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "39260df9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:12.159781Z",
     "iopub.status.busy": "2021-09-08T04:48:12.159120Z",
     "iopub.status.idle": "2021-09-08T04:48:12.164859Z",
     "shell.execute_reply": "2021-09-08T04:48:12.165382Z",
     "shell.execute_reply.started": "2021-09-08T04:37:06.715562Z"
    },
    "papermill": {
     "duration": 0.036415,
     "end_time": "2021-09-08T04:48:12.165582",
     "exception": false,
     "start_time": "2021-09-08T04:48:12.129167",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "Y = data[\"price_rupiah\"]\n",
    "X = data.drop(\"price_rupiah\",axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "aa5bf78d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:12.222487Z",
     "iopub.status.busy": "2021-09-08T04:48:12.221806Z",
     "iopub.status.idle": "2021-09-08T04:48:12.226607Z",
     "shell.execute_reply": "2021-09-08T04:48:12.227177Z",
     "shell.execute_reply.started": "2021-09-08T04:37:08.565887Z"
    },
    "papermill": {
     "duration": 0.035246,
     "end_time": "2021-09-08T04:48:12.227360",
     "exception": false,
     "start_time": "2021-09-08T04:48:12.192114",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y=pd.DataFrame(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "609db3ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:12.289947Z",
     "iopub.status.busy": "2021-09-08T04:48:12.288965Z",
     "iopub.status.idle": "2021-09-08T04:48:12.292017Z",
     "shell.execute_reply": "2021-09-08T04:48:12.291444Z",
     "shell.execute_reply.started": "2021-09-08T04:37:30.610656Z"
    },
    "papermill": {
     "duration": 0.038029,
     "end_time": "2021-09-08T04:48:12.292154",
     "exception": false,
     "start_time": "2021-09-08T04:48:12.254125",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Splitting the dataset into train and test datasets\n",
    "# 80% Train Data + 20% Test Data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, test_size=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4c4d76d",
   "metadata": {
    "papermill": {
     "duration": 0.027258,
     "end_time": "2021-09-08T04:48:12.345994",
     "exception": false,
     "start_time": "2021-09-08T04:48:12.318736",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Using XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e54af7b5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-09-08T04:48:12.405390Z",
     "iopub.status.busy": "2021-09-08T04:48:12.404752Z",
     "iopub.status.idle": "2021-09-08T04:48:13.024548Z",
     "shell.execute_reply": "2021-09-08T04:48:13.025439Z",
     "shell.execute_reply.started": "2021-09-08T04:37:33.535455Z"
    },
    "papermill": {
     "duration": 0.653288,
     "end_time": "2021-09-08T04:48:13.025706",
     "exception": false,
     "start_time": "2021-09-08T04:48:12.372418",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 5764.620192307692\n",
      "R2 score : 0.9752894278317424\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "# Define the model\n",
    "model = XGBRegressor(n_estimators=1000, learning_rate=0.09) \n",
    "\n",
    "# Fit the model\n",
    "model.fit(X_train, y_train) \n",
    "\n",
    "# Get predictions\n",
    "predictions = model.predict(X_test) \n",
    "\n",
    "# Calculate MAE\n",
    "mae = mean_absolute_error(predictions, y_test)\n",
    "print(\"Mean Absolute Error:\" , mae)\n",
    "\n",
    "#Calculating R2\n",
    "r2 =  metrics.r2_score(y_test, predictions)\n",
    "print(\"R2 score :\", r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96bcbf18",
   "metadata": {
    "papermill": {
     "duration": 0.026248,
     "end_time": "2021-09-08T04:48:13.080957",
     "exception": false,
     "start_time": "2021-09-08T04:48:13.054709",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**We can see that our model has performed really well on the Test Dataset with a R2 Score >95%.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6ff86e",
   "metadata": {
    "papermill": {
     "duration": 0.026457,
     "end_time": "2021-09-08T04:48:13.134452",
     "exception": false,
     "start_time": "2021-09-08T04:48:13.107995",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cdb8314d",
   "metadata": {
    "papermill": {
     "duration": 0.026341,
     "end_time": "2021-09-08T04:48:13.187679",
     "exception": false,
     "start_time": "2021-09-08T04:48:13.161338",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "64a852be",
   "metadata": {
    "papermill": {
     "duration": 0.027396,
     "end_time": "2021-09-08T04:48:13.241848",
     "exception": false,
     "start_time": "2021-09-08T04:48:13.214452",
     "status": "completed"
    },
    "tags": []
   },
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 15.603149,
   "end_time": "2021-09-08T04:48:14.180791",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2021-09-08T04:47:58.577642",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}