ML-For-Beginners/2-Regression/3-Linear/solution/notebook.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "## Pumpkin Pricing\n",
    "\n",
    "Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data: \n",
    "\n",
    "- Only get pumpkins priced by the bushel\n",
    "- Convert the date to a month\n",
    "- Calculate the price to be an average of high and low prices\n",
    "- Convert the price to reflect the pricing by bushel quantity"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   City Name Type       Package      Variety Sub Variety  Grade     Date  \\\n",
       "0  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN  4/29/17   \n",
       "1  BALTIMORE  NaN  24 inch bins          NaN         NaN    NaN   5/6/17   \n",
       "2  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
       "3  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  9/24/16   \n",
       "4  BALTIMORE  NaN  24 inch bins  HOWDEN TYPE         NaN    NaN  11/5/16   \n",
       "\n",
       "   Low Price  High Price  Mostly Low  ...  Unit of Sale Quality Condition  \\\n",
       "0      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
       "1      270.0       280.0       270.0  ...           NaN     NaN       NaN   \n",
       "2      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
       "3      160.0       160.0       160.0  ...           NaN     NaN       NaN   \n",
       "4       90.0       100.0        90.0  ...           NaN     NaN       NaN   \n",
       "\n",
       "  Appearance Storage  Crop Repack  Trans Mode  Unnamed: 24  Unnamed: 25  \n",
       "0        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
       "1        NaN     NaN   NaN      E         NaN          NaN          NaN  \n",
       "2        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "3        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "4        NaN     NaN   NaN      N         NaN          NaN          NaN  \n",
       "\n",
       "[5 rows x 26 columns]"
      ],
      "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>City Name</th>\n      <th>Type</th>\n      <th>Package</th>\n      <th>Variety</th>\n      <th>Sub Variety</th>\n      <th>Grade</th>\n      <th>Date</th>\n      <th>Low Price</th>\n      <th>High Price</th>\n      <th>Mostly Low</th>\n      <th>...</th>\n      <th>Unit of Sale</th>\n      <th>Quality</th>\n      <th>Condition</th>\n      <th>Appearance</th>\n      <th>Storage</th>\n      <th>Crop</th>\n      <th>Repack</th>\n      <th>Trans Mode</th>\n      <th>Unnamed: 24</th>\n      <th>Unnamed: 25</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>4/29/17</td>\n      <td>270.0</td>\n      <td>280.0</td>\n      <td>270.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>E</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>5/6/17</td>\n      <td>270.0</td>\n      <td>280.0</td>\n      <td>270.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>E</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>HOWDEN TYPE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9/24/16</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>HOWDEN TYPE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>9/24/16</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>160.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BALTIMORE</td>\n      <td>NaN</td>\n      <td>24 inch bins</td>\n      <td>HOWDEN TYPE</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>11/5/16</td>\n      <td>90.0</td>\n      <td>100.0</td>\n      <td>90.0</td>\n      <td>...</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>N</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 26 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 47
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n",
    "\n",
    "pumpkins.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    Month  Variety  City  Package  Low Price  High Price      Price\n",
       "70      1        3     1        0          5           3  13.636364\n",
       "71      1        3     1        0         10           7  16.363636\n",
       "72      2        3     1        0         10           7  16.363636\n",
       "73      2        3     1        0          9           6  15.454545\n",
       "74      2        3     1        0          5           3  13.636364"
      ],
      "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>Month</th>\n      <th>Variety</th>\n      <th>City</th>\n      <th>Package</th>\n      <th>Low Price</th>\n      <th>High Price</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>70</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>3</td>\n      <td>13.636364</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>10</td>\n      <td>7</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>10</td>\n      <td>7</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>9</td>\n      <td>6</td>\n      <td>15.454545</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>5</td>\n      <td>3</td>\n      <td>13.636364</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 48
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
    "\n",
    "new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date']\n",
    "\n",
    "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
    "\n",
    "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
    "\n",
    "month = pd.DatetimeIndex(pumpkins['Date']).month\n",
    "\n",
    "new_pumpkins = pd.DataFrame({'Month': month, 'Variety': pumpkins['Variety'], 'City': pumpkins['City Name'], 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
    "\n",
    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
    "\n",
    "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
    "\n",
    "new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)\n",
    "\n",
    "new_pumpkins.head()\n"
   ]
  },
  {
   "source": [
    "A scatterplot reminds us that we only have month data from August through December. We probably need more data to be able to draw conclusions in a linear fashion."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7ffe31f74640>"
      ]
     },
     "metadata": {},
     "execution_count": 49
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 368.925 248.518125\" width=\"368.925pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M -0 248.518125 \nL 368.925 248.518125 \nL 368.925 0 \nL -0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 26.925 224.64 \nL 361.725 224.64 \nL 361.725 7.2 \nL 26.925 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"PathCollection_1\">\n    <defs>\n     <path d=\"M 0 3 \nC 0.795609 3 1.55874 2.683901 2.12132 2.12132 \nC 2.683901 1.55874 3 0.795609 3 0 \nC 3 -0.795609 2.683901 -1.55874 2.12132 -2.12132 \nC 1.55874 -2.683901 0.795609 -3 0 -3 \nC -0.795609 -3 -1.55874 -2.683901 -2.12132 -2.12132 \nC -2.683901 -1.55874 -3 -0.795609 -3 0 \nC -3 0.795609 -2.683901 1.55874 -2.12132 2.12132 \nC -1.55874 2.683901 -0.795609 3 0 3 \nz\n\" id=\"m44c2a297c2\" style=\"stroke:#1f77b4;\"/>\n    </defs>\n    <g clip-path=\"url(#pc99b83412c)\">\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m44c2a297c2\" y=\"201.474881\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m44c2a297c2\" y=\"188.193399\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"188.193399\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"192.62056\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"201.474881\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"188.193399\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"192.62056\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"189.300189\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"201.474881\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"192.62056\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"189.300189\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"201.474881\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"192.62056\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"189.300189\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"201.474881\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"194.83414\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"194.325\" xlink:href=\"#m44c2a297c2\" y=\"192.62056\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m44c2a297c2\" y=\"188.193399\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m44c2a297c2\" y=\"197.047721\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m44c2a297c2\" y=\"188.193399\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m44c2a297c2\" y=\"197.047721\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m44c2a297c2\" y=\"197.047721\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m44c2a297c2\" y=\"197.047721\"/>\n     <use style=\"fill:#1f77b4;stroke:#1f77b4;\" x=\"118.234091\" xlink:href=\"#m44c2a297c2\" y=\"92.566727\"/>\n     <u
      "image/png": "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
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.scatter('Month','Price',data=new_pumpkins)"
   ]
  },
  {
   "source": [
    "Try some different correlations"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.32363971816089226\n0.6061712937226021\n"
     ]
    }
   ],
   "source": [
    "print(new_pumpkins['City'].corr(new_pumpkins['Price']))\n",
    "\n",
    "print(new_pumpkins['Package'].corr(new_pumpkins['Price']))\n"
   ]
  },
  {
   "source": [
    "Drop unused columns"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\nInt64Index: 415 entries, 70 to 1742\nData columns (total 7 columns):\n #   Column      Non-Null Count  Dtype  \n---  ------      --------------  -----  \n 0   Month       415 non-null    int64  \n 1   Variety     415 non-null    int64  \n 2   City        415 non-null    int64  \n 3   Package     415 non-null    int64  \n 4   Low Price   415 non-null    int64  \n 5   High Price  415 non-null    int64  \n 6   Price       415 non-null    float64\ndtypes: float64(1), int64(6)\nmemory usage: 25.9 KB\n"
     ]
    }
   ],
   "source": [
    "\n",
    "new_pumpkins.dropna(inplace=True)\n",
    "new_pumpkins.info()\n",
    "\n",
    "\n"
   ]
  },
  {
   "source": [
    "Create a new dataframe"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Package      Price\n",
       "70          0  13.636364\n",
       "71          0  16.363636\n",
       "72          0  16.363636\n",
       "73          0  15.454545\n",
       "74          0  13.636364\n",
       "...       ...        ...\n",
       "1738        2  30.000000\n",
       "1739        2  28.750000\n",
       "1740        2  25.750000\n",
       "1741        2  24.000000\n",
       "1742        2  24.000000\n",
       "\n",
       "[415 rows x 2 columns]"
      ],
      "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>Package</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>70</th>\n      <td>0</td>\n      <td>13.636364</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>0</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>0</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>0</td>\n      <td>15.454545</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>0</td>\n      <td>13.636364</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>2</td>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>1739</th>\n      <td>2</td>\n      <td>28.750000</td>\n    </tr>\n    <tr>\n      <th>1740</th>\n      <td>2</td>\n      <td>25.750000</td>\n    </tr>\n    <tr>\n      <th>1741</th>\n      <td>2</td>\n      <td>24.000000</td>\n    </tr>\n    <tr>\n      <th>1742</th>\n      <td>2</td>\n      <td>24.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>415 rows × 2 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 52
    }
   ],
   "source": [
    "new_columns = ['Package', 'Price']\n",
    "lin_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')\n",
    "\n",
    "lin_pumpkins\n"
   ]
  },
  {
   "source": [
    "Set X and y arrays to correspond to Package and Price"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = lin_pumpkins.values[:, :1]\n",
    "y = lin_pumpkins.values[:, 1:2]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model Accuracy:  0.3470558343642912\nCoefficients:  [[4.88720357]]\nMean squared error:  62.582140520848164\nCoefficient of determination:  0.4133978531557345\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train,y_train)\n",
    "\n",
    "pred = lin_reg.predict(X_test)\n",
    "\n",
    "accuracy_score = lin_reg.score(X_train,y_train)\n",
    "print('Model Accuracy: ', accuracy_score)\n",
    "\n",
    "# The coefficients\n",
    "print('Coefficients: ', lin_reg.coef_)\n",
    "# The mean squared error\n",
    "print('Mean squared error: ',\n",
    "      mean_squared_error(y_test, pred))\n",
    "# The coefficient of determination: 1 is perfect prediction\n",
    "print('Coefficient of determination: ',\n",
    "      r2_score(y_test, pred)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"262.19625pt\" version=\"1.1\" viewBox=\"0 0 382.603125 262.19625\" width=\"382.603125pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 262.19625 \nL 382.603125 262.19625 \nL 382.603125 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 40.603125 224.64 \nL 375.403125 224.64 \nL 375.403125 7.2 \nL 40.603125 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"PathCollection_1\">\n    <defs>\n     <path d=\"M 0 3 \nC 0.795609 3 1.55874 2.683901 2.12132 2.12132 \nC 2.683901 1.55874 3 0.795609 3 0 \nC 3 -0.795609 2.683901 -1.55874 2.12132 -2.12132 \nC 1.55874 -2.683901 0.795609 -3 0 -3 \nC -0.795609 -3 -1.55874 -2.683901 -2.12132 -2.12132 \nC -2.683901 -1.55874 -3 -0.795609 -3 0 \nC -3 0.795609 -2.683901 1.55874 -2.12132 2.12132 \nC -1.55874 2.683901 -0.795609 3 0 3 \nz\n\" id=\"m6ae6aaec88\" style=\"stroke:#000000;\"/>\n    </defs>\n    <g clip-path=\"url(#p9c2f50fa15)\">\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"202.581672\"/>\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"192.62056\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"82.826973\"/>\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"202.581672\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"63.347466\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"92.566727\"/>\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"188.193399\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"151.005249\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"92.566727\"/>\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"179.339078\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"121.785988\"/>\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"192.62056\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"92.566727\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"107.176358\"/>\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"199.261301\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"102.306481\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"107.176358\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"107.176358\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"92.566727\"/>\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"192.62056\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"121.785988\"/>\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"192.62056\"/>\n     <use style=\"stroke:#000000;\" x=\"131.912216\" xlink:href=\"#m6ae6aaec88\" y=\"199.261301\"/>\n     <use style=\"stroke:#000000;\" x=\"55.821307\" xlink:href=\"#m6ae6aaec88\" y=\"214.756364\"/>\n     <use style=\"stroke:#000000;\" x=\"131.912216\" xlink:href=\"#m6ae6aaec88\" y=\"199.261301\"/>\n     <use style=\"stroke:#000000;\" x=\"208.003125\" xlink:href=\"#m6ae6aaec88\" y=\"102.306481\"/>\n     <use style=\"stroke:#000000;\" x=\"360.184943\" xlink:href=\"#
      "image/png": "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
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "\n",
    "plt.scatter(X_test, y_test,  color='black')\n",
    "plt.plot(X_test, pred, color='blue', linewidth=3)\n",
    "\n",
    "plt.xlabel('Package')\n",
    "plt.ylabel('Price')\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[34.78078482]])"
      ]
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "source": [
    "lin_reg.predict( np.array([ [3] ]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      Month  Variety  City  Package      Price\n",
       "70        1        3     1        0  13.636364\n",
       "71        1        3     1        0  16.363636\n",
       "72        2        3     1        0  16.363636\n",
       "73        2        3     1        0  15.454545\n",
       "74        2        3     1        0  13.636364\n",
       "...     ...      ...   ...      ...        ...\n",
       "1738      1        1     9        2  30.000000\n",
       "1739      1        1     9        2  28.750000\n",
       "1740      1        1     9        2  25.750000\n",
       "1741      1        1     9        2  24.000000\n",
       "1742      1        1     9        2  24.000000\n",
       "\n",
       "[415 rows x 5 columns]"
      ],
      "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>Month</th>\n      <th>Variety</th>\n      <th>City</th>\n      <th>Package</th>\n      <th>Price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>70</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>13.636364</td>\n    </tr>\n    <tr>\n      <th>71</th>\n      <td>1</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>72</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>16.363636</td>\n    </tr>\n    <tr>\n      <th>73</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>15.454545</td>\n    </tr>\n    <tr>\n      <th>74</th>\n      <td>2</td>\n      <td>3</td>\n      <td>1</td>\n      <td>0</td>\n      <td>13.636364</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1738</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>30.000000</td>\n    </tr>\n    <tr>\n      <th>1739</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>28.750000</td>\n    </tr>\n    <tr>\n      <th>1740</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>25.750000</td>\n    </tr>\n    <tr>\n      <th>1741</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>24.000000</td>\n    </tr>\n    <tr>\n      <th>1742</th>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>2</td>\n      <td>24.000000</td>\n    </tr>\n  </tbody>\n</table>\n<p>415 rows × 5 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 57
    }
   ],
   "source": [
    "new_columns = ['Variety', 'Package', 'City', 'Month', 'Price']\n",
    "poly_pumpkins = new_pumpkins.drop([c for c in new_pumpkins.columns if c not in new_columns], axis='columns')\n",
    "\n",
    "poly_pumpkins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7ffe1145eb20>"
      ],
      "text/html": "<style  type=\"text/css\" >\n    #T_527ee678_875b_11eb_9f69_acde48001122row0_col0 {\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row0_col1 {\n            background-color:  #ead4c8;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row0_col2 {\n            background-color:  #4961d2;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row0_col3 {\n            background-color:  #9bbcff;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row0_col4 {\n            background-color:  #bbd1f8;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row1_col0 {\n            background-color:  #9fbfff;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row1_col1 {\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row1_col2 {\n            background-color:  #3b4cc0;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row1_col3 {\n            background-color:  #3b4cc0;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row1_col4 {\n            background-color:  #3b4cc0;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row2_col0 {\n            background-color:  #3b4cc0;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row2_col1 {\n            background-color:  #a9c6fd;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row2_col2 {\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row2_col3 {\n            background-color:  #edd2c3;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row2_col4 {\n            background-color:  #f5c0a7;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row3_col0 {\n            background-color:  #455cce;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row3_col1 {\n            background-color:  #6485ec;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row3_col2 {\n            background-color:  #ccd9ed;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row3_col3 {\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row3_col4 {\n            background-color:  #f08a6c;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row4_col0 {\n            background-color:  #445acc;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row4_col1 {\n            background-color:  #3b4cc0;\n            color:  #f1f1f1;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row4_col2 {\n            background-color:  #d2dbe8;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row4_col3 {\n            background-color:  #f39778;\n            color:  #000000;\n        }    #T_527ee678_875b_11eb_9f69_acde48001122row4_col4 {\n            background-color:  #b40426;\n            color:  #f1f1f1;\n        }</style><table id=\"T_527ee678_875b_11eb_9f69_acde48001122\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >Month</th>        <th class=\"col_heading level0 col1\" >Variety</th>        <th class=\"col_heading level0 col2\" >City</th>        <th class=\"col_heading level0 col3\" >Package</th>        <th class=\"col_heading level0 col4\" >Price</th>    </tr></thead><tbody>\n                <tr>\n                        <th id=\"T_527ee678_875b_11eb_9f69_acde48001122level0_row0\" class=\"row_heading level0 row0\" >Month</th>\n                        <td id=\"T_527ee678_875b_11eb_9f69_a
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "corr = poly_pumpkins.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "source": [
    "Select the Package/Price columns"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=poly_pumpkins.iloc[:,3:4].values\n",
    "y=poly_pumpkins.iloc[:,4:5].values\n"
   ]
  },
  {
   "source": [
    "Create Polynomial Regression model"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"248.518125pt\" version=\"1.1\" viewBox=\"0 0 368.925 248.518125\" width=\"368.925pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M -0 248.518125 \nL 368.925 248.518125 \nL 368.925 0 \nL -0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"patch_2\">\n    <path d=\"M 26.925 224.64 \nL 361.725 224.64 \nL 361.725 7.2 \nL 26.925 7.2 \nz\n\" style=\"fill:#ffffff;\"/>\n   </g>\n   <g id=\"PathCollection_1\">\n    <defs>\n     <path d=\"M 0 3 \nC 0.795609 3 1.55874 2.683901 2.12132 2.12132 \nC 2.683901 1.55874 3 0.795609 3 0 \nC 3 -0.795609 2.683901 -1.55874 2.12132 -2.12132 \nC 1.55874 -2.683901 0.795609 -3 0 -3 \nC -0.795609 -3 -1.55874 -2.683901 -2.12132 -2.12132 \nC -2.683901 -1.55874 -3 -0.795609 -3 0 \nC -3 0.795609 -2.683901 1.55874 -2.12132 2.12132 \nC -1.55874 2.683901 -0.795609 3 0 3 \nz\n\" id=\"m7aa789a087\" style=\"stroke:#000000;\"/>\n    </defs>\n    <g clip-path=\"url(#pdeb33c423d)\">\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"201.474881\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"188.193399\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"188.193399\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"192.62056\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"201.474881\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"188.193399\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"192.62056\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"189.300189\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"201.474881\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"192.62056\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"189.300189\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"201.474881\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"192.62056\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"189.300189\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"201.474881\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"194.83414\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"192.62056\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"188.193399\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"197.047721\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"188.193399\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"197.047721\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"197.047721\"/>\n     <use style=\"stroke:#000000;\" x=\"42.143182\" xlink:href=\"#m7aa789a087\" y=\"197.047721\"/>\n     <use style=\"stroke:#000000;\" x=\"194.325\" xlink:href=\"#m7aa789a087\" y=\"92.566727\"/>\n     <use style=\"stroke:#000000;\" x=\"194.325\" xlink:href=\"#m7aa789a087\" y=\"121.785988\"/>\n     <use style=\"stroke:#000000;\" x=\"194.325\" xlink:href=\"#m7aa789a087\" y=\"92.566727\"/>\n     <use style=\"stroke:#000000;\" x=\"194.325\" xlink:href=\"#m7aa789a087\" y=\"121.785988\"/>\n     <u
      "image/png": "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
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "pipeline = make_pipeline(PolynomialFeatures(4), LinearRegression())\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
    "\n",
    "pipeline.fit(np.array(X_train), y_train)\n",
    "\n",
    "y_pred=pipeline.predict(X_test)\n",
    "\n",
    "df = pd.DataFrame({'x': X_test[:,0], 'y': y_pred[:,0]})\n",
    "df.sort_values(by='x',inplace = True)\n",
    "points = pd.DataFrame(df).to_numpy()\n",
    "\n",
    "plt.plot(points[:, 0], points[:, 1],color=\"blue\")\n",
    "\n",
    "plt.scatter(X,y, color=\"black\")\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model Accuracy:  0.8537946517073784\n"
     ]
    }
   ],
   "source": [
    "accuracy_score = pipeline.score(X_train,y_train)\n",
    "print('Model Accuracy: ', accuracy_score)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}