checkpoint_regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2ddc28b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import plotly.express as px\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e0f995e9",
   "metadata": {},
   "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>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7129300520</td>\n",
       "      <td>20141013T000000</td>\n",
       "      <td>221900.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1180</td>\n",
       "      <td>5650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1180</td>\n",
       "      <td>0</td>\n",
       "      <td>1955</td>\n",
       "      <td>0</td>\n",
       "      <td>98178</td>\n",
       "      <td>47.5112</td>\n",
       "      <td>-122.257</td>\n",
       "      <td>1340</td>\n",
       "      <td>5650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6414100192</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>538000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>2570</td>\n",
       "      <td>7242</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>2170</td>\n",
       "      <td>400</td>\n",
       "      <td>1951</td>\n",
       "      <td>1991</td>\n",
       "      <td>98125</td>\n",
       "      <td>47.7210</td>\n",
       "      <td>-122.319</td>\n",
       "      <td>1690</td>\n",
       "      <td>7639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5631500400</td>\n",
       "      <td>20150225T000000</td>\n",
       "      <td>180000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>770</td>\n",
       "      <td>10000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>770</td>\n",
       "      <td>0</td>\n",
       "      <td>1933</td>\n",
       "      <td>0</td>\n",
       "      <td>98028</td>\n",
       "      <td>47.7379</td>\n",
       "      <td>-122.233</td>\n",
       "      <td>2720</td>\n",
       "      <td>8062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2487200875</td>\n",
       "      <td>20141209T000000</td>\n",
       "      <td>604000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1960</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>910</td>\n",
       "      <td>1965</td>\n",
       "      <td>0</td>\n",
       "      <td>98136</td>\n",
       "      <td>47.5208</td>\n",
       "      <td>-122.393</td>\n",
       "      <td>1360</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1954400510</td>\n",
       "      <td>20150218T000000</td>\n",
       "      <td>510000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1680</td>\n",
       "      <td>8080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1680</td>\n",
       "      <td>0</td>\n",
       "      <td>1987</td>\n",
       "      <td>0</td>\n",
       "      <td>98074</td>\n",
       "      <td>47.6168</td>\n",
       "      <td>-122.045</td>\n",
       "      <td>1800</td>\n",
       "      <td>7503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7237550310</td>\n",
       "      <td>20140512T000000</td>\n",
       "      <td>1225000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>4.50</td>\n",
       "      <td>5420</td>\n",
       "      <td>101930</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>3890</td>\n",
       "      <td>1530</td>\n",
       "      <td>2001</td>\n",
       "      <td>0</td>\n",
       "      <td>98053</td>\n",
       "      <td>47.6561</td>\n",
       "      <td>-122.005</td>\n",
       "      <td>4760</td>\n",
       "      <td>101930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1321400060</td>\n",
       "      <td>20140627T000000</td>\n",
       "      <td>257500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.25</td>\n",
       "      <td>1715</td>\n",
       "      <td>6819</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1715</td>\n",
       "      <td>0</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>98003</td>\n",
       "      <td>47.3097</td>\n",
       "      <td>-122.327</td>\n",
       "      <td>2238</td>\n",
       "      <td>6819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2008000270</td>\n",
       "      <td>20150115T000000</td>\n",
       "      <td>291850.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.50</td>\n",
       "      <td>1060</td>\n",
       "      <td>9711</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1060</td>\n",
       "      <td>0</td>\n",
       "      <td>1963</td>\n",
       "      <td>0</td>\n",
       "      <td>98198</td>\n",
       "      <td>47.4095</td>\n",
       "      <td>-122.315</td>\n",
       "      <td>1650</td>\n",
       "      <td>9711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2414600126</td>\n",
       "      <td>20150415T000000</td>\n",
       "      <td>229500.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1780</td>\n",
       "      <td>7470</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1050</td>\n",
       "      <td>730</td>\n",
       "      <td>1960</td>\n",
       "      <td>0</td>\n",
       "      <td>98146</td>\n",
       "      <td>47.5123</td>\n",
       "      <td>-122.337</td>\n",
       "      <td>1780</td>\n",
       "      <td>8113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3793500160</td>\n",
       "      <td>20150312T000000</td>\n",
       "      <td>323000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1890</td>\n",
       "      <td>6560</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1890</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98038</td>\n",
       "      <td>47.3684</td>\n",
       "      <td>-122.031</td>\n",
       "      <td>2390</td>\n",
       "      <td>7570</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date      price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  7129300520  20141013T000000   221900.0         3       1.00         1180   \n",
       "1  6414100192  20141209T000000   538000.0         3       2.25         2570   \n",
       "2  5631500400  20150225T000000   180000.0         2       1.00          770   \n",
       "3  2487200875  20141209T000000   604000.0         4       3.00         1960   \n",
       "4  1954400510  20150218T000000   510000.0         3       2.00         1680   \n",
       "5  7237550310  20140512T000000  1225000.0         4       4.50         5420   \n",
       "6  1321400060  20140627T000000   257500.0         3       2.25         1715   \n",
       "7  2008000270  20150115T000000   291850.0         3       1.50         1060   \n",
       "8  2414600126  20150415T000000   229500.0         3       1.00         1780   \n",
       "9  3793500160  20150312T000000   323000.0         3       2.50         1890   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      5650     1.0           0     0  ...      7        1180              0   \n",
       "1      7242     2.0           0     0  ...      7        2170            400   \n",
       "2     10000     1.0           0     0  ...      6         770              0   \n",
       "3      5000     1.0           0     0  ...      7        1050            910   \n",
       "4      8080     1.0           0     0  ...      8        1680              0   \n",
       "5    101930     1.0           0     0  ...     11        3890           1530   \n",
       "6      6819     2.0           0     0  ...      7        1715              0   \n",
       "7      9711     1.0           0     0  ...      7        1060              0   \n",
       "8      7470     1.0           0     0  ...      7        1050            730   \n",
       "9      6560     2.0           0     0  ...      7        1890              0   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      1955             0    98178  47.5112 -122.257           1340   \n",
       "1      1951          1991    98125  47.7210 -122.319           1690   \n",
       "2      1933             0    98028  47.7379 -122.233           2720   \n",
       "3      1965             0    98136  47.5208 -122.393           1360   \n",
       "4      1987             0    98074  47.6168 -122.045           1800   \n",
       "5      2001             0    98053  47.6561 -122.005           4760   \n",
       "6      1995             0    98003  47.3097 -122.327           2238   \n",
       "7      1963             0    98198  47.4095 -122.315           1650   \n",
       "8      1960             0    98146  47.5123 -122.337           1780   \n",
       "9      2003             0    98038  47.3684 -122.031           2390   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "5      101930  \n",
       "6        6819  \n",
       "7        9711  \n",
       "8        8113  \n",
       "9        7570  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('kc_house_data.csv')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5e7d84c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 21613 entries, 0 to 21612\n",
      "Data columns (total 21 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   id             21613 non-null  int64  \n",
      " 1   date           21613 non-null  object \n",
      " 2   price          21613 non-null  float64\n",
      " 3   bedrooms       21613 non-null  int64  \n",
      " 4   bathrooms      21613 non-null  float64\n",
      " 5   sqft_living    21613 non-null  int64  \n",
      " 6   sqft_lot       21613 non-null  int64  \n",
      " 7   floors         21613 non-null  float64\n",
      " 8   waterfront     21613 non-null  int64  \n",
      " 9   view           21613 non-null  int64  \n",
      " 10  condition      21613 non-null  int64  \n",
      " 11  grade          21613 non-null  int64  \n",
      " 12  sqft_above     21613 non-null  int64  \n",
      " 13  sqft_basement  21613 non-null  int64  \n",
      " 14  yr_built       21613 non-null  int64  \n",
      " 15  yr_renovated   21613 non-null  int64  \n",
      " 16  zipcode        21613 non-null  int64  \n",
      " 17  lat            21613 non-null  float64\n",
      " 18  long           21613 non-null  float64\n",
      " 19  sqft_living15  21613 non-null  int64  \n",
      " 20  sqft_lot15     21613 non-null  int64  \n",
      "dtypes: float64(5), int64(15), object(1)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "062fb8ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id               0\n",
       "date             0\n",
       "price            0\n",
       "bedrooms         0\n",
       "bathrooms        0\n",
       "sqft_living      0\n",
       "sqft_lot         0\n",
       "floors           0\n",
       "waterfront       0\n",
       "view             0\n",
       "condition        0\n",
       "grade            0\n",
       "sqft_above       0\n",
       "sqft_basement    0\n",
       "yr_built         0\n",
       "yr_renovated     0\n",
       "zipcode          0\n",
       "lat              0\n",
       "long             0\n",
       "sqft_living15    0\n",
       "sqft_lot15       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8552c90e",
   "metadata": {},
   "source": [
    "<b>No null values to deal with the dataframe.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6feb8287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21613, 21)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47121c5b",
   "metadata": {},
   "source": [
    "<b>Let's plot the correlation between all the features</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a6ed8617",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(20,15))\n",
    "colormap=sns.diverging_palette(200,10,as_cmap=True)\n",
    "sns.heatmap(df.corr(),annot=True,cmap=colormap);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b486483a",
   "metadata": {},
   "source": [
    "<b>It seems that 'sqft_living' feature has the best correlation coefficient with 'price'. In order to verify its impact, let's plot 'sqft_living' vs 'price'.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c416aad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=df['sqft_living'],y=df['price'])\n",
    "plt.xlabel('Living square feet')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Living vs Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6e0855ca",
   "metadata": {},
   "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>sqft_living</th>\n",
       "      <th>grade</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>sqft_above</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sqft_living</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.762704</td>\n",
       "      <td>0.754665</td>\n",
       "      <td>0.576671</td>\n",
       "      <td>0.876597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>0.762704</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.664983</td>\n",
       "      <td>0.356967</td>\n",
       "      <td>0.755923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.754665</td>\n",
       "      <td>0.664983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.515884</td>\n",
       "      <td>0.685342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.576671</td>\n",
       "      <td>0.356967</td>\n",
       "      <td>0.515884</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.477600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sqft_above</th>\n",
       "      <td>0.876597</td>\n",
       "      <td>0.755923</td>\n",
       "      <td>0.685342</td>\n",
       "      <td>0.477600</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             sqft_living     grade  bathrooms  bedrooms  sqft_above\n",
       "sqft_living     1.000000  0.762704   0.754665  0.576671    0.876597\n",
       "grade           0.762704  1.000000   0.664983  0.356967    0.755923\n",
       "bathrooms       0.754665  0.664983   1.000000  0.515884    0.685342\n",
       "bedrooms        0.576671  0.356967   0.515884  1.000000    0.477600\n",
       "sqft_above      0.876597  0.755923   0.685342  0.477600    1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examination of the correlation between 'sqft_living' and the other features. The result will determine the importance of each feature in predicting price further\n",
    "\n",
    "df[['sqft_living','grade','bathrooms','bedrooms','sqft_above']].corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fbad6bc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    21450\n",
       "1      163\n",
       "Name: waterfront, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examination of the importance of the 'waterfront' feature\n",
    "df['waterfront'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "472b244c",
   "metadata": {},
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>waterfront</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.315636e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.661876e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   price\n",
       "waterfront              \n",
       "0           5.315636e+05\n",
       "1           1.661876e+06"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['price','waterfront']].groupby('waterfront').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "093e81ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='waterfront',y='price',data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64d5cafe",
   "metadata": {},
   "source": [
    "<b>'waterfront' feature can be a key factor for predicting price. According to the correlation coefficient with 'price' feature, it will be taken into account in the multi-linear regression study.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0aa752f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3     9824\n",
       "4     6882\n",
       "2     2760\n",
       "5     1601\n",
       "6      272\n",
       "1      199\n",
       "7       38\n",
       "0       13\n",
       "8       13\n",
       "9        6\n",
       "10       3\n",
       "11       1\n",
       "33       1\n",
       "Name: bedrooms, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examination of the importance of the number of bedrooms\n",
    "\n",
    "df['bedrooms'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "11cbeb29",
   "metadata": {},
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.095038e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.176429e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.013727e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.662321e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.354195e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.865998e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>8.255206e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9.511847e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.105077e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.939998e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8.193333e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.200000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6.400000e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 price\n",
       "bedrooms              \n",
       "0         4.095038e+05\n",
       "1         3.176429e+05\n",
       "2         4.013727e+05\n",
       "3         4.662321e+05\n",
       "4         6.354195e+05\n",
       "5         7.865998e+05\n",
       "6         8.255206e+05\n",
       "7         9.511847e+05\n",
       "8         1.105077e+06\n",
       "9         8.939998e+05\n",
       "10        8.193333e+05\n",
       "11        5.200000e+05\n",
       "33        6.400000e+05"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['price','bedrooms']].groupby('bedrooms').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c566c1c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,4))\n",
    "sns.barplot(x='bedrooms',y='price',data=df)\n",
    "plt.xlabel('Number of bedrooms')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Number of bedrooms vs Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "555b8860",
   "metadata": {},
   "source": [
    "<b>From this result, it seems that price has a positive linear progression from 1 to 8 numbers of bedrooms,but it decreases from 9 to 11. 11 and 33 numbers of bedrooms are outliers. The numbers of bedrooms can be useful to predict the variation of house price.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f5bb833e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.50    5380\n",
       "1.00    3852\n",
       "1.75    3048\n",
       "2.25    2047\n",
       "2.00    1930\n",
       "1.50    1446\n",
       "2.75    1185\n",
       "3.00     753\n",
       "3.50     731\n",
       "3.25     589\n",
       "3.75     155\n",
       "4.00     136\n",
       "4.50     100\n",
       "4.25      79\n",
       "0.75      72\n",
       "4.75      23\n",
       "5.00      21\n",
       "5.25      13\n",
       "0.00      10\n",
       "5.50      10\n",
       "1.25       9\n",
       "6.00       6\n",
       "0.50       4\n",
       "5.75       4\n",
       "6.75       2\n",
       "8.00       2\n",
       "6.25       2\n",
       "6.50       2\n",
       "7.50       1\n",
       "7.75       1\n",
       "Name: bathrooms, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Examination of the number of bathrooms\n",
    "\n",
    "df['bathrooms'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6df0d5e",
   "metadata": {},
   "source": [
    "<b>This result indicates that all values are presented in a float format which is not appropriate to quantify the real number of bathrooms. So, it's better to round and cast them to an integer.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8619ec1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    13851\n",
       "1     3933\n",
       "3     2527\n",
       "4     1201\n",
       "5       57\n",
       "6       24\n",
       "0       14\n",
       "8        4\n",
       "7        2\n",
       "Name: bathrooms, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['bathrooms']=round(df['bathrooms']).astype(int)\n",
    "df['bathrooms'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f8631007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot the number of bathrooms with price\n",
    "\n",
    "plt.figure(figsize=(14,4))\n",
    "sns.barplot(x='bathrooms',y='price',data=df)\n",
    "plt.xlabel('Number of bathrooms')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Number of bathrooms vs Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe49315",
   "metadata": {},
   "source": [
    "<b>As well as the number of bathrooms increases, the price shows a positive progression. The number of bathrooms can be useful to predict price variation.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e75fe4d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "sns.barplot(x='grade',y='price',data=df)\n",
    "plt.xlabel('Grade')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Price vs Grade')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8b8dfe4d",
   "metadata": {},
   "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>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.420000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.056667e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.143810e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.485240e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3.019196e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.025903e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.428528e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.735132e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.071771e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.496842e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2.191222e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.709615e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price\n",
       "grade              \n",
       "1      1.420000e+05\n",
       "3      2.056667e+05\n",
       "4      2.143810e+05\n",
       "5      2.485240e+05\n",
       "6      3.019196e+05\n",
       "7      4.025903e+05\n",
       "8      5.428528e+05\n",
       "9      7.735132e+05\n",
       "10     1.071771e+06\n",
       "11     1.496842e+06\n",
       "12     2.191222e+06\n",
       "13     3.709615e+06"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['price','grade']].groupby('grade').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e36730c2",
   "metadata": {},
   "source": [
    "<b>As well as grade increases, the price shows a progression too. 'grade' feature can be useful for price house prediction.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8c421d9",
   "metadata": {},
   "source": [
    "<b><u>FEATURES SELECTION CONCLUSION</u></b>\n",
    "\n",
    "<b>From the correlation coefficient, 'sqft_living' is the most significant to predict the price. In addition, 'grade' feature shows a significantly progression. These two features will be used for Linear Regression.</b>\n",
    "    \n",
    "<b>It is also notice that 'sqft_living' has a strong correlation with: 'grade','bathrooms','bedrooms','sqft_above' features. So they will be used for multi-linear regression model with 'sqft_living' feature. The same features will be used for polynomial regression.\n",
    "</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f124fc6",
   "metadata": {},
   "source": [
    "<b><u>1. LINEAR REGRESSION</u></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6abcb11",
   "metadata": {},
   "source": [
    "<b>Let's start the linear regression with 'sqft_living' feature.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "80dabf26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df['sqft_living'].values\n",
    "y=df['price'].values\n",
    "\n",
    "# Split data with test size 20%\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=3)\n",
    "\n",
    "# Reshaping x_train, y_train, x_test, y_test\n",
    "\n",
    "x_train=x_train.reshape(-1,1)\n",
    "y_train=y_train.reshape(-1,1)\n",
    "x_test=x_test.reshape(-1,1)\n",
    "y_test=y_test.reshape(-1,1)\n",
    "\n",
    "# Build linear regression model\n",
    "\n",
    "model= LinearRegression()\n",
    "\n",
    "# Fitting the training data\n",
    "\n",
    "model.fit(x_train,y_train)\n",
    "\n",
    "# Make predictions using the testing set\n",
    "\n",
    "pred=model.predict(x_test)\n",
    "\n",
    "# Plot the linear regression\n",
    "plt.subplots(figsize=(15,10))\n",
    "plt.scatter(x_test,y_test,label='Data')\n",
    "plt.plot(x_test,pred,color='red',label='Predicted regression line')\n",
    "plt.xlabel('Living square feet')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f1d9b84b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Squared mean error is equal to: 254289.15\n",
      "R squared is equal to: 0.4962\n",
      "The expression of the price based on living square feet is: house_price = -47235.81 + 282.25 *sqft_living\n"
     ]
    }
   ],
   "source": [
    "# Measure the performance of the linear regression, using the testing set\n",
    "\n",
    "MSE=metrics.mean_squared_error(y_test,pred)\n",
    "\n",
    "print('Squared mean error is equal to:',round(np.sqrt(MSE),2))\n",
    "print('R squared is equal to:',round(metrics.r2_score(y_test,pred),4))\n",
    "\n",
    "# Linear regression expression based on 'sqft_living' feature\n",
    "\n",
    "intercept = round(model.intercept_[0],2)\n",
    "coefficient = round(model.coef_[0][0],2)\n",
    "sqft_living=df['sqft_living']\n",
    "print('The expression of the price based on living square feet is:',f\"house_price = {intercept} + {coefficient} *sqft_living\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d66a1db",
   "metadata": {},
   "source": [
    "<b>Let's continue the linear regression with the 'grade' feature and compare the performance.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "52884f23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x=df['grade'].values\n",
    "y=df['price'].values\n",
    "\n",
    "# Split data with test size 20%\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=3)\n",
    "\n",
    "# Reshaping x_train, y_train, x_test, y_test\n",
    "\n",
    "x_train=x_train.reshape(-1,1)\n",
    "y_train=y_train.reshape(-1,1)\n",
    "x_test=x_test.reshape(-1,1)\n",
    "y_test=y_test.reshape(-1,1)\n",
    "\n",
    "# Build linear regression model\n",
    "\n",
    "model= LinearRegression()\n",
    "\n",
    "# Fitting the training data\n",
    "\n",
    "model.fit(x_train,y_train)\n",
    "\n",
    "# Make predictions using the testing set\n",
    "\n",
    "pred=model.predict(x_test)\n",
    "\n",
    "# Plot the linear regression\n",
    "plt.subplots(figsize=(15,10))\n",
    "plt.scatter(x_test,y_test,label='Data')\n",
    "plt.plot(x_test,pred,color='red',label='Predicted regression line')\n",
    "plt.xlabel('Grade')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7a986841",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Squared mean error is equal to: 263387.61\n",
      "R squared is equal to: 0.4595\n",
      "The expression of the price based on grade is: house_price = -1061459.62 + 209225.48 *grade\n"
     ]
    }
   ],
   "source": [
    "# Measure the performance of the linear regression, using the testing set\n",
    "\n",
    "MSE=metrics.mean_squared_error(y_test,pred)\n",
    "\n",
    "print('Squared mean error is equal to:',round(np.sqrt(MSE),2))\n",
    "print('R squared is equal to:',round(metrics.r2_score(y_test,pred),4))\n",
    "\n",
    "# Linear regression expression based on 'grade' feature\n",
    "\n",
    "intercept = round(model.intercept_[0],2)\n",
    "coefficient = round(model.coef_[0][0],2)\n",
    "grade=df['grade']\n",
    "print('The expression of the price based on grade is:',f\"house_price = {intercept} + {coefficient} *grade\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e13c7331",
   "metadata": {},
   "source": [
    "<b>From this result, we can conclude that half of price variation can be explained by the model based on 'sqft_living' and 'grade' features. Linear regression model was also studied with 'bedrooms' and 'bathrooms' features but the model performance was very weak compared to the studies shown above.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90ba96e2",
   "metadata": {},
   "source": [
    "<b><u>2. MULTI-LINEAR REGRESSION</u></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e583ea",
   "metadata": {},
   "source": [
    "<b>As previously said: 'sqft_living','grade','bedrooms,'bathrooms','sqft_above' features will be used for multi-linear regression.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "5e74f238",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error is equal to: 238568.74\n",
      "R squared is equal to: 0.56\n",
      "Intercept=  -520392.65189313015\n",
      "Coefficient= [ 2.58070056e+02  9.74069729e+04  1.59464724e+04 -8.59978284e+01\n",
      "  2.52730720e+01 -4.48251375e+04]\n"
     ]
    }
   ],
   "source": [
    "x=df[['sqft_living','grade','bathrooms','sqft_above','sqft_living15','bedrooms']]\n",
    "y=df['price'].values\n",
    "\n",
    "# Split data with test size 20%\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=3)\n",
    "\n",
    "# Build the multi-linear regression\n",
    "\n",
    "model=LinearRegression()\n",
    "\n",
    "# Fitting the training data\n",
    "\n",
    "model.fit(x_train,y_train)\n",
    "\n",
    "# Predict the testing set\n",
    "\n",
    "predicted= model.predict(x_test)\n",
    "\n",
    "# Model performance measures\n",
    "\n",
    "MSE=mean_squared_error(y_test,predicted)\n",
    "\n",
    "print('Mean squared error is equal to:',round(np.sqrt(MSE),2))\n",
    "\n",
    "print('R squared is equal to:',round(metrics.r2_score(y_test,predicted),2))\n",
    "\n",
    "print('Intercept= ',model.intercept_)\n",
    "print('Coefficient=',model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c1fa23",
   "metadata": {},
   "source": [
    "<b>With 'grade','bathrooms','sqft_above' and 'sqft_living','sqft_living15','bedrooms' features, an improvement is noticed in terms of house price prediction. Let's add more features: 'floors','view','waterfront' </b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "91d6ad0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error is equal to: 224152.58\n",
      "R squared is equal to: 0.61\n",
      "Intercept=  -484868.81742976524\n",
      "Coefficient= [ 2.08549582e+02  9.60615911e+04  1.95704276e+04 -3.94809678e+01\n",
      "  8.09886664e+00 -3.38443614e+04 -2.32845399e+04  6.33801784e+04\n",
      "  5.50895956e+05]\n"
     ]
    }
   ],
   "source": [
    "x=df[['sqft_living','grade','bathrooms','sqft_above','sqft_living15','bedrooms','floors','view','waterfront']]\n",
    "y=df['price'].values\n",
    "\n",
    "# Split data with test size 20%\n",
    "\n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=3)\n",
    "\n",
    "# Build the multi-linear regression\n",
    "\n",
    "model=LinearRegression()\n",
    "\n",
    "# Fitting the training data\n",
    "\n",
    "model.fit(x_train,y_train)\n",
    "\n",
    "# Predict the testing set\n",
    "\n",
    "predicted= model.predict(x_test)\n",
    "\n",
    "# Model performance measures\n",
    "\n",
    "MSE=mean_squared_error(y_test,predicted)\n",
    "\n",
    "print('Mean squared error is equal to:',round(np.sqrt(MSE),2))\n",
    "\n",
    "print('R squared is equal to:',round(metrics.r2_score(y_test,predicted),2))\n",
    "\n",
    "print('Intercept= ',model.intercept_)\n",
    "print('Coefficient=',model.coef_)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78a54b2f",
   "metadata": {},
   "source": [
    "<b>Compared to linear regression model, this second multi-linear regression improves the performance of the model with a score of R squared equals to 0.61. Briefly 9 features can be useful in order to predict the house price</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e71a454c",
   "metadata": {},
   "source": [
    "<b><u>3. POLYNOMIAL REGRESSION</u></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ea09985",
   "metadata": {},
   "source": [
    "<b>Polynomial regression will be done with degree 2 and degree 3</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8a7726ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error is equal to: 203615.06\n",
      "R squared is equal to: 0.68\n"
     ]
    }
   ],
   "source": [
    "x=df[['sqft_living','grade','bathrooms','sqft_above','sqft_living15','bedrooms','floors','view','waterfront']]\n",
    "y=df['price'].values\n",
    "\n",
    "\n",
    "# Split data with test size 20%\n",
    "\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=3)\n",
    "\n",
    "# Build linear regression\n",
    "\n",
    "lg=LinearRegression()\n",
    "\n",
    "# Build polynomial regression degree=2\n",
    "\n",
    "poly=PolynomialFeatures(degree=2)\n",
    "\n",
    "# Transform the training set\n",
    "\n",
    "x_train_fit=poly.fit_transform(x_train)\n",
    "\n",
    "# Fitting data\n",
    "\n",
    "lg.fit(x_train_fit,y_train)\n",
    "\n",
    "# Transform the testing set\n",
    "\n",
    "x_test_fit=poly.fit_transform(x_test)\n",
    "\n",
    "# Predict price\n",
    "\n",
    "predicted=lg.predict(x_test_fit)\n",
    "\n",
    "# Model performance measures\n",
    "\n",
    "MSE=mean_squared_error(y_test,predicted)\n",
    "print('Mean squared error is equal to:',round(np.sqrt(MSE),2))\n",
    "\n",
    "print('R squared is equal to:',round(metrics.r2_score(y_test,predicted),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "ad29e493",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error is equal to: 208945.35\n",
      "R squared is equal to: 0.66\n"
     ]
    }
   ],
   "source": [
    "x=df[['sqft_living','grade','bathrooms','sqft_above','sqft_living15','bedrooms','floors','view','waterfront']]\n",
    "y=df['price'].values\n",
    "\n",
    "\n",
    "# Split data with test size 20%\n",
    "\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=3)\n",
    "\n",
    "# Build linear regression\n",
    "\n",
    "lg=LinearRegression()\n",
    "\n",
    "# Build polynomial regression degree=3\n",
    "\n",
    "poly=PolynomialFeatures(degree=3)\n",
    "\n",
    "# Transform the training set\n",
    "\n",
    "x_train_fit=poly.fit_transform(x_train)\n",
    "\n",
    "# Fitting data\n",
    "\n",
    "lg.fit(x_train_fit,y_train)\n",
    "\n",
    "# Transform the testing set\n",
    "\n",
    "x_test_fit=poly.fit_transform(x_test)\n",
    "\n",
    "# Predict price\n",
    "\n",
    "predicted=lg.predict(x_test_fit)\n",
    "\n",
    "# Model performance measures\n",
    "\n",
    "MSE=mean_squared_error(y_test,predicted)\n",
    "print('Mean squared error is equal to:',round(np.sqrt(MSE),2))\n",
    "\n",
    "print('R squared is equal to:',round(metrics.r2_score(y_test,predicted),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db4b5ade",
   "metadata": {},
   "source": [
    "<b>From this result, polynomial regression model with degree = 2 shows the best permformance in order to predict the price house.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7001551c",
   "metadata": {},
   "source": [
    "<b><u>3. GENERAL CONCLUSION</u></b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef43b9b",
   "metadata": {},
   "source": [
    "<b>The study started with the choice of the independent features to use in order to determine the most important feature which allows to predict the house price. According to the correlation coefficient, the 'sqft_living' feature shows à high score in correlation with price. First, it is used in the linear regression model which presents a R squared score of 0.4962.\n",
    "Then a linear regression model based on 'grade' feature also has been held, and the R sqared score is 0.4595. The two features having high correlation coefficients with price allow to conclude that with a linear regression model, a half of variation of the price can be predicted.\n",
    "</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69995c71",
   "metadata": {},
   "source": [
    "<b>For multi-linear regression, two models are studied. Based on the correlation coefficients, six features are taken into account with a R squared score of 0.56. Then three more features are added, and the result shows of R squared of 0.61, which allow to say that there is an improvement.</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "febb625f",
   "metadata": {},
   "source": [
    "<b>With there nine features, the study continues with the polynomial regression with a degree = 2 and a degree = 3.\n",
    "The final result shows that a polynomial regression model with a degree = 2 within the nine features (0.68) is better than the model with a degree = 3 (0.66).</b>\n",
    "    \n",
    "<b>In conclusion, from a linear regression model with 'sqft_living' feature, an important improvement of R squared score is obtained with nine features used in a polynomial regression model, with a degree equal two.\n",
    "</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "760d073b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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