HousePrice_Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ac43e4fb-3066-41af-bbf1-913fdaffdd71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "14485a63-125e-4539-91c1-6d5e0c4e3fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5badb1f7-f942-481b-9cbf-824fafcb5c0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>size</th>\n",
       "      <th>size_units</th>\n",
       "      <th>lot_size</th>\n",
       "      <th>lot_size_units</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2590.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>6000.00</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98144</td>\n",
       "      <td>795000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>0.31</td>\n",
       "      <td>acre</td>\n",
       "      <td>98106</td>\n",
       "      <td>915000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>3783.00</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98107</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>5175.00</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98199</td>\n",
       "      <td>1950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>98102</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beds  baths    size size_units  lot_size lot_size_units  zip_code  \\\n",
       "0     3    2.5  2590.0       sqft   6000.00           sqft     98144   \n",
       "1     4    2.0  2240.0       sqft      0.31           acre     98106   \n",
       "2     4    3.0  2040.0       sqft   3783.00           sqft     98107   \n",
       "3     4    3.0  3800.0       sqft   5175.00           sqft     98199   \n",
       "4     2    2.0  1042.0       sqft       NaN            NaN     98102   \n",
       "\n",
       "       price  \n",
       "0   795000.0  \n",
       "1   915000.0  \n",
       "2   950000.0  \n",
       "3  1950000.0  \n",
       "4   950000.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e89a8d15-b3c0-4b8a-af27-7cc8929449c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2016, 8)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7de369f6-d759-45af-b305-e972ea73b89a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2016 entries, 0 to 2015\n",
      "Data columns (total 8 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   beds            2016 non-null   int64  \n",
      " 1   baths           2016 non-null   float64\n",
      " 2   size            2016 non-null   float64\n",
      " 3   size_units      2016 non-null   object \n",
      " 4   lot_size        1669 non-null   float64\n",
      " 5   lot_size_units  1669 non-null   object \n",
      " 6   zip_code        2016 non-null   int64  \n",
      " 7   price           2016 non-null   float64\n",
      "dtypes: float64(4), int64(2), object(2)\n",
      "memory usage: 126.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e92e9257-11fc-4e35-bc7e-add26d379cd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beds\n",
      "3     645\n",
      "2     560\n",
      "4     398\n",
      "1     256\n",
      "5     123\n",
      "6      22\n",
      "9       5\n",
      "7       3\n",
      "8       2\n",
      "15      1\n",
      "14      1\n",
      "Name: count, dtype: int64\n",
      "********************\n",
      "baths\n",
      "2.0    627\n",
      "1.0    493\n",
      "2.5    282\n",
      "3.0    198\n",
      "3.5    179\n",
      "1.5    137\n",
      "4.0     37\n",
      "4.5     21\n",
      "5.0     16\n",
      "5.5     13\n",
      "6.0      5\n",
      "7.0      4\n",
      "8.5      1\n",
      "0.5      1\n",
      "9.0      1\n",
      "6.5      1\n",
      "Name: count, dtype: int64\n",
      "********************\n",
      "size\n",
      "2080.0    12\n",
      "1440.0    11\n",
      "1460.0    11\n",
      "1370.0    11\n",
      "1670.0    11\n",
      "          ..\n",
      "1548.0     1\n",
      "1174.0     1\n",
      "1865.0     1\n",
      "578.0      1\n",
      "795.0      1\n",
      "Name: count, Length: 879, dtype: int64\n",
      "********************\n",
      "size_units\n",
      "sqft    2016\n",
      "Name: count, dtype: int64\n",
      "********************\n",
      "lot_size\n",
      "5000.0    61\n",
      "4000.0    45\n",
      "6000.0    38\n",
      "1.0       26\n",
      "4800.0    16\n",
      "          ..\n",
      "745.0      1\n",
      "5043.0     1\n",
      "2256.0     1\n",
      "8540.0     1\n",
      "4267.0     1\n",
      "Name: count, Length: 959, dtype: int64\n",
      "********************\n",
      "lot_size_units\n",
      "sqft    1449\n",
      "acre     220\n",
      "Name: count, dtype: int64\n",
      "********************\n",
      "zip_code\n",
      "98115    170\n",
      "98103    166\n",
      "98117    151\n",
      "98144    113\n",
      "98122    109\n",
      "98118    100\n",
      "98116     88\n",
      "98107     83\n",
      "98126     80\n",
      "98106     78\n",
      "98125     78\n",
      "98105     73\n",
      "98199     72\n",
      "98119     70\n",
      "98133     61\n",
      "98109     61\n",
      "98136     60\n",
      "98102     60\n",
      "98121     59\n",
      "98112     57\n",
      "98178     44\n",
      "98168     44\n",
      "98146     41\n",
      "98108     33\n",
      "98177     27\n",
      "98101     23\n",
      "98104     14\n",
      "98164      1\n",
      "Name: count, dtype: int64\n",
      "********************\n",
      "price\n",
      "750000.0     27\n",
      "700000.0     25\n",
      "850000.0     23\n",
      "950000.0     20\n",
      "900000.0     19\n",
      "             ..\n",
      "205000.0      1\n",
      "3400000.0     1\n",
      "1278500.0     1\n",
      "6250000.0     1\n",
      "659000.0      1\n",
      "Name: count, Length: 767, dtype: int64\n",
      "********************\n"
     ]
    }
   ],
   "source": [
    "for column in data.columns:\n",
    "    print(data[column].value_counts())\n",
    "    print(\"*\"*20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5143c486-b78e-49e6-8bb8-5a1d764fd413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beds                0\n",
       "baths               0\n",
       "size                0\n",
       "size_units          0\n",
       "lot_size          347\n",
       "lot_size_units    347\n",
       "zip_code            0\n",
       "price               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "33df6d85-feb0-4e2d-b50e-6f0818233429",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['lot_size','lot_size_units'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a998bc13-b7a1-49a4-bb4a-f7f0b08fc436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>size</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2.016000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.857639</td>\n",
       "      <td>2.159970</td>\n",
       "      <td>1735.740575</td>\n",
       "      <td>98123.638889</td>\n",
       "      <td>9.636252e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.255092</td>\n",
       "      <td>1.002023</td>\n",
       "      <td>920.132591</td>\n",
       "      <td>22.650819</td>\n",
       "      <td>9.440954e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>98101.000000</td>\n",
       "      <td>1.590000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1068.750000</td>\n",
       "      <td>98108.000000</td>\n",
       "      <td>6.017500e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>98117.000000</td>\n",
       "      <td>8.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2222.500000</td>\n",
       "      <td>98126.000000</td>\n",
       "      <td>1.105250e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11010.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>2.500000e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              beds        baths          size      zip_code         price\n",
       "count  2016.000000  2016.000000   2016.000000   2016.000000  2.016000e+03\n",
       "mean      2.857639     2.159970   1735.740575  98123.638889  9.636252e+05\n",
       "std       1.255092     1.002023    920.132591     22.650819  9.440954e+05\n",
       "min       1.000000     0.500000    250.000000  98101.000000  1.590000e+05\n",
       "25%       2.000000     1.500000   1068.750000  98108.000000  6.017500e+05\n",
       "50%       3.000000     2.000000   1560.000000  98117.000000  8.000000e+05\n",
       "75%       4.000000     2.500000   2222.500000  98126.000000  1.105250e+06\n",
       "max      15.000000     9.000000  11010.000000  98199.000000  2.500000e+07"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfd9ca58-54fc-4a5c-92cd-29a218e5b5ce",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2016 entries, 0 to 2015\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   beds        2016 non-null   int64  \n",
      " 1   baths       2016 non-null   float64\n",
      " 2   size        2016 non-null   float64\n",
      " 3   size_units  2016 non-null   object \n",
      " 4   zip_code    2016 non-null   int64  \n",
      " 5   price       2016 non-null   float64\n",
      "dtypes: float64(3), int64(2), object(1)\n",
      "memory usage: 94.6+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5f21a4ba-f554-41ac-81d5-c8dcd4034f3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beds\n",
       "3     645\n",
       "2     560\n",
       "4     398\n",
       "1     256\n",
       "5     123\n",
       "6      22\n",
       "9       5\n",
       "7       3\n",
       "8       2\n",
       "15      1\n",
       "14      1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['beds'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cf333943-b6aa-4f7b-a6f8-3e6b4b112978",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>size</th>\n",
       "      <th>size_units</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2590.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98144</td>\n",
       "      <td>795000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98106</td>\n",
       "      <td>915000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98107</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98199</td>\n",
       "      <td>1950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98102</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beds  baths    size size_units  zip_code      price\n",
       "0     3    2.5  2590.0       sqft     98144   795000.0\n",
       "1     4    2.0  2240.0       sqft     98106   915000.0\n",
       "2     4    3.0  2040.0       sqft     98107   950000.0\n",
       "3     4    3.0  3800.0       sqft     98199  1950000.0\n",
       "4     2    2.0  1042.0       sqft     98102   950000.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "10ef0508-d4ad-4477-95fa-f2b61cdc3e48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       3.069498e+07\n",
       "1       4.084821e+07\n",
       "2       4.656863e+07\n",
       "3       5.131579e+07\n",
       "4       9.117083e+07\n",
       "            ...     \n",
       "2011    6.642336e+07\n",
       "2012    6.186727e+07\n",
       "2013    5.373832e+07\n",
       "2014    7.421384e+07\n",
       "2015    3.853801e+07\n",
       "Name: price_per_sqft, Length: 2016, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['price_per_sqft'] = data['price'] * 100000 / data['size']\n",
    "data['price_per_sqft']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2f8b0c6f-ce7d-4bc7-9786-7faeee06355b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>size</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>price</th>\n",
       "      <th>price_per_sqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2016.000000</td>\n",
       "      <td>2.016000e+03</td>\n",
       "      <td>2.016000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.857639</td>\n",
       "      <td>2.159970</td>\n",
       "      <td>1735.740575</td>\n",
       "      <td>98123.638889</td>\n",
       "      <td>9.636252e+05</td>\n",
       "      <td>5.915851e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.255092</td>\n",
       "      <td>1.002023</td>\n",
       "      <td>920.132591</td>\n",
       "      <td>22.650819</td>\n",
       "      <td>9.440954e+05</td>\n",
       "      <td>8.327952e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>98101.000000</td>\n",
       "      <td>1.590000e+05</td>\n",
       "      <td>6.796117e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1068.750000</td>\n",
       "      <td>98108.000000</td>\n",
       "      <td>6.017500e+05</td>\n",
       "      <td>4.452221e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1560.000000</td>\n",
       "      <td>98117.000000</td>\n",
       "      <td>8.000000e+05</td>\n",
       "      <td>5.529762e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>2222.500000</td>\n",
       "      <td>98126.000000</td>\n",
       "      <td>1.105250e+06</td>\n",
       "      <td>6.595389e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>11010.000000</td>\n",
       "      <td>98199.000000</td>\n",
       "      <td>2.500000e+07</td>\n",
       "      <td>3.424658e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              beds        baths          size      zip_code         price  \\\n",
       "count  2016.000000  2016.000000   2016.000000   2016.000000  2.016000e+03   \n",
       "mean      2.857639     2.159970   1735.740575  98123.638889  9.636252e+05   \n",
       "std       1.255092     1.002023    920.132591     22.650819  9.440954e+05   \n",
       "min       1.000000     0.500000    250.000000  98101.000000  1.590000e+05   \n",
       "25%       2.000000     1.500000   1068.750000  98108.000000  6.017500e+05   \n",
       "50%       3.000000     2.000000   1560.000000  98117.000000  8.000000e+05   \n",
       "75%       4.000000     2.500000   2222.500000  98126.000000  1.105250e+06   \n",
       "max      15.000000     9.000000  11010.000000  98199.000000  2.500000e+07   \n",
       "\n",
       "       price_per_sqft  \n",
       "count    2.016000e+03  \n",
       "mean     5.915851e+07  \n",
       "std      8.327952e+07  \n",
       "min      6.796117e+06  \n",
       "25%      4.452221e+07  \n",
       "50%      5.529762e+07  \n",
       "75%      6.595389e+07  \n",
       "max      3.424658e+09  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ef0c74a-9eae-473a-bf7d-ef450b806bd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2016, 7)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "02ce373d-424b-4a76-987d-8952766560c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beds</th>\n",
       "      <th>baths</th>\n",
       "      <th>size</th>\n",
       "      <th>size_units</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>price</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2590.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98144</td>\n",
       "      <td>795000.0</td>\n",
       "      <td>3.069498e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98106</td>\n",
       "      <td>915000.0</td>\n",
       "      <td>4.084821e+07</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
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       "      <td>98107</td>\n",
       "      <td>950000.0</td>\n",
       "      <td>4.656863e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98199</td>\n",
       "      <td>1950000.0</td>\n",
       "      <td>5.131579e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98102</td>\n",
       "      <td>950000.0</td>\n",
       "      <td>9.117083e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1370.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98112</td>\n",
       "      <td>910000.0</td>\n",
       "      <td>6.642336e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>889.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98121</td>\n",
       "      <td>550000.0</td>\n",
       "      <td>6.186727e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2140.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98199</td>\n",
       "      <td>1150000.0</td>\n",
       "      <td>5.373832e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>795.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98103</td>\n",
       "      <td>590000.0</td>\n",
       "      <td>7.421384e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1710.0</td>\n",
       "      <td>sqft</td>\n",
       "      <td>98133</td>\n",
       "      <td>659000.0</td>\n",
       "      <td>3.853801e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2016 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      beds  baths    size size_units  zip_code      price  price_per_sqft\n",
       "0        3    2.5  2590.0       sqft     98144   795000.0    3.069498e+07\n",
       "1        4    2.0  2240.0       sqft     98106   915000.0    4.084821e+07\n",
       "2        4    3.0  2040.0       sqft     98107   950000.0    4.656863e+07\n",
       "3        4    3.0  3800.0       sqft     98199  1950000.0    5.131579e+07\n",
       "4        2    2.0  1042.0       sqft     98102   950000.0    9.117083e+07\n",
       "...    ...    ...     ...        ...       ...        ...             ...\n",
       "2011     3    2.0  1370.0       sqft     98112   910000.0    6.642336e+07\n",
       "2012     1    1.0   889.0       sqft     98121   550000.0    6.186727e+07\n",
       "2013     4    2.0  2140.0       sqft     98199  1150000.0    5.373832e+07\n",
       "2014     2    2.0   795.0       sqft     98103   590000.0    7.421384e+07\n",
       "2015     3    2.0  1710.0       sqft     98133   659000.0    3.853801e+07\n",
       "\n",
       "[2016 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "85a24669-35fb-48b8-b983-048da08d67d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['size_units'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "760edee1-9c7b-4baf-bc2e-dd5c6c5bb6be",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(columns=['price_per_sqft'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87f46c0e-8997-4335-a254-3164a597bf97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>baths</th>\n",
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       "      <th>price</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2590.0</td>\n",
       "      <td>98144</td>\n",
       "      <td>795000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2240.0</td>\n",
       "      <td>98106</td>\n",
       "      <td>915000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>98107</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>98199</td>\n",
       "      <td>1950000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>98102</td>\n",
       "      <td>950000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beds  baths    size  zip_code      price\n",
       "0     3    2.5  2590.0     98144   795000.0\n",
       "1     4    2.0  2240.0     98106   915000.0\n",
       "2     4    3.0  2040.0     98107   950000.0\n",
       "3     4    3.0  3800.0     98199  1950000.0\n",
       "4     2    2.0  1042.0     98102   950000.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "773d3e98-d256-4d66-87f4-70759e24cb49",
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv(\"final_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4110ea0d-8682-4712-8955-6e51707eebd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=data.drop(columns=['price'])\n",
    "y=data['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d8e78881-446c-4285-a3dd-9a043902be65",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression,Lasso,Ridge\n",
    "from sklearn.preprocessing import OneHotEncoder,StandardScaler\n",
    "from sklearn.compose import make_column_transformer\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.metrics import r2_score\n",
    "from sklearn.preprocessing import LabelEncoder,OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "adf0fd8f-6293-489f-a289-db6f1153c4f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1612, 4)\n",
      "(1612,)\n"
     ]
    }
   ],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y, test_size=0.2, random_state=0)\n",
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "482caa50-9bd9-4f4d-aa91-5b17442dc259",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "OneHotEncoder.__init__() got an unexpected keyword argument 'sparse'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[24], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m column_trans \u001b[38;5;241m=\u001b[39m make_column_transformer((\u001b[43mOneHotEncoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43msparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m,[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbeds\u001b[39m\u001b[38;5;124m'\u001b[39m]),remainder\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mTypeError\u001b[0m: OneHotEncoder.__init__() got an unexpected keyword argument 'sparse'"
     ]
    }
   ],
   "source": [
    "column_trans = make_column_transformer((OneHotEncoder(sparse=False),['beds']),remainder='passthrough')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "470a6338-530a-4393-b9fd-5918ca2827cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3752a9eb-c4ff-4c8e-ba05-706e9a02217d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LinearRegression(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8ffd706-5108-4ba0-854f-033813f797bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "lr = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5826f4bb-db43-440a-ab75-ee3713fb9bc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# X being feature matrix\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(X)\n",
    "\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_scaled, y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdd81995-6e15-409c-8d47-783e58b7c9d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "pipe = make_pipeline(column_trans,scaler, lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5496323d-05ec-43f1-ae2c-f2a382c99bee",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "986e9b53-d18d-4ddf-bb11-15f4dba6587a",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline(steps=[('columntransformer',\n",
    "                 ColumnTransformer(remainder='passthrough',\n",
    "                                   transformers=[('onehotencoder',\n",
    "                                                  OneHotEncoder(sparse=False),\n",
    "                                                  ['beds'])])),\n",
    "                ('standardscaler', StandardScaler()),\n",
    "                ('linearregression', LinearRegression())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "076104ba-7db4-453d-b8c1-365cce8be093",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lr = pipe.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2b7ede7-619a-4e58-a1e7-fef3a6fd0a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6361437-1628-43c6-ac7e-a6a26418117a",
   "metadata": {},
   "outputs": [],
   "source": [
    "r2_score(y_test,y_pred_lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26efd67f-6789-4771-af6e-6de0975ae400",
   "metadata": {},
   "outputs": [],
   "source": [
    "lasso = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b552233-997c-4a12-a091-a0cedf2836ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = make_pipeline(column_trans,scaler, lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35592866-ba25-4188-bf98-42b849c1c5da",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8954cfc2-3d8f-4b89-8c92-777ae81d29e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import transformers\n",
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04ca019c-6e84-44a5-b8e9-eb2faddbd8d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline(steps=[('columntransformer',\n",
    "                 columnTransformer(remainder='passthrough',\n",
    "                                   transformers=[('onehotencoder',\n",
    "                                                  OneHotEncoder(sparse=False),\n",
    "                                                  ['beds'])])),\n",
    "                ('standardscaler', StandardScaler()), ('lasso', Lasso())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b9f0138-c7e5-4a26-a45a-a40c99741871",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lasso = pipe.predict(X_test)\n",
    "r2_score(y_test,y_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f65297af-5ca9-45d3-a589-5a1311e1fa91",
   "metadata": {},
   "outputs": [],
   "source": [
    "ridge = Ridge()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61c85c19-5d3c-4724-94ee-5e2c0318c6dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe = make_pipeline(column_trans,scaler, ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ba11145-9df0-4eed-bc3e-66e14cc4dcde",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2e776f1-1088-4e90-bde2-31e8b88bb96b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Pipeline(steps=[('columntransformer',\n",
    "                 ColumnTransformer(remainder='passthrough',\n",
    "                                   transformers=[('onehotencoder',\n",
    "                                                  OneHotEncoder(sparse=False),\n",
    "                                                  ['beds'])])),\n",
    "                ('standardscaler', StandardScaler()), ('ridge', Ridge())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78ebfedc-f2e9-4e3e-9ac7-2318bce85823",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_ridge = pipe.predict(X_test)\n",
    "r2_score(y_test,y_pred_ridge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf7bd073-e8ca-4a10-95f6-f8926ca7f16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f16f2542-370f-45d4-9e5b-1b13d0dcb75d",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"No Regularization: \", r2_score(y_test,y_pred_lr))\n",
    "print(\"Lasso: \", r2_score(y_test,y_pred_lasso))\n",
    "print(\"Ridge: \", r2_score(y_test,y_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "885668bd-6e5d-4913-a273-c57a06d1a691",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d8c46ec-28b9-4984-b50f-597622e0be4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(pipe, open('RidgeModel.pkl','wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f16d2b3a-98a9-4e9f-bf98-a490310fb6c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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