house prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "25a6cee2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8174a64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  Load data set \n",
    "df  = pd.read_csv('minihomeprices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1cb5ce25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30</td>\n",
       "      <td>595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>760000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  bedrooms  age   price\n",
       "0  2600       3.0   20  550000\n",
       "1  3000       4.0   15  565000\n",
       "2  3200       NaN   18  610000\n",
       "3  3600       3.0   30  595000\n",
       "4  4000       5.0    8  760000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show first five rows\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7ed7acaa",
   "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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>age</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>20</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>4.2</td>\n",
       "      <td>18</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30</td>\n",
       "      <td>595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8</td>\n",
       "      <td>760000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  bedrooms  age   price\n",
       "0  2600       3.0   20  550000\n",
       "1  3000       4.0   15  565000\n",
       "2  3200       4.2   18  610000\n",
       "3  3600       3.0   30  595000\n",
       "4  4000       5.0    8  760000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fill null values with median value\n",
    "df['bedrooms'] = df['bedrooms'].fillna( df['bedrooms'].mean() )\n",
    "\n",
    "df.headad()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a5b2a4bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shash\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "sns.scatterplot('bedrooms', 'price',data=df)\n",
    "plt.title(\"Price vs Bedroom Scatter plot\")\n",
    "\n",
    "plt.xlabel(\"House Bedrooms\")\n",
    "plt.ylabel('House Price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "631f43b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lmplot\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "sns.lmplot(x=\"bedrooms\", y=\"price\", data=df);\n",
    "plt.title(\"Price and bedroom wise line plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "875382aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPMENTING MULTIPLE REGRESSION MODEL\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8422500d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdl = LinearRegression()\n",
    "\n",
    "#  Set dependent and independent variables\n",
    "\n",
    "X = df.drop(['price'], axis=1)\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "81e53e60",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['bedrooms'] = df['bedrooms'].astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "03203cf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Fitting Model\n",
    "mdl.fit( X, y  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c08c9c70",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shash\\anaconda3\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
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     "data": {
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       "array([551989.04870256])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PREDICTION\n",
    "mdl.predict([[ 4000, 2, 50 ]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1ced3e8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  116.66950551 18756.28806982 -3675.75111708]\n",
      "231586.00639409182\n"
     ]
    }
   ],
   "source": [
    "print(mdl.coef_)\n",
    "print(mdl.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3d6e457f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95.40926625396438\n"
     ]
    }
   ],
   "source": [
    "#  know score \n",
    "score = mdl.score( X, y )\n",
    "\n",
    "print(score * 100)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "ffaae075",
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   "outputs": [],
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   "id": "bf484e41",
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   "cell_type": "code",
   "execution_count": null,
   "id": "5a2b8ab7",
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   "outputs": [],
   "source": []
  }
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