MULTILINEAR_REGRESSION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bcbff42",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "afe5e2c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('homeprices_2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4891c31e",
   "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</td>\n",
       "      <td>20</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>4</td>\n",
       "      <td>15</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>3</td>\n",
       "      <td>30</td>\n",
       "      <td>595000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>760000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4100</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>810000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  bedrooms  age   price\n",
       "0  2600         3   20  550000\n",
       "1  3000         4   15  565000\n",
       "2  3200         3   18  610000\n",
       "3  3600         3   30  595000\n",
       "4  4000         5    8  760000\n",
       "5  4100         6    8  810000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "53affbc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "area        0\n",
       "bedrooms    0\n",
       "age         0\n",
       "price       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "81c14273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['area'],df['price'],color='red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "203bfcef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['age'],df['price'],color='green')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8fe7d4d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['bedrooms'],df['price'],color='orange') #phle independent aayega phir dependent variable aayega\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c3496744",
   "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>age</th>\n",
       "      <th>bedrooms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>20</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4100</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  age  bedrooms\n",
       "0  2600   20         3\n",
       "1  3000   15         4\n",
       "2  3200   18         3\n",
       "3  3600   30         3\n",
       "4  4000    8         5\n",
       "5  4100    8         6"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#independent variable denoted by x\n",
    "x=df[['area','age','bedrooms']] #IV always atored as 2D\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "816d85be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    550000\n",
       "1    565000\n",
       "2    610000\n",
       "3    595000\n",
       "4    760000\n",
       "5    810000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if there are many IV in bigger dataset then take only necessary columns as independent columns that will really affect DV\n",
    "#dependent variable denoted by y\n",
    "y=df['price']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ffd3147a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "feef3647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 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-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 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-2 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-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 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-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 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-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 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-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" 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-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" 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": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x,y)#it will calc coeff and intercept "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5213b9d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9511608968605542"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "y_pred=model.predict(x)\n",
    "score=r2_score(y_pred,y)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3574ee39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  119.67905405, -4207.28764479, 13097.24903475])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_#gives value of m1 m2 and m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7f0c0b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256461.14864864893"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1edcb3d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([995793.91891892])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict([[3000,3,30]])#area,no of bedrooms,age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "77f4cb8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Area :3000\n",
      "Bedrooms :3\n",
      "Age of the house30\n",
      "the price of the house is : [995793.91891892]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\sklearn\\base.py:464: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "area=int(input('Area :'))#dynamically values are given\n",
    "broom=int(input('Bedrooms :'))\n",
    "age=int(input('Age of the house'))\n",
    "result=model.predict([[area,broom,age]])\n",
    "print('the price of the house is :',result)"
   ]
  }
 ],
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