Simple Linear Regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  # importing pandas library to fetch the csv data\n",
    "import numpy as np # Numpy is an array\n",
    "import matplotlib.pyplot as plt # Matplotlib has three basic layers ---1: back-end layer, 2:Artist Layer, 3:Scripting Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'housedata.csv'\n",
    "df = pd.read_csv(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 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",
       "\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",
       "\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",
       "\n",
       "   sqft_lot15  \n",
       "0        5650  \n",
       "1        7639  \n",
       "2        8062  \n",
       "3        5000  \n",
       "4        7503  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[['sqft_living','price']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1180</td>\n",
       "      <td>221900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2570</td>\n",
       "      <td>538000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>770</td>\n",
       "      <td>180000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1960</td>\n",
       "      <td>604000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1680</td>\n",
       "      <td>510000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sqft_living     price\n",
       "0         1180  221900.0\n",
       "1         2570  538000.0\n",
       "2          770  180000.0\n",
       "3         1960  604000.0\n",
       "4         1680  510000.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "# It is the magic function \n",
    "plt.title('Scatter plot between area and price')\n",
    "plt.xlabel('Area')\n",
    "plt.ylabel('Price')\n",
    "plt.scatter(df1['sqft_living'],df1['price'],color='green',marker='*')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will need to import two classes from sklearn library\n",
    "from sklearn import linear_model # we will create a object of linear_model class to tarin our model\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(df1[['sqft_living']],df1[['price']],test_size=0.30,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = linear_model.LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 360116.30871034],\n",
       "       [1261900.99358095],\n",
       "       [ 362899.59477476],\n",
       "       ...,\n",
       "       [ 560512.90534826],\n",
       "       [ 374032.73903242],\n",
       "       [ 329500.16200177]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": [
    "%matplotlib inline\n",
    "plt.scatter(x_train,y_train,color='red',marker='*')\n",
    "plt.plot(x_train,clf.predict(x_train))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.48889929019453016"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "intc = clf.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef = clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[290534.15709996]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y = b0 + b1x   b0-->intercept\n",
    "intc + coef*1180"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[290534.15709996]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[1180]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}