Linear Regression Single Variable.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing required packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2600</td>\n",
       "      <td>550000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "      <td>565000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "      <td>610000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "      <td>680000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "      <td>725000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area   price\n",
       "0  2600  550000\n",
       "1  3000  565000\n",
       "2  3200  610000\n",
       "3  3600  680000\n",
       "4  4000  725000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reading the csv\n",
    "df=pd.read_csv('homeprices.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x26a3af25dd8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "# data scatter plot to see the distribution of data\n",
    "plt.xlabel('Area(Sqr ft)')\n",
    "plt.ylabel('Prices(USD$)')\n",
    "plt.scatter(df.area,df.price,color='blue',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# training the dataset\n",
    "reg=linear_model.LinearRegression()\n",
    "reg.fit(df[['area']],df.price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([628715.75342466])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " reg.predict(3300)\n",
    "    #price=m*area+b\n",
    "    #y=mx+b\n",
    "    #m is slope (or Gradient)\n",
    "    #Y is intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([135.78767123])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# coefficient value (Slope)\n",
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#intercept value\n",
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "628715.7534151643"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y=m*x+b\n",
    "\n",
    "# y= 135.78767123*3300+180616.43835616432\n",
    "135.78767123*3300+180616.43835616432"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So y=628715.7534151643 and its predicted the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([520085.61643836])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#similarly for for area 2500\n",
    "reg.predict(2500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([135.78767123])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "520085.6164311643"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y=m*x+b\n",
    "\n",
    "# y= 135.78767123 *2500+180616.43835616432\n",
    "135.78767123 *2500+180616.43835616432"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Similarly will predict for bulk of areas(Sq Ft) in that location \n",
    "# we have created the list of Areas in file areas.csv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "0  1000\n",
       "1  1500\n",
       "2  2300\n",
       "3  3540\n",
       "4  4120"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=pd.read_csv('areas.csv')\n",
    "d.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 316404.10958904,  384297.94520548,  492928.08219178,\n",
       "        661304.79452055,  740061.64383562,  799808.21917808,\n",
       "        926090.75342466,  650441.78082192,  825607.87671233,\n",
       "        492928.08219178, 1402705.47945205, 1348390.4109589 ,\n",
       "       1144708.90410959])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict(d)\n",
    "# this is bit confusing to read"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# will create the new column(predictedprice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>predictedprice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>3.164041e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "      <td>3.842979e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "      <td>4.929281e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3540</td>\n",
       "      <td>6.613048e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "      <td>7.400616e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4560</td>\n",
       "      <td>7.998082e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5490</td>\n",
       "      <td>9.260908e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3460</td>\n",
       "      <td>6.504418e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4750</td>\n",
       "      <td>8.256079e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2300</td>\n",
       "      <td>4.929281e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9000</td>\n",
       "      <td>1.402705e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8600</td>\n",
       "      <td>1.348390e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>7100</td>\n",
       "      <td>1.144709e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    area  predictedprice\n",
       "0   1000    3.164041e+05\n",
       "1   1500    3.842979e+05\n",
       "2   2300    4.929281e+05\n",
       "3   3540    6.613048e+05\n",
       "4   4120    7.400616e+05\n",
       "5   4560    7.998082e+05\n",
       "6   5490    9.260908e+05\n",
       "7   3460    6.504418e+05\n",
       "8   4750    8.256079e+05\n",
       "9   2300    4.929281e+05\n",
       "10  9000    1.402705e+06\n",
       "11  8600    1.348390e+06\n",
       "12  7100    1.144709e+06"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p=reg.predict(d)\n",
    "d['predictedprice']=p\n",
    "d\n",
    "#however this is not permanent to file. its just display on screen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#to save the new predictedprice column in new csv file.\n",
    "d.to_csv(\"predictedprice.csv\",index=False)\n",
    "#index=False is to disable extra the column numbers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>predictedprice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>316404.109589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1500</td>\n",
       "      <td>384297.945205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2300</td>\n",
       "      <td>492928.082192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3540</td>\n",
       "      <td>661304.794521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4120</td>\n",
       "      <td>740061.643836</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  predictedprice\n",
       "0  1000   316404.109589\n",
       "1  1500   384297.945205\n",
       "2  2300   492928.082192\n",
       "3  3540   661304.794521\n",
       "4  4120   740061.643836"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p2=pd.read_csv('predictedprice.csv')\n",
    "p2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x26a3b2962e8>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
    "#lets Drow the best fit(prediction) line of original data\n",
    "plt.xlabel('Area(Sqr ft)')\n",
    "plt.ylabel('Prices(USD$)')\n",
    "plt.scatter(df.area,df.price ,color='red',marker='+')\n",
    "plt.plot(df.area,reg.predict(df[['area']]),color='blue')"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}