1_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 style=\"color:green\" align=\"center\">Machine Learning With Python: Linear Regression With One Variable</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:purple\">Sample problem of predicting home price in monroe, new jersey (USA)</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below table represents current home prices in monroe township based on square feet area, new jersey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"homepricetable.JPG\" style=\"width:370px;height:250px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Problem Statement**: Given above data build a machine learning model that can predict home prices based on square feet area\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can represent values in above table as a scatter plot (values are shown in red markers). After that one can draw a straight line that best fits values on chart. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"scatterplot.JPG\" style=\"width:600px;height:370px\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can draw multiple lines like this but we choose the one where total sum of error is minimum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"equation.PNG\" style=\"width:600px;height:370px\" >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might remember about linear equation from your high school days math class. Home prices can be presented as following equation,\n",
    "\n",
    "home price = m * (area) + b\n",
    "\n",
    "Generic form of same equation is,"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"linear_equation.png\" >"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "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": [
    "df = pd.read_csv('homeprices.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1fa581927f0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SI72M9mpMAo4BLrb9HuAZ4NzOTklfBHYCV5fQVuBg23OAzwPflbRvxfywfantlu1WT09P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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa589c1d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel('area')\n",
    "plt.ylabel('price')\n",
    "plt.scatter(df.area,df.price,color='red',marker='+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>2600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "0  2600\n",
       "1  3000\n",
       "2  3200\n",
       "3  3600\n",
       "4  4000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area = df[['area']]\n",
    "area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    550000\n",
       "1    565000\n",
       "2    610000\n",
       "3    680000\n",
       "4    725000\n",
       "Name: price, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price = df.price\n",
    "price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create linear regression object\n",
    "reg = linear_model.LinearRegression()\n",
    "reg.fit(area,price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 587979.45205479])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.predict([[3000]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 135.78767123])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180616.43835616432"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Y = m * X + b (m is coefficient and b is intercept)**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "859554.7945061643"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "5000*135.78767123 + 180616.43835616432"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 style=\"color:purple\">Generate CSV file with list of home price predictions</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area\n",
       "0  1000\n",
       "1  1500\n",
       "2  2300"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_df = pd.read_csv(\"areas.csv\")\n",
    "area_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = reg.predict(area_df)\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>prices</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        prices\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": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area_df['prices']=p\n",
    "area_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "area_df.to_csv(\"prediction.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1fa584aea58>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2AuYC10l6StLV+dmmr0ZE9WwQb5CGlAN0AmaWtJ+Vyzrl/ZrlS7SJiIWkZTLW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\nJDMzKwtOSGZmVhackMzMrCz8f6c1uSgDW+CnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fa587d85f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.xlabel('area', fontsize=20)\n",
    "plt.ylabel('price', fontsize=20)\n",
    "plt.scatter(df.area,df.price,color='red',marker='+')\n",
    "plt.plot(df.area,reg.predict(area),color='blue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Exercise</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p >Predict canada's per capita income in year 2020. There is an exercise folder here on github at same level as this notebook, download that and you will find canada_per_capita_income.csv file. Using this build a regression model and predict the per capita income fo canadian citizens in year 2020</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Answer</h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "41288.69409442"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}