7.4 Regression.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.4</td>\n",
       "      <td>6600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.5</td>\n",
       "      <td>10100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.3</td>\n",
       "      <td>7300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.5</td>\n",
       "      <td>6100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  salary\n",
       "1   2.4    6600\n",
       "2   5.5   10100\n",
       "3   3.3    7300\n",
       "4   0.2    5000\n",
       "5   1.5    6100"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv('data/salary.csv', index_col = 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "X = df[['year']]\n",
    "Y = df['salary'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x9733a20>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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58x71q4CHFJtZFbmlYmZmTbmlUmHeG97MepnnqXSQ55aYWa9zS6WDPLfEzHqd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\ns9w4qJiZWW7+PzwQ++vjfceCAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x97f1390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% pylab inline\n",
    "plt.scatter(X,Y, color=\"black\")\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regr = LinearRegression()\n",
    "regr.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient:[ 712.59413615]\n",
      "Intercept:5049.009899813836\n"
     ]
    }
   ],
   "source": [
    "print('Coefficient:{}'.format(regr.coef_) )\n",
    "print('Intercept:{}'.format(regr.intercept_) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#regr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xc0ae898>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RXvNblcSdxBllrs1M0CA5lVcBTxC0OO4GXhuWXwc8A/wb8J6wrAX4IsEIsX8F3hSWHwnc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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc3150f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,Y, color=\"black\")\n",
    "plt.plot(X, regr.predict(X), linewidth = 3, color = \"blue\")\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('salary')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "poly_reg= PolynomialFeatures(degree = 2)\n",
    "X_ = poly_reg.fit_transform(X)\n",
    "regr = LinearRegression()\n",
    "regr.fit(X_, Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X2 = X.sort_values(['year'])\n",
    "X2_ = poly_reg.fit_transform(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xc4d1c88>]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rVq2io6ODQCAQ8Rzb8cyY/GFJoR95+mlXE1i92j0uLIQ774R582DgwOSvb7OO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\nCsYYY0IsKRhjjAn5/+G/jjYqehZuAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc4d1d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X,Y, color=\"black\")\n",
    "plt.plot(X2, regr.predict(X2_), color= \"blue\", linewidth = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv('data/house-prices.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Home</th>\n",
       "      <th>Price</th>\n",
       "      <th>SqFt</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Offers</th>\n",
       "      <th>Brick</th>\n",
       "      <th>Neighborhood</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>114300</td>\n",
       "      <td>1790</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>No</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>114200</td>\n",
       "      <td>2030</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>114800</td>\n",
       "      <td>1740</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>94700</td>\n",
       "      <td>1980</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>119800</td>\n",
       "      <td>2130</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>No</td>\n",
       "      <td>East</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Home   Price  SqFt  Bedrooms  Bathrooms  Offers Brick Neighborhood\n",
       "0     1  114300  1790         2          2       2    No         East\n",
       "1     2  114200  2030         4          2       3    No         East\n",
       "2     3  114800  1740         3          2       1    No         East\n",
       "3     4   94700  1980         3          2       3    No         East\n",
       "4     5  119800  2130         3          3       3    No         East"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#pandas.get_dummies(df['Brick'])\n",
    "#pandas.get_dummies(df['Neighborhood'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "      <th>SqFt</th>\n",
       "      <th>Bedrooms</th>\n",
       "      <th>Bathrooms</th>\n",
       "      <th>Offers</th>\n",
       "      <th>Yes</th>\n",
       "      <th>East</th>\n",
       "      <th>North</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>114300</td>\n",
       "      <td>1790</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>114200</td>\n",
       "      <td>2030</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>114800</td>\n",
       "      <td>1740</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>94700</td>\n",
       "      <td>1980</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>119800</td>\n",
       "      <td>2130</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Price  SqFt  Bedrooms  Bathrooms  Offers  Yes  East  North\n",
       "0  114300  1790         2          2       2    0     1      0\n",
       "1  114200  2030         4          2       3    0     1      0\n",
       "2  114800  1740         3          2       1    0     1      0\n",
       "3   94700  1980         3          2       3    0     1      0\n",
       "4  119800  2130         3          3       3    0     1      0"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house = pandas.concat([df, pandas.get_dummies(df['Brick']), pandas.get_dummies(df['Neighborhood'])], axis = 1)\n",
    "del house['No']\n",
    "del house['West']\n",
    "del house['Brick']\n",
    "del house['Neighborhood']\n",
    "del house['Home']\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = house[['SqFt', 'Bedrooms', 'Bathrooms', 'Offers', 'Yes', 'East', 'North']]\n",
    "Y = house['Price'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "regr = LinearRegression()\n",
    "regr.fit(X,Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#regr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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