SF_Housing_v1_60%.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"SF_HSG_copy.csv\"\n",
    "\n",
    "df= pd.read_csv(filename, header=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Address</th>\n",
       "      <th>Price</th>\n",
       "      <th>bed</th>\n",
       "      <th>bath</th>\n",
       "      <th>Area</th>\n",
       "      <th>Zip</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1450 Post St Unit 1115</td>\n",
       "      <td>625000</td>\n",
       "      <td>2</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1067</td>\n",
       "      <td>94109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1450 Post St Unit 306</td>\n",
       "      <td>560000</td>\n",
       "      <td>1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1067</td>\n",
       "      <td>94109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>401 Harrison St Unit 41D</td>\n",
       "      <td>4500000</td>\n",
       "      <td>3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1949</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>564 Arch St</td>\n",
       "      <td>995000</td>\n",
       "      <td>3</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1266</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37 Turquoise Way</td>\n",
       "      <td>1600000</td>\n",
       "      <td>4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2200</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Address    Price   bed   bath   Area      Zip\n",
       "0    1450 Post St Unit 1115   625000     2    1.5   1067  94109.0\n",
       "1     1450 Post St Unit 306   560000     1    1.5   1067  94109.0\n",
       "2  401 Harrison St Unit 41D  4500000     3    3.0   1949      NaN\n",
       "3              564 Arch St    995000     3    2.0   1266      NaN\n",
       "4         37 Turquoise Way   1600000     4    2.5   2200      NaN"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.DataFrame(df)\n",
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "X = Data.iloc[:, 4:5]\n",
    "y = Data.iloc[:, 1:2]\n",
    "\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92, 1)"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_x_set = xtrain\n",
    "train_y_set = ytrain\n",
    "test_x_set = xtest\n",
    "test_y_set = ytest\n",
    "\n",
    "xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(92, 1)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 2815886179752.21\n",
      "Variance score: 0.60\n"
     ]
    },
    {
     "data": {
      "image/png": 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kXaiQEHPnevwtY4Tzguw3RU7XrZPLbdr4rh9GIiaG6Jln5GvrQqhERM2bEzVq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\ng8CCzDAMYxD+D4HPAIQdGNrJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21d6e17ec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "\n",
    "# Train the model using the training sets\n",
    "regr.fit(train_x_set, train_y_set)\n",
    "\n",
    "# Make predictions using the testing set\n",
    "Hsg_y_pred = regr.predict(test_x_set)\n",
    "\n",
    "# The coefficients\n",
    "#print('Coefficients: \\n', regr.coef_)\n",
    "# The mean squared error\n",
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(test_y_set, Hsg_y_pred))\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('Variance score: %.2f' % r2_score(test_y_set, Hsg_y_pred))\n",
    "\n",
    "# Plot outputs\n",
    "plt.scatter(test_x_set, test_y_set,  color='black')\n",
    "plt.plot(test_x_set, Hsg_y_pred, color='blue', linewidth=3)\n",
    "\n",
    "plt.xticks(())\n",
    "plt.yticks(())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}