Housing Prices _Jiying_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "#Importing Modules\n",
    "from pandas import DataFrame, Series\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import statsmodels.api as sm\n",
    "from sklearn.cross_validation import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Reading Data\n",
    "df = pd.read_csv('~/Documents/Kaggle/kaggle_fa16/datasets/houseprice/train.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RMSE --> after log-transformed\n",
    "\n",
    "log(x+1)\n",
    "\n",
    "to submit to kaggle, do e^x to get back original values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns that are not too pertinent: Street, Utilities, RoofMatl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring Lot Area Variable"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First we look at the entire graph of lot area vs sales price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2339ff4bd30>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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M3TmXPTxYuT1iS9a74svYnXMdyees3B6xJexV+DJ251xH8p6V2yOlpWOorX2EFSt+jc1Z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\nDbkAz8F6SoOBd4A7sZ7VB9i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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2339fb94710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot of Lot area vs Sales price\n",
    "plt.scatter(x = df['LotArea'], y = df['SalePrice'])\n",
    "plt.title('Lot Area vs Sale Price')\n",
    "plt.xlabel('Lot Area')\n",
    "plt.ylabel('Sale Price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Weed out outliers with a function and try graphing again"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "#Make a copy\n",
    "df_copy = df.copy(deep = True)\n",
    "\n",
    "#Method to delete outliers\n",
    "#Outlier defined as above one SD of the data\n",
    "#def reject_outliers(data):\n",
    "    #m = np.mean(data)\n",
    "    #sd = np.std(data)\n",
    "    #filtered = [n for n in data if (m - sd < n < m + sd)]\n",
    "    #return filtered     \n",
    "df_copy[np.abs(df_copy['LotArea']-np.mean(df_copy['LotArea']))<=(0.2 * np.std(df_copy['LotArea']))]\n",
    "for i in df_copy['LotArea']:\n",
    "    print(i>50000)\n",
    "#Apply to two variables\n",
    "#lot_area_fixed =reject_outliers(df2['LotArea'])\n",
    "#sale_price_fixed = reject_outliers(df2['SalePrice'])\n",
    "\n",
    "#Graph again\n",
    "#plt.scatter(x = df_copy['LotArea'], y = df_copy['SalePrice'])\n",
    "#plt.title('Lot Area vs Sale Price')\n",
    "#plt.xlabel('Lot Area')\n",
    "#plt.ylabel('Sale Price')\n",
    "#plt.xlim((0,250000))\n",
    "#plt.ylim((0,800000))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## OK that didn't work, let's try running a linear regression on it first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Pandas data cast to numpy dtype of object. Check input data with np.asarray(data).",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-c125475ce435>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mhouse_y\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SalePrice'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mhouse_x\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'SalePrice'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOLS\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhouse_y\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhouse_x\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\Jiying\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m    689\u001b[0m                  **kwargs):\n\u001b[0;32m    690\u001b[0m         super(OLS, self).__init__(endog, exog, missing=missing,\n\u001b[1;32m--> 691\u001b[1;33m                                   hasconst=hasconst, **kwargs)\n\u001b[0m\u001b[0;32m    692\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;34m\"weights\"\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_keys\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    693\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"weights\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jiying\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, weights, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m    584\u001b[0m             \u001b[0mweights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mweights\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    585\u001b[0m         super(WLS, self).__init__(endog, exog, missing=missing,\n\u001b[1;32m--> 586\u001b[1;33m                                   weights=weights, hasconst=hasconst, **kwargs)\n\u001b[0m\u001b[0;32m    587\u001b[0m         \u001b[0mnobs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    588\u001b[0m         \u001b[0mweights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mweights\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jiying\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[0;32m     89\u001b[0m     \"\"\"\n\u001b[0;32m     90\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 91\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mRegressionModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     92\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data_attr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'pinv_wexog'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wendog'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'wexog'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'weights'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     93\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jiying\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[0;32m    184\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    185\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 186\u001b[1;33m         \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLikelihoodModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    187\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitialize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jiying\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, **kwargs)\u001b[0m\n\u001b[0;32m     58\u001b[0m         \u001b[0mhasconst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'hasconst'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m         self.data = self._handle_data(endog, exog, missing, hasconst,\n\u001b[1;32m---> 60\u001b[1;33m                                       **kwargs)\n\u001b[0m\u001b[0;32m     61\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk_constant\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mk_constant\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexog\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jiying\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\model.py\u001b[0m in \u001b[0;36m_handle_data\u001b[1;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     83\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_handle_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhasconst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 84\u001b[1;33m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhandle_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmissing\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhasconst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     85\u001b[0m         \u001b[1;31m# kwargs arrays could have changed, easier to just attach here\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     86\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jiying\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\data.py\u001b[0m in \u001b[0;36mhandle_data\u001b[1;34m(endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m    564\u001b[0m     \u001b[0mklass\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mhandle_data_class_factory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    565\u001b[0m     return klass(endog, exog=exog, missing=missing, hasconst=hasconst,\n\u001b[1;32m--> 566\u001b[1;33m                  **kwargs)\n\u001b[0m",
      "\u001b[1;32mC:\\Users\\Jiying\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\data.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, endog, exog, missing, hasconst, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0morig_endog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mendog\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     71\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0morig_exog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_endog_exog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m         \u001b[1;31m# this has side-effects, attaches k_constant and const_idx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Jiying\\Anaconda3\\lib\\site-packages\\statsmodels\\base\\data.py\u001b[0m in \u001b[0;36m_convert_endog_exog\u001b[1;34m(self, endog, exog)\u001b[0m\n\u001b[0;32m    426\u001b[0m         \u001b[0mexog\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mexog\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mexog\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    427\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mendog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mobject\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mexog\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 428\u001b[1;33m             raise ValueError(\"Pandas data cast to numpy dtype of object. \"\n\u001b[0m\u001b[0;32m    429\u001b[0m                              \"Check input data with np.asarray(data).\")\n\u001b[0;32m    430\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPandasData\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_endog_exog\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mendog\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mexog\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Pandas data cast to numpy dtype of object. Check input data with np.asarray(data)."
     ]
    }
   ],
   "source": [
    "house_y = df['SalePrice']\n",
    "house_x = df.drop(['SalePrice'], axis = 1)\n",
    "result = sm.OLS(house_y, house_x).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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