3_housing_analysis/project-3_housing_analyis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://imgur.com/1ZcRyrc.png\" style=\"float: left; margin: 15px; height: 80px\">\n",
    "\n",
    "# Project 3\n",
    "\n",
    "### Regression and Classification with the Ames Housing Data\n",
    "\n",
    "---\n",
    "\n",
    "You have just joined a new \"full stack\" real estate company in Ames, Iowa. The strategy of the firm is two-fold:\n",
    "- Own the entire process from the purchase of the land all the way to sale of the house, and anything in between.\n",
    "- Use statistical analysis to optimize investment and maximize return.\n",
    "\n",
    "The company is still small, and though investment is substantial the short-term goals of the company are more oriented towards purchasing existing houses and flipping them as opposed to constructing entirely new houses. That being said, the company has access to a large construction workforce operating at rock-bottom prices.\n",
    "\n",
    "This project uses the [Ames housing data recently made available on kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "#%config InlineBackend.figure_format = 'retina'\n",
    "#%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://imgur.com/l5NasQj.png\" style=\"float: left; margin: 25px 15px 0px 0px; height: 25px\">\n",
    "\n",
    "## 1. Estimating the value of homes from fixed characteristics.\n",
    "\n",
    "---\n",
    "\n",
    "Your superiors have outlined this year's strategy for the company:\n",
    "1. Develop an algorithm to reliably estimate the value of residential houses based on *fixed* characteristics.\n",
    "2. Identify characteristics of houses that the company can cost-effectively change/renovate with their construction team.\n",
    "3. Evaluate the mean dollar value of different renovations.\n",
    "\n",
    "Then we can use that to buy houses that are likely to sell for more than the cost of the purchase plus renovations.\n",
    "\n",
    "Your first job is to tackle #1. You have a dataset of housing sale data with a huge amount of features identifying different aspects of the house. The full description of the data features can be found in a separate file:\n",
    "\n",
    "    housing.csv\n",
    "    data_description.txt\n",
    "    \n",
    "You need to build a reliable estimator for the price of the house given characteristics of the house that cannot be renovated. Some examples include:\n",
    "- The neighborhood\n",
    "- Square feet\n",
    "- Bedrooms, bathrooms\n",
    "- Basement and garage space\n",
    "\n",
    "and many more. \n",
    "\n",
    "Some examples of things that **ARE renovate-able:**\n",
    "- Roof and exterior features\n",
    "- \"Quality\" metrics, such as kitchen quality\n",
    "- \"Condition\" metrics, such as condition of garage\n",
    "- Heating and electrical components\n",
    "\n",
    "and generally anything you deem can be modified without having to undergo major construction on the house.\n",
    "\n",
    "---\n",
    "\n",
    "**Your goals:**\n",
    "1. Perform any cleaning, feature engineering, and EDA you deem necessary.\n",
    "- Be sure to remove any houses that are not residential from the dataset.\n",
    "- Identify **fixed** features that can predict price.\n",
    "- Train a model on pre-2010 data and evaluate its performance on the 2010 houses.\n",
    "- Characterize your model. How well does it perform? What are the best estimates of price?\n",
    "\n",
    "> **Note:** The EDA and feature engineering component to this project is not trivial! Be sure to always think critically and creatively. Justify your actions! Use the data description file!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Load the data\n",
    "house = pd.read_csv('/Users/gopetro/documents/dsi/materials/project3/housing.csv')\n",
    "\n",
    "\n",
    "#Jeff is running python 2.7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. View, clean and conduct EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities    ...     PoolArea PoolQC Fence MiscFeature MiscVal  \\\n",
       "0         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "1         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "2         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "3         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "4         Lvl    AllPub    ...            0    NaN   NaN         NaN       0   \n",
       "\n",
       "  MoSold YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0      2   2008        WD         Normal     208500  \n",
       "1      5   2007        WD         Normal     181500  \n",
       "2      9   2008        WD         Normal     223500  \n",
       "3      2   2006        WD        Abnorml     140000  \n",
       "4     12   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's have quick look at header.\n",
    "house.head()\n",
    "#Noticed the following:\n",
    "#NaN values, repeating columns, Id column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Drop Id column\n",
    "house = house.drop('Id', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 80)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check number of rows and columns\n",
    "house.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['RL', 'RM', 'C (all)', 'FV', 'RH'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check unique values in MSZoning and remove rows that are not residential\n",
    "house.MSZoning.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#This steps answers Question #2. \n",
    "#Removing non residential houses\n",
    "\n",
    "#Only include residential houses\n",
    "#Use 'MSZoning' as filter\n",
    "house = house[(house.MSZoning != 'C (all)')] #Removes rows classified as Commercial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1450, 80)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check shape to confirm rows removed\n",
    "house.shape #This removed 10 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Check the number of different values in a column. If a column has less than 5 unique values then drop column due to lack of variance\n",
    "house.Street.nunique() # 2 unique values in entire column\n",
    "house.LandContour.nunique() # 4 unique values in entire column\n",
    "house.Utilities.nunique() # 2 unique values in entire column\n",
    "house.LandSlope.nunique() # 3 unique values in entire column\n",
    "house.PoolQC.nunique() # 3 unique values in entire column\n",
    "house.BsmtCond.nunique() # 4 unique values in entire column\n",
    "\n",
    "house = house.drop(['Street','LandContour', 'Utilities', 'LandSlope','PoolQC','BsmtCond']\n",
    "                   , axis=1)\n",
    "\n",
    "#The following columns were checked but not dropped\n",
    "#house.PoolArea.nunique() # 8 unique values in entire column\n",
    "#house.RoofMatl.nunique() # 8 unique values in entire column\n",
    "#house.ExterCond.nunique() # 5 unique values in entire column\n",
    "#house.Heating.nunique() # 6 unique values in entire column\n",
    "#house.PoolArea.nunique()  # 8 unique values in entire column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MSSubClass',\n",
       " 'MSZoning',\n",
       " 'LotFrontage',\n",
       " 'LotArea',\n",
       " 'Alley',\n",
       " 'LotShape',\n",
       " 'LotConfig',\n",
       " 'Neighborhood',\n",
       " 'Condition1',\n",
       " 'Condition2',\n",
       " 'BldgType',\n",
       " 'HouseStyle',\n",
       " 'OverallQual',\n",
       " 'OverallCond',\n",
       " 'YearBuilt',\n",
       " 'YearRemodAdd',\n",
       " 'RoofStyle',\n",
       " 'RoofMatl',\n",
       " 'Exterior1st',\n",
       " 'Exterior2nd',\n",
       " 'MasVnrType',\n",
       " 'MasVnrArea',\n",
       " 'ExterQual',\n",
       " 'ExterCond',\n",
       " 'Foundation',\n",
       " 'BsmtQual',\n",
       " 'BsmtExposure',\n",
       " 'BsmtFinType1',\n",
       " 'BsmtFinSF1',\n",
       " 'BsmtFinType2',\n",
       " 'BsmtFinSF2',\n",
       " 'BsmtUnfSF',\n",
       " 'TotalBsmtSF',\n",
       " 'Heating',\n",
       " 'HeatingQC',\n",
       " 'CentralAir',\n",
       " 'Electrical',\n",
       " '1stFlrSF',\n",
       " '2ndFlrSF',\n",
       " 'LowQualFinSF',\n",
       " 'GrLivArea',\n",
       " 'BsmtFullBath',\n",
       " 'BsmtHalfBath',\n",
       " 'FullBath',\n",
       " 'HalfBath',\n",
       " 'BedroomAbvGr',\n",
       " 'KitchenAbvGr',\n",
       " 'KitchenQual',\n",
       " 'TotRmsAbvGrd',\n",
       " 'Functional',\n",
       " 'Fireplaces',\n",
       " 'FireplaceQu',\n",
       " 'GarageType',\n",
       " 'GarageYrBlt',\n",
       " 'GarageFinish',\n",
       " 'GarageCars',\n",
       " 'GarageArea',\n",
       " 'GarageQual',\n",
       " 'GarageCond',\n",
       " 'PavedDrive',\n",
       " 'WoodDeckSF',\n",
       " 'OpenPorchSF',\n",
       " 'EnclosedPorch',\n",
       " '3SsnPorch',\n",
       " 'ScreenPorch',\n",
       " 'PoolArea',\n",
       " 'Fence',\n",
       " 'MiscFeature',\n",
       " 'MiscVal',\n",
       " 'MoSold',\n",
       " 'YrSold',\n",
       " 'SaleType',\n",
       " 'SaleCondition',\n",
       " 'SalePrice']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View header information to see if any unusual charcters\n",
    "list(house)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSSubClass         int64\n",
      "MSZoning          object\n",
      "LotFrontage      float64\n",
      "LotArea            int64\n",
      "Alley             object\n",
      "LotShape          object\n",
      "LotConfig         object\n",
      "Neighborhood      object\n",
      "Condition1        object\n",
      "Condition2        object\n",
      "BldgType          object\n",
      "HouseStyle        object\n",
      "OverallQual        int64\n",
      "OverallCond        int64\n",
      "YearBuilt          int64\n",
      "YearRemodAdd       int64\n",
      "RoofStyle         object\n",
      "RoofMatl          object\n",
      "Exterior1st       object\n",
      "Exterior2nd       object\n",
      "MasVnrType        object\n",
      "MasVnrArea       float64\n",
      "ExterQual         object\n",
      "ExterCond         object\n",
      "Foundation        object\n",
      "BsmtQual          object\n",
      "BsmtExposure      object\n",
      "BsmtFinType1      object\n",
      "BsmtFinSF1         int64\n",
      "BsmtFinType2      object\n",
      "                  ...   \n",
      "HalfBath           int64\n",
      "BedroomAbvGr       int64\n",
      "KitchenAbvGr       int64\n",
      "KitchenQual       object\n",
      "TotRmsAbvGrd       int64\n",
      "Functional        object\n",
      "Fireplaces         int64\n",
      "FireplaceQu       object\n",
      "GarageType        object\n",
      "GarageYrBlt      float64\n",
      "GarageFinish      object\n",
      "GarageCars         int64\n",
      "GarageArea         int64\n",
      "GarageQual        object\n",
      "GarageCond        object\n",
      "PavedDrive        object\n",
      "WoodDeckSF         int64\n",
      "OpenPorchSF        int64\n",
      "EnclosedPorch      int64\n",
      "3SsnPorch          int64\n",
      "ScreenPorch        int64\n",
      "PoolArea           int64\n",
      "Fence             object\n",
      "MiscFeature       object\n",
      "MiscVal            int64\n",
      "MoSold             int64\n",
      "YrSold             int64\n",
      "SaleType          object\n",
      "SaleCondition     object\n",
      "SalePrice          int64\n",
      "dtype: object\n",
      "(1450, 74)\n"
     ]
    }
   ],
   "source": [
    "#View object types\n",
    "print house.dtypes\n",
    "#View shape\n",
    "print house.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass          0\n",
       "MSZoning            0\n",
       "LotFrontage       259\n",
       "LotArea             0\n",
       "Alley            1361\n",
       "LotShape            0\n",
       "LotConfig           0\n",
       "Neighborhood        0\n",
       "Condition1          0\n",
       "Condition2          0\n",
       "BldgType            0\n",
       "HouseStyle          0\n",
       "OverallQual         0\n",
       "OverallCond         0\n",
       "YearBuilt           0\n",
       "YearRemodAdd        0\n",
       "RoofStyle           0\n",
       "RoofMatl            0\n",
       "Exterior1st         0\n",
       "Exterior2nd         0\n",
       "MasVnrType          8\n",
       "MasVnrArea          8\n",
       "ExterQual           0\n",
       "ExterCond           0\n",
       "Foundation          0\n",
       "BsmtQual           37\n",
       "BsmtExposure       38\n",
       "BsmtFinType1       37\n",
       "BsmtFinSF1          0\n",
       "BsmtFinType2       38\n",
       "                 ... \n",
       "HalfBath            0\n",
       "BedroomAbvGr        0\n",
       "KitchenAbvGr        0\n",
       "KitchenQual         0\n",
       "TotRmsAbvGrd        0\n",
       "Functional          0\n",
       "Fireplaces          0\n",
       "FireplaceQu       681\n",
       "GarageType         79\n",
       "GarageYrBlt        79\n",
       "GarageFinish       79\n",
       "GarageCars          0\n",
       "GarageArea          0\n",
       "GarageQual         79\n",
       "GarageCond         79\n",
       "PavedDrive          0\n",
       "WoodDeckSF          0\n",
       "OpenPorchSF         0\n",
       "EnclosedPorch       0\n",
       "3SsnPorch           0\n",
       "ScreenPorch         0\n",
       "PoolArea            0\n",
       "Fence            1172\n",
       "MiscFeature      1398\n",
       "MiscVal             0\n",
       "MoSold              0\n",
       "YrSold              0\n",
       "SaleType            0\n",
       "SaleCondition       0\n",
       "SalePrice           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#After comparing the null values to the data_descriptions.txt file it appears that the term 'NAn' is not null.\n",
    "#The term NAn refers to something else. See below:\n",
    "\n",
    "#Alley: NA = No alley access\n",
    "#BsmtQual: NA = No Basement\n",
    "#BsmtCond: NA = No Basement\n",
    "#BsmtFinType1: NA = No Basement\n",
    "#BsmtFinType2: NA = No Basement\n",
    "#GarageType: NA = No Garage\n",
    "#GarageFinish: NA = No Garage\n",
    "#GarageQual: NA = No Garage\n",
    "#GarageCond: NA = No Garage\n",
    "#PoolQC: NA = No Pool\n",
    "#Fence: NA = No Fence\n",
    "#MiscFeature = NA = None\n",
    "\n",
    "#Convert null to appropriate description\n",
    "#Credit Joseph Nelson\n",
    "\n",
    "house.Alley = house.Alley.fillna(value='none', inplace=False)\n",
    "house.BsmtQual = house.BsmtQual.fillna(value='none', inplace=False)\n",
    "house.BsmtFinType1 = house.BsmtFinType1.fillna(value='none', inplace=False)\n",
    "house.BsmtFinType2 = house.BsmtFinType2.fillna(value='none', inplace=False)\n",
    "house.GarageType = house.GarageType.fillna(value='none', inplace=False)\n",
    "house.GarageFinish = house.GarageFinish.fillna(value='none', inplace=False)\n",
    "house.GarageQual = house.GarageQual.fillna(value='none', inplace=False)\n",
    "house.GarageCond = house.GarageCond.fillna(value='none', inplace=False)\n",
    "house.Fence = house.Fence.fillna(value='none', inplace=False)\n",
    "house.MiscFeature = house.MiscFeature.fillna(value='none', inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass         0\n",
       "MSZoning           0\n",
       "LotFrontage      259\n",
       "LotArea            0\n",
       "Alley              0\n",
       "LotShape           0\n",
       "LotConfig          0\n",
       "Neighborhood       0\n",
       "Condition1         0\n",
       "Condition2         0\n",
       "BldgType           0\n",
       "HouseStyle         0\n",
       "OverallQual        0\n",
       "OverallCond        0\n",
       "YearBuilt          0\n",
       "YearRemodAdd       0\n",
       "RoofStyle          0\n",
       "RoofMatl           0\n",
       "Exterior1st        0\n",
       "Exterior2nd        0\n",
       "MasVnrType         8\n",
       "MasVnrArea         8\n",
       "ExterQual          0\n",
       "ExterCond          0\n",
       "Foundation         0\n",
       "BsmtQual           0\n",
       "BsmtExposure      38\n",
       "BsmtFinType1       0\n",
       "BsmtFinSF1         0\n",
       "BsmtFinType2       0\n",
       "                ... \n",
       "HalfBath           0\n",
       "BedroomAbvGr       0\n",
       "KitchenAbvGr       0\n",
       "KitchenQual        0\n",
       "TotRmsAbvGrd       0\n",
       "Functional         0\n",
       "Fireplaces         0\n",
       "FireplaceQu      681\n",
       "GarageType         0\n",
       "GarageYrBlt       79\n",
       "GarageFinish       0\n",
       "GarageCars         0\n",
       "GarageArea         0\n",
       "GarageQual         0\n",
       "GarageCond         0\n",
       "PavedDrive         0\n",
       "WoodDeckSF         0\n",
       "OpenPorchSF        0\n",
       "EnclosedPorch      0\n",
       "3SsnPorch          0\n",
       "ScreenPorch        0\n",
       "PoolArea           0\n",
       "Fence              0\n",
       "MiscFeature        0\n",
       "MiscVal            0\n",
       "MoSold             0\n",
       "YrSold             0\n",
       "SaleType           0\n",
       "SaleCondition      0\n",
       "SalePrice          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.isnull().sum()\n",
    "\n",
    "#Recheck the null values.  Still some null values.  Probably don't need to do anything about these values right now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['CollgCr', 'Veenker', 'Crawfor', 'NoRidge', 'Mitchel', 'Somerst',\n",
       "       'NWAmes', 'OldTown', 'BrkSide', 'Sawyer', 'NridgHt', 'NAmes',\n",
       "       'SawyerW', 'IDOTRR', 'MeadowV', 'Edwards', 'Timber', 'Gilbert',\n",
       "       'StoneBr', 'ClearCr', 'NPkVill', 'Blmngtn', 'BrDale', 'SWISU',\n",
       "       'Blueste'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.Neighborhood.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1191.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1442.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "      <td>1450.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.903448</td>\n",
       "      <td>70.052897</td>\n",
       "      <td>10523.831724</td>\n",
       "      <td>6.114483</td>\n",
       "      <td>5.582069</td>\n",
       "      <td>1971.593103</td>\n",
       "      <td>1985.049655</td>\n",
       "      <td>104.404300</td>\n",
       "      <td>445.162759</td>\n",
       "      <td>46.870345</td>\n",
       "      <td>...</td>\n",
       "      <td>94.826897</td>\n",
       "      <td>46.537931</td>\n",
       "      <td>21.536552</td>\n",
       "      <td>3.433103</td>\n",
       "      <td>15.096552</td>\n",
       "      <td>2.777931</td>\n",
       "      <td>43.365517</td>\n",
       "      <td>6.312414</td>\n",
       "      <td>2007.812414</td>\n",
       "      <td>181654.942069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.265033</td>\n",
       "      <td>24.301013</td>\n",
       "      <td>10012.185355</td>\n",
       "      <td>1.372860</td>\n",
       "      <td>1.107330</td>\n",
       "      <td>30.022696</td>\n",
       "      <td>20.552319</td>\n",
       "      <td>181.486539</td>\n",
       "      <td>456.353871</td>\n",
       "      <td>161.828458</td>\n",
       "      <td>...</td>\n",
       "      <td>125.560863</td>\n",
       "      <td>65.222761</td>\n",
       "      <td>60.838826</td>\n",
       "      <td>29.416948</td>\n",
       "      <td>55.893399</td>\n",
       "      <td>40.315051</td>\n",
       "      <td>497.636495</td>\n",
       "      <td>2.698244</td>\n",
       "      <td>1.326321</td>\n",
       "      <td>79176.485241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>37900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7544.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>130000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>9496.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163945.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11613.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.750000</td>\n",
       "      <td>713.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>1474.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MSSubClass  LotFrontage        LotArea  OverallQual  OverallCond  \\\n",
       "count  1450.000000  1191.000000    1450.000000  1450.000000  1450.000000   \n",
       "mean     56.903448    70.052897   10523.831724     6.114483     5.582069   \n",
       "std      42.265033    24.301013   10012.185355     1.372860     1.107330   \n",
       "min      20.000000    21.000000    1300.000000     1.000000     1.000000   \n",
       "25%      20.000000    59.000000    7544.500000     5.000000     5.000000   \n",
       "50%      50.000000    70.000000    9496.000000     6.000000     5.000000   \n",
       "75%      70.000000    80.000000   11613.500000     7.000000     6.000000   \n",
       "max     190.000000   313.000000  215245.000000    10.000000     9.000000   \n",
       "\n",
       "         YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1   BsmtFinSF2  \\\n",
       "count  1450.000000   1450.000000  1442.000000  1450.000000  1450.000000   \n",
       "mean   1971.593103   1985.049655   104.404300   445.162759    46.870345   \n",
       "std      30.022696     20.552319   181.486539   456.353871   161.828458   \n",
       "min    1872.000000   1950.000000     0.000000     0.000000     0.000000   \n",
       "25%    1954.000000   1967.000000     0.000000     0.000000     0.000000   \n",
       "50%    1973.000000   1994.000000     0.000000   384.000000     0.000000   \n",
       "75%    2001.000000   2004.000000   166.750000   713.750000     0.000000   \n",
       "max    2010.000000   2010.000000  1600.000000  5644.000000  1474.000000   \n",
       "\n",
       "           ...         WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  \\\n",
       "count      ...        1450.000000  1450.000000    1450.000000  1450.000000   \n",
       "mean       ...          94.826897    46.537931      21.536552     3.433103   \n",
       "std        ...         125.560863    65.222761      60.838826    29.416948   \n",
       "min        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "25%        ...           0.000000     0.000000       0.000000     0.000000   \n",
       "50%        ...           0.000000    25.000000       0.000000     0.000000   \n",
       "75%        ...         168.000000    68.000000       0.000000     0.000000   \n",
       "max        ...         857.000000   547.000000     552.000000   508.000000   \n",
       "\n",
       "       ScreenPorch     PoolArea       MiscVal       MoSold       YrSold  \\\n",
       "count  1450.000000  1450.000000   1450.000000  1450.000000  1450.000000   \n",
       "mean     15.096552     2.777931     43.365517     6.312414  2007.812414   \n",
       "std      55.893399    40.315051    497.636495     2.698244     1.326321   \n",
       "min       0.000000     0.000000      0.000000     1.000000  2006.000000   \n",
       "25%       0.000000     0.000000      0.000000     5.000000  2007.000000   \n",
       "50%       0.000000     0.000000      0.000000     6.000000  2008.000000   \n",
       "75%       0.000000     0.000000      0.000000     8.000000  2009.000000   \n",
       "max     480.000000   738.000000  15500.000000    12.000000  2010.000000   \n",
       "\n",
       "           SalePrice  \n",
       "count    1450.000000  \n",
       "mean   181654.942069  \n",
       "std     79176.485241  \n",
       "min     37900.000000  \n",
       "25%    130000.000000  \n",
       "50%    163945.000000  \n",
       "75%    214000.000000  \n",
       "max    755000.000000  \n",
       "\n",
       "[8 rows x 37 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#View summary stats\n",
    "house.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TFFjGcjMzM9auXcuSJUsYN24cd+/efWVyzXbt2rFv3z7atWunnDM0NCQwMJDp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a33c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create heat map of correlation matrix to see if we can see anything useful\n",
    "mean_corr = house.corr()\n",
    "\n",
    "# Set the default matplotlib figure size:\n",
    "fig, ax = plt.subplots(figsize=(9,7))\n",
    "\n",
    "# Generate a mask for the upper triangle (taken from seaborn example gallery)\n",
    "mask = np.zeros_like(mean_corr, dtype=np.bool)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Plot the heatmap with seaborn.\n",
    "# Assign the matplotlib axis the function returns. This will let us resize the labels.\n",
    "ax = sns.heatmap(mean_corr, mask=mask, ax=ax)\n",
    "\n",
    "# Resize the labels.\n",
    "ax.set_xticklabels(ax.xaxis.get_ticklabels(), fontsize=14)\n",
    "ax.set_yticklabels(ax.yaxis.get_ticklabels(), fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above doesn't really tell us anything"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "259"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.LotFrontage.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "house.LotFrontage = house.LotFrontage.replace(np.NaN, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7aerUuzRIPZHX6dphnKjcjTbIyms1xESEOAIuwcwFyBr9jDMSlQSrNUJ/Jono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I7NzVYuvj1S0y/7lfOTrGq1kpwtRyzki4tOviz1eoavBJjyxSs80lg0Q0OTCVEsUihIREJJouHq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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a877290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#To predict house prices let's build a pairplot to see if we can see any visual trends\n",
    "\n",
    "#Create subset of variables we want to use\n",
    "subset = house.loc[:,['SalePrice','LotArea', 'LotFrontage','GrLivArea', 'BsmtUnfSF','Condition1','YearBuilt','YearRemodAdd','TotalBsmtSF','BsmtCond','BsmtQual','GarageQual','GarageCond','GarageArea','PoolArea','OpenPorchSF','YrSold','BedroomAbvGr','KitchenQual','SaleCondition','Neighborhood','TotRmsAbvGrd','FullBath','OverallQual','OverallCond','BldgType','SaleType']]\n",
    "\n",
    "\n",
    "sns.pairplot(data=subset,\n",
    "                  y_vars=['SalePrice'],x_vars=['LotArea','GrLivArea','BsmtUnfSF','YearBuilt','YearRemodAdd'],hue='BldgType')\n",
    "plt.show()\n",
    "\n",
    "#Looks like LotArea, GrLivArea and YearRemodAdd have some outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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7aerUuzRIPZHX6dphnKjcjTbIyms1xESEOAIuwcwFyBr9jDMSlQSrNUJ/Jono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I7NzVYuvj1S0y/7lfOTrGq1kpwtRyzki4tOviz1eoavBJjyxSs80lg0Q0OTCVEsUihIREJJouHq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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b11e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Remove outliers\n",
    "\n",
    "#subset = subset[subset.LotArea <= 20000]\n",
    "#subset = subset[subset.GrLivArea <= 4000]\n",
    "#subset = subset[subset.YearRemodAdd >= 1955]\n",
    "\n",
    "#I was tempted to remove outliers above however I realized I was taking away nearly 250 rows which is rquivalent to ~17% of the data.\n",
    "\n",
    "sns.pairplot(data=subset,\n",
    "                  y_vars=['SalePrice'],x_vars=['LotArea','GrLivArea','BsmtUnfSF','YearBuilt','YearRemodAdd'],hue='BldgType')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IN+pRWGKB4JzrRBRA2u2Rjxs3Dtu2bcOMGTMghMDSpUvx0UcfwW63Y/r06Zg3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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b11150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(subset.GrLivArea,hist=True)\n",
    "plt.title('Histogram of Sqft above ground (sqft)')\n",
    "plt.xlabel('SqFt')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Px8Ojjz7KkSNHcLvd3H///Vx//fXs2LGD++67j/T0dACmTJnCTTfd1GyNExERCSUBQ3vl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\n/tm+IFfS9NQ9LiFj045i/vC3XE5W1JL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vX9/hverqaqqqqrp9bUL8KaRpC6EpNzc3jEYj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\nCE1I0xZCCCE08R/GU2beW1sQYAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119b11690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(subset.YearBuilt, hist=True)\n",
    "plt.title('Histogram of Year House Built')\n",
    "plt.xlabel('Year Built')\n",
    "plt.show()\n",
    "#Lack of additions below 1955 and between 1980-1990.  May need to bootstrap.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BavAiImIvBbzFQuFwzIVmQFPVioiIvRTwFjLGEGxjDV6LzYiIiJ0U8BY6PMdN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\nQYMGUV5eHt1WUVFBRkYG6en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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b411f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(subset.LotArea, hist=True)\n",
    "plt.title('Histogram of Lot Area')\n",
    "plt.xlabel('Lot Area (sqft)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Create train/test split of subset.  \n",
    "#Training data set are houses sold prior to 2010 \n",
    "#Test data set are houses sold in the year 2010\n",
    "\n",
    "#Create train subset\n",
    "train = subset[subset.YrSold < 2010]\n",
    "\n",
    "#Create x_train and y_train set\n",
    "#X_train = train[[\"GrLivArea\",\"YearRemodAdd\",\"YearBuilt\", \"GarageArea\",\"TotalBsmtSF\" ]].values\n",
    "X_train = train[[\"GrLivArea\",\"YearBuilt\",\"GarageArea\",\"TotalBsmtSF\" ]].values\n",
    "y_train = train[[\"SalePrice\"]].values\n",
    "\n",
    "#Create train subset\n",
    "test = subset[subset.YrSold == 2010]\n",
    "\n",
    "#Create x_test and y_test set.\n",
    "#X_test = test[[\"GrLivArea\",\"YearRemodAdd\",\"YearBuilt\", \"GarageArea\",\"TotalBsmtSF\" ]].values\n",
    "X_test = test[[\"GrLivArea\",\"YearRemodAdd\",\"GarageArea\",\"TotalBsmtSF\" ]].values\n",
    "y_test = test[[\"SalePrice\"]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Prediction error on test data: $2152.22226828\n",
      "\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.945\n",
      "Model:                            OLS   Adj. R-squared:                  0.945\n",
      "Method:                 Least Squares   F-statistic:                     5483.\n",
      "Date:                Tue, 13 Jun 2017   Prob (F-statistic):               0.00\n",
      "Time:                        18:59:19   Log-Likelihood:                -15548.\n",
      "No. Observations:                1278   AIC:                         3.110e+04\n",
      "Df Residuals:                    1274   BIC:                         3.112e+04\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
      "------------------------------------------------------------------------------\n",
      "x1            67.0286      2.929     22.881      0.000        61.282    72.776\n",
      "x2            -8.7274      2.234     -3.906      0.000       -13.111    -4.344\n",
      "x3           105.6005      7.386     14.297      0.000        91.110   120.091\n",
      "x4            44.4788      3.555     12.512      0.000        37.505    51.453\n",
      "==============================================================================\n",
      "Omnibus:                      631.781   Durbin-Watson:                   1.996\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            50886.022\n",
      "Skew:                          -1.394   Prob(JB):                         0.00\n",
      "Kurtosis:                      33.787   Cond. No.                         16.2\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "#Although you could do this in scikit-learn I prefered to do this using statsmodel because of the statistical outputs.\n",
    "import statsmodels.api as sm\n",
    "\n",
    "#Create model using multi-linear regression on training data set\n",
    "model = sm.OLS(y_train, X_train)\n",
    "results = model.fit()\n",
    "\n",
    "#Predict model on test data\n",
    "x_pred = results.predict(X_train)\n",
    "predictions = results.predict(X_test)\n",
    "\n",
    "#View results of training model\n",
    "prediction_error = y_test - predictions\n",
    "print 'Average Prediction error on test data: $%s' % np.mean(prediction_error)\n",
    "print''\n",
    "#View Summary statistics\n",
    "print results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comment \n",
    "Based on fixed inputs the training dataset is doing quite well with the prediction. If I had more time I would probably try elastic net to see if there is any improvement on the prediction.  Overall I am pretty happy with my prediction for the following reasons:\n",
    "    1. R-squared > 0.90\n",
    "    2. P values < 0.05\n",
    "    3. Standard error for each element is pretty low\n",
    "    4. My prediction error indicates I am over predicting house price by $2152.\n",
    " \n",
    "Below I have plotted the training and test data sets versus predicted\n",
    "First plot: Training vs Actual \n",
    "Second plot: Test vs Actual\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PDBo0CEOHDkVWVhbOnj0LALh8+TJCQ0MNOmbAtr/fsWPH4sCBA8I1du3ahRdeeMHkuLKy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\nPFgGUChMyx1KBWA0Wi4teWOgH8hZAiqWRcjgQAFFgLBXi0AoxgSYBjC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11dHTnYTDsFAV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\nEklEQVQQQojbKKAghBBCiNv+P8JyvYZF7HznAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11abe5250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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dNzzXz8/P6Nzi4mL4+Pigbdu2RscrKiqsBhQqlcqRj+t2+HyeqyU/G8Dn83R8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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a8c4710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the training model\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(x_pred, y_train, s=30, marker='o', zorder=10)\n",
    "#plt.plot(predictions, y_test, 'r-', label=\"OLS\")\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.ylabel(\"Actual Values\")\n",
    "\n",
    "plt.xlim([0,500000])\n",
    "plt.ylim([0,500000])\n",
    "#plt.xlabel(\"Predicted Values - $\\hat{y_test}$\")\n",
    "#plt.ylabel(\"Actual Values - $y_test$\")\n",
    "plt.title(\"Price Estimation on Homes Sold Before 2010 (Train Data)\")\n",
    "plt.show()\n",
    "\n",
    "# Plot the test model\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(predictions, y_test, s=30, marker='o', zorder=10)\n",
    "#plt.plot(predictions, y_test, 'r-', label=\"OLS\")\n",
    "plt.xlabel(\"Predicted Values\")\n",
    "plt.ylabel(\"Actual Values\")\n",
    "\n",
    "plt.xlim([0,400000])\n",
    "plt.ylim([0,400000])\n",
    "#plt.xlabel(\"Predicted Values - $\\hat{y_test}$\")\n",
    "#plt.ylabel(\"Actual Values - $y_test$\")\n",
    "plt.title(\"Price Estimation on Homes Sold in 2010 (Test Data)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#Comment\n",
    "Answers to question 1\n",
    "\n",
    "1.Perform any cleaning, feature engineering, and EDA you deem necessary.\n",
    "    \n",
    "2.Be sure to remove any houses that are not residential from the dataset.\n",
    "\n",
    "3.Identify fixed features that can predict price. In my example I used GrLivArea, YearRemodAdd and TotalBsmtSF\n",
    "\n",
    "4.Train a model on pre-2010 data and evaluate its performance on the 2010 houses.\n",
    "\n",
    "5.Characterize your model. How well does it perform? What are the best estimates of price?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sfam' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-f5ba376d0555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;31m#Set predictor variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m#X = sfam[[\"GrLivArea\",\"YearRemodAdd\",\"LotArea\",\"OpenPorchSF\", \"YearBuilt\", \"GarageArea\",\"PoolArea\",\"TotalBsmtSF\" ]].values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msfam\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"GrLivArea\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"YearRemodAdd\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"LotArea\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"YearBuilt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"GarageArea\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"TotalBsmtSF\"\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'sfam' is not defined"
     ]
    }
   ],
   "source": [
    "#Multi-linear regresssion using statsmodel\n",
    "\n",
    "#Do this twice.  Once for single family homes and again for dual family homes\n",
    "\n",
    "#Single family homes\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "\n",
    "#Set predictor variables\n",
    "#X = sfam[[\"GrLivArea\",\"YearRemodAdd\",\"LotArea\",\"OpenPorchSF\", \"YearBuilt\", \"GarageArea\",\"PoolArea\",\"TotalBsmtSF\" ]].values\n",
    "X = sfam[[\"GrLivArea\",\"YearRemodAdd\",\"LotArea\",\"YearBuilt\", \"GarageArea\",\"TotalBsmtSF\" ]].values\n",
    "X = np.concatenate([X, np.ones((X.shape[0], 1))], axis=1)\n",
    "\n",
    "#Standardize variables\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "Xs = ss.fit_transform(X)\n",
    "\n",
    "#Set target variable y\n",
    "y = sfam['SalePrice']\n",
    "\n",
    "# Note the difference in argument order\n",
    "# optionally, you can chain \"fit()\" to the model object\n",
    "#model = sm.OLS.fit_regularized(y, X)\n",
    "#model = model.fit_regularized(method='elastic_net')\n",
    "model = sm.OLS(y, X)\n",
    "model = model.fit()\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Plot the model\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(predictions, y, s=30, marker='o', zorder=10)\n",
    "#plt.plot(predictions, y, 'r-', label=\"OLS\")\n",
    "plt.xlabel(\"Predicted Values - $\\hat{y}$\")\n",
    "plt.ylabel(\"Actual Values - $y$\")\n",
    "plt.title(\"Price Estimation on Single Family Homes\")\n",
    "plt.show()\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#print np.sqrt(mean_squared_error(y, predictions))\n",
    "print model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Now apply to test set post 2010\n",
    "print('Parameters: ', model.params)\n",
    "print('R2: ', model.rsquared)\n",
    "\n",
    "#y_pred = model.predict(x_test[subset])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In terms of predicting single family homes we can get a pretty good match where r2 is 0.81.  However one concern is the amount of inputs required.\n",
    "\n",
    "When predicting dual family homes this becomes much more complicated.  There is much more spread at the high end of the price spectrum.  \n",
    "\n",
    "If I had more time I might try and incorporate a logitic regression function with bathrooms or home condition.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dfam' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-4af7720580a6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#Set predictor variables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;31m#X = dfam[[\"GrLivArea\",\"YearRemodAdd\",\"LotArea\",\"OpenPorchSF\", \"YearBuilt\", \"GarageArea\",\"PoolArea\",\"TotalBsmtSF\" ]].values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdfam\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"GrLivArea\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"YearRemodAdd\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"LotArea\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"YearBuilt\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"GarageArea\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"TotalBsmtSF\"\u001b[0m \u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dfam' is not defined"
     ]
    }
   ],
   "source": [
    "#Same as above except for dual family homes\n",
    "\n",
    "import statsmodels.api as sm\n",
    "\n",
    "#Set predictor variables\n",
    "#X = dfam[[\"GrLivArea\",\"YearRemodAdd\",\"LotArea\",\"OpenPorchSF\", \"YearBuilt\", \"GarageArea\",\"PoolArea\",\"TotalBsmtSF\" ]].values\n",
    "X = dfam[[\"GrLivArea\",\"YearRemodAdd\",\"LotArea\", \"YearBuilt\", \"GarageArea\",\"TotalBsmtSF\" ]].values\n",
    "X = np.concatenate([X, np.ones((X.shape[0], 1))], axis=1)\n",
    "\n",
    "#Standardize variables\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "ss = StandardScaler()\n",
    "Xs = ss.fit_transform(X)\n",
    "\n",
    "#Set target variable y\n",
    "y = dfam['SalePrice']\n",
    "\n",
    "# Note the difference in argument order\n",
    "# optionally, you can chain \"fit()\" to the model object\n",
    "model = sm.OLS(y, X)\n",
    "model = model.fit()\n",
    "#model = sm.OLS.fit_regularized(y, X)\n",
    "#model = model.fit_regularized(method='elastic_net')\n",
    "predictions = model.predict(X)\n",
    "\n",
    "# Plot the model\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(predictions, y, s=30, marker='o', zorder=10)\n",
    "plt.xlabel(\"Predicted Values - $\\hat{y}$\")\n",
    "plt.ylabel(\"Actual Values - $y$\")\n",
    "plt.title(\"Price Estimation on Single Family Homes\")\n",
    "plt.show()\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "#print np.sqrt(mean_squared_error(y, predictions))\n",
    "print model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://imgur.com/l5NasQj.png\" style=\"float: left; margin: 25px 15px 0px 0px; height: 25px\">\n",
    "\n",
    "## 2. Determine any value of *changeable* property characteristics unexplained by the *fixed* ones.\n",
    "\n",
    "---\n",
    "\n",
    "Now that you have a model that estimates the price of a house based on its static characteristics, we can move forward with part 2 and 3 of the plan: what are the costs/benefits of quality, condition, and renovations?\n",
    "\n",
    "There are two specific requirements for these estimates:\n",
    "1. The estimates of effects must be in terms of dollars added or subtracted from the house value. \n",
    "2. The effects must be on the variance in price remaining from the first model.\n",
    "\n",
    "The residuals from the first model (training and testing) represent the variance in price unexplained by the fixed characteristics. Of that variance in price remaining, how much of it can be explained by the easy-to-change aspects of the property?\n",
    "\n",
    "---\n",
    "\n",
    "**Your goals:**\n",
    "1. Evaluate the effect in dollars of the renovate-able features. \n",
    "- How would your company use this second model and its coefficients to determine whether they should buy a property or not? Explain how the company can use the two models you have built to determine if they can make money. \n",
    "- Investigate how much of the variance in price remaining is explained by these features.\n",
    "- Do you trust your model? Should it be used to evaluate which properties to buy and fix up?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#To establish renovatable features we need to find a column that can divide the dataset into renovated vs non-renovated\n",
    "\n",
    "#Create subset of houses that have been renovated.  The way we check is if 'YearBuilt' != 'YearRemoAdd'\n",
    "reno = subset[subset.YearBuilt !=subset.YearRemodAdd]\n",
    "\n",
    "#For some reason some houses that have been renovated are also considred new.  We want to exclude them SaleType != 'New'\n",
    "reno = reno[reno.SaleType != 'New']\n",
    "\n",
    "#Create subset of houses that have NOT been renovated ()'YearBuilt' == 'YearRemoAdd')\n",
    "original = subset[subset.YearBuilt == subset.YearRemodAdd]\n",
    "#Now remove houses that have SaleType == New because we do not want to include brand new homes.\n",
    "original = original[original.SaleType != 'New']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>SalePrice</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>...</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>SaleType</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>223500</td>\n",
       "      <td>11250</td>\n",
       "      <td>68.0</td>\n",
       "      <td>1786</td>\n",
       "      <td>434</td>\n",
       "      <td>Norm</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>920</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Normal</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140000</td>\n",
       "      <td>9550</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1717</td>\n",
       "      <td>540</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>143000</td>\n",
       "      <td>14115</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1362</td>\n",
       "      <td>64</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1993</td>\n",
       "      <td>1995</td>\n",
       "      <td>796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Mitchel</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>307000</td>\n",
       "      <td>10084</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1694</td>\n",
       "      <td>317</td>\n",
       "      <td>Norm</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>1686</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Somerst</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>129900</td>\n",
       "      <td>6120</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1774</td>\n",
       "      <td>952</td>\n",
       "      <td>Artery</td>\n",
       "      <td>1931</td>\n",
       "      <td>1950</td>\n",
       "      <td>952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>OldTown</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   SalePrice  LotArea  LotFrontage  GrLivArea  BsmtUnfSF Condition1  \\\n",
       "2     223500    11250         68.0       1786        434       Norm   \n",
       "3     140000     9550         60.0       1717        540       Norm   \n",
       "5     143000    14115         85.0       1362         64       Norm   \n",
       "6     307000    10084         75.0       1694        317       Norm   \n",
       "8     129900     6120         51.0       1774        952     Artery   \n",
       "\n",
       "   YearBuilt  YearRemodAdd  TotalBsmtSF  BsmtCond   ...    BedroomAbvGr  \\\n",
       "2       2001          2002          920       NaN   ...               3   \n",
       "3       1915          1970          756       NaN   ...               3   \n",
       "5       1993          1995          796       NaN   ...               1   \n",
       "6       2004          2005         1686       NaN   ...               3   \n",
       "8       1931          1950          952       NaN   ...               2   \n",
       "\n",
       "  KitchenQual SaleCondition  Neighborhood  TotRmsAbvGrd  FullBath  \\\n",
       "2          Gd        Normal       CollgCr             6         2   \n",
       "3          Gd       Abnorml       Crawfor             7         1   \n",
       "5          TA        Normal       Mitchel             5         1   \n",
       "6          Gd        Normal       Somerst             7         2   \n",
       "8          TA       Abnorml       OldTown             8         2   \n",
       "\n",
       "   OverallQual  OverallCond BldgType SaleType  \n",
       "2            7            5     1Fam       WD  \n",
       "3            7            5     1Fam       WD  \n",
       "5            5            5     1Fam       WD  \n",
       "6            8            5     1Fam       WD  \n",
       "8            7            5     1Fam       WD  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reno.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "My methodology is to calcualte the dollar per sqft for all renovated and non renovated houses.  Then compare average  between the two subsets to get an idea of the incremental uptick when renovating.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary stats of $/sqft for Original houses excluding new builds\n",
      "count    694.000000\n",
      "mean     122.531602\n",
      "std       25.709093\n",
      "min       47.021944\n",
      "25%      106.593704\n",
      "50%      122.667984\n",
      "75%      138.256418\n",
      "max      231.057452\n",
      "Name: dpsqft, dtype: float64\n",
      "\n",
      "Summary stats of $/sqft for Renovated houses\n",
      "count    634.000000\n",
      "mean     112.842501\n",
      "std       30.751056\n",
      "min       38.510911\n",
      "25%       91.849959\n",
      "50%      110.522318\n",
      "75%      131.813722\n",
      "max      216.208477\n",
      "Name: dpsqft, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Calucalte $/sqft value of renovated and non renovated houses\n",
    "#Create new column to add $/sqft in reno and original subsets\n",
    "reno['dpsqft'] = reno.SalePrice/reno.GrLivArea\n",
    "original['dpsqft'] = original.SalePrice/original.GrLivArea\n",
    "\n",
    "#Calculate summary stats of dollar per sqft in reno and original\n",
    "print 'Summary stats of $/sqft for Original houses excluding new builds'\n",
    "print original.dpsqft.describe()\n",
    "print ''\n",
    "print 'Summary stats of $/sqft for Renovated houses'\n",
    "print reno.dpsqft.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a problem!\n",
    "\n",
    "The average price of renovated house is lower than non-renovated which does not make sense.  \n",
    "There are either problems in the data or the grouping is too generalized.  This may not be accounting for market forces like time of sale or location or somethingelse I have not thought of.\n",
    "\n",
    "Let's look at the dollar/sqft values when we group by 'OverallCond'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "OverallQual\n",
      "1      92.571273\n",
      "2     105.993151\n",
      "3      80.644629\n",
      "4     104.243659\n",
      "5     112.896056\n",
      "6     114.496555\n",
      "7     124.494470\n",
      "8     144.511059\n",
      "9     178.693921\n",
      "10    160.423437\n",
      "Name: dpsqft, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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R5gAAWI4yBwDAcpQ5AACWo8wBALAcZQ4AgOUocwAALEeZAwBgOcocAADLUeYA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\ncv8HpksBFJGsNWAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a74b850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Use groupby function to filter dollar/sqft by 'OverallQual' then calcualte the mean\n",
    "subset['dpsqft'] = subset.SalePrice/subset.GrLivArea\n",
    "\n",
    "#print 'Average $/sqft ranked by Condition'\n",
    "print subset.groupby('OverallQual').dpsqft.mean()\n",
    "\n",
    "#Calculate the median to compare and see if there are any major differences.  \n",
    "#Didn't see any signifigant difference to mean so its commented out\n",
    "\n",
    "#print 'Median $/sqft ranked by Condition'\n",
    "#print subset.groupby('OverallQual').dpsqft.median()\n",
    "\n",
    "\n",
    "#Plot Bar chart of House Quality vs Dollar/sqft\n",
    "xbar = np.sort(subset.OverallQual.unique())\n",
    "ybar = subset.groupby('OverallQual').dpsqft.mean().values\n",
    "std_dev = subset.groupby('OverallQual').dpsqft.std().values\n",
    "\n",
    "#plt.bar(xbar,ybar,0.35,color='blue')\n",
    "plt.bar(xbar,ybar,0.35,color='blue',yerr=std_dev, error_kw=dict(elinewidth=2,ecolor='black'))\n",
    "\n",
    "plt.title('Average $/sqft by House Quality')\n",
    "plt.xlabel('House Quality')\n",
    "plt.ylabel('$/sqft')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The calculation and bar chart above suggests that the house quality representing the best value for home renovation are 3 and 8.  By making changes and improving by just one category one would expect an average improvement of around $27-$34 per squarefoot.\n",
    "\n",
    "Alternatively if your house was ranked in category 4,5,6 or 7 there is very little improvement from one category to another.  So one would have to improve at least two categories to see any real value of a renovation.\n",
    "\n",
    "Something that needs more work with this philospohy is the error bars associated with each category and the amount of effort required to renovate.  It's entirely possible that the effory level required to renovate to improve one category is the same as moving up 3 categories.\n",
    "\n",
    "Using the information above I would target houses in category 3 that have an average dollar/sqft value less than the $80/sqft.  These houses would represent the greatest renovation potential with the lowest risk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>SalePrice</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>...</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>dpsqft</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>107400</td>\n",
       "      <td>5790</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1605</td>\n",
       "      <td>840</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1915</td>\n",
       "      <td>1950</td>\n",
       "      <td>840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>TA</td>\n",
       "      <td>Normal</td>\n",
       "      <td>OldTown</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "      <td>66.915888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>76500</td>\n",
       "      <td>5350</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1306</td>\n",
       "      <td>728</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1940</td>\n",
       "      <td>1966</td>\n",
       "      <td>728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>Fa</td>\n",
       "      <td>Normal</td>\n",
       "      <td>BrkSide</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "      <td>58.575804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>620</th>\n",
       "      <td>67000</td>\n",
       "      <td>8248</td>\n",
       "      <td>45.0</td>\n",
       "      <td>864</td>\n",
       "      <td>823</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1914</td>\n",
       "      <td>1950</td>\n",
       "      <td>864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>TA</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Edwards</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "      <td>77.546296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>710</th>\n",
       "      <td>52000</td>\n",
       "      <td>4130</td>\n",
       "      <td>56.0</td>\n",
       "      <td>729</td>\n",
       "      <td>270</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1935</td>\n",
       "      <td>2003</td>\n",
       "      <td>270</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>TA</td>\n",
       "      <td>Normal</td>\n",
       "      <td>BrkSide</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "      <td>71.330590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>37900</td>\n",
       "      <td>5925</td>\n",
       "      <td>50.0</td>\n",
       "      <td>968</td>\n",
       "      <td>600</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1910</td>\n",
       "      <td>1950</td>\n",
       "      <td>600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>TA</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>OldTown</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "      <td>39.152893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>95000</td>\n",
       "      <td>10615</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1699</td>\n",
       "      <td>538</td>\n",
       "      <td>Artery</td>\n",
       "      <td>1900</td>\n",
       "      <td>1970</td>\n",
       "      <td>978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>TA</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>OldTown</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>2fmCon</td>\n",
       "      <td>WD</td>\n",
       "      <td>55.915244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1380</th>\n",
       "      <td>58500</td>\n",
       "      <td>8212</td>\n",
       "      <td>45.0</td>\n",
       "      <td>864</td>\n",
       "      <td>661</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1914</td>\n",
       "      <td>1950</td>\n",
       "      <td>864</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>TA</td>\n",
       "      <td>Normal</td>\n",
       "      <td>Edwards</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>WD</td>\n",
       "      <td>67.708333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      SalePrice  LotArea  LotFrontage  GrLivArea  BsmtUnfSF Condition1  \\\n",
       "74       107400     5790         60.0       1605        840       Norm   \n",
       "250       76500     5350         55.0       1306        728       Norm   \n",
       "620       67000     8248         45.0        864        823       Norm   \n",
       "710       52000     4130         56.0        729        270       Norm   \n",
       "968       37900     5925         50.0        968        600       Norm   \n",
       "1186      95000    10615        107.0       1699        538     Artery   \n",
       "1380      58500     8212         45.0        864        661       Norm   \n",
       "\n",
       "      YearBuilt  YearRemodAdd  TotalBsmtSF  BsmtCond    ...     KitchenQual  \\\n",
       "74         1915          1950          840       NaN    ...              TA   \n",
       "250        1940          1966          728       NaN    ...              Fa   \n",
       "620        1914          1950          864       NaN    ...              TA   \n",
       "710        1935          2003          270       NaN    ...              TA   \n",
       "968        1910          1950          600       NaN    ...              TA   \n",
       "1186       1900          1970          978       NaN    ...              TA   \n",
       "1380       1914          1950          864       NaN    ...              TA   \n",
       "\n",
       "     SaleCondition Neighborhood  TotRmsAbvGrd  FullBath  OverallQual  \\\n",
       "74          Normal      OldTown             8         2            3   \n",
       "250         Normal      BrkSide             6         1            3   \n",
       "620         Normal      Edwards             5         1            3   \n",
       "710         Normal      BrkSide             5         1            3   \n",
       "968        Abnorml      OldTown             6         1            3   \n",
       "1186       Abnorml      OldTown             7         2            3   \n",
       "1380        Normal      Edwards             5         1            3   \n",
       "\n",
       "      OverallCond  BldgType SaleType     dpsqft  \n",
       "74              6      1Fam       WD  66.915888  \n",
       "250             2      1Fam       WD  58.575804  \n",
       "620             3      1Fam       WD  77.546296  \n",
       "710             6      1Fam       WD  71.330590  \n",
       "968             6      1Fam       WD  39.152893  \n",
       "1186            5    2fmCon       WD  55.915244  \n",
       "1380            3      1Fam       WD  67.708333  \n",
       "\n",
       "[7 rows x 28 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#prospects = subset[(subset.OverallQual==3) & (subset.dpsqft <= 80) & (subset.GrLivArea > 1000)]\n",
    "prospects = subset[(subset.OverallQual==3) & (subset.dpsqft <= 80)]\n",
    "\n",
    "prospects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The list above represents seven homes that I would target for potential renovation.  If we had more personal knowledge of the Neighborhoods we might be able to refine this list even more. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Gd', 'TA', 'Ex', 'Fa'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset.KitchenQual.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KitchenQual\n",
      "Ex    160.130585\n",
      "Fa     92.119419\n",
      "Gd    129.936768\n",
      "TA    109.810815\n",
      "Name: dpsqft, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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YMEnS4cOHNX/+fBUsWDAtywEAIEtK02n24sWLa+bMmQ/dPnPmTHXt2lXPPfecEhISdPbs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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b18cd50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's have a look at average $/sqft vs KitchenQuality\n",
    "\n",
    "#Use groupby function to calculate $/sqft for each category in Kitchen Quality\n",
    "print subset.groupby('KitchenQual').dpsqft.mean()\n",
    "N = 4\n",
    "ind = np.arange(N)\n",
    "label = ['Fair','Average','Good','Excellent']\n",
    "ybar = np.sort(subset.groupby('KitchenQual').dpsqft.mean().values)\n",
    "std_dev = subset.groupby('KitchenQual').dpsqft.std().values\n",
    "\n",
    "\n",
    "#plt.bar(xbar,ybar,0.35,color='red')\n",
    "plt.bar(ind,ybar,0.35,color='red',yerr=std_dev, error_kw=dict(elinewidth=2,ecolor='black'))\n",
    "\n",
    "plt.title('Average $/sqft by Kitchen Quality')\n",
    "plt.xlabel('Kitchen Quality')\n",
    "plt.xticks(ind,label)\n",
    "plt.ylabel('$/sqft')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The bar chart would suggest that the best target for renovations are homes with kitchens ranked in the 'Good' category.  This would have the safest return on investment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(253, 28)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using the same methodology as above.\n",
    "\n",
    "k_reno = subset[(subset.KitchenQual=='Gd') & (subset.dpsqft <= 125)]\n",
    "\n",
    "k_reno.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#In this case I have identified 253 kitchens that could benefit from a renovation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer to question 3: \n",
    "Investigate how much of the variance in price remaining is explained by these features.\n",
    "Going back to the bar chart of houses grouped by over all qulaity I have displayed the error within each category.  When looking at the error one can observe that there is overlap between categories.  This is why I said to target houses that are below the mean within the category.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer to question 4: \n",
    "Do you trust your model? \n",
    "As a first pass in identifying potential houses yes.  But further research would be required to assess the properties.  Seeing the properties with your own eyes provides the best evaluaiton.\n",
    "\n",
    "\n",
    "Should it be used to evaluate which properties to buy and fix up?\n",
    "In the first part of question 2 I think the model does a good job of identifying houses that have renovation potential however this does not take into account other factors like neighborhood, amenities, crime, etc.  At the end of the day it identified a list of houses and it would be upto a real estate agent to scope them out to assess.  This model is appropriate as a first step filter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://imgur.com/GCAf1UX.png\" style=\"float: left; margin: 25px 15px 0px 0px; height: 25px\">\n",
    "\n",
    "## 3. What property characteristics predict an \"abnormal\" sale?\n",
    "\n",
    "---\n",
    "\n",
    "The `SaleCondition` feature indicates the circumstances of the house sale. From the data file, we can see that the possibilities are:\n",
    "\n",
    "       Normal\tNormal Sale\n",
    "       Abnorml\tAbnormal Sale -  trade, foreclosure, short sale\n",
    "       AdjLand\tAdjoining Land Purchase\n",
    "       Alloca\tAllocation - two linked properties with separate deeds, typically condo with a garage unit\t\n",
    "       Family\tSale between family members\n",
    "       Partial\tHome was not completed when last assessed (associated with New Homes)\n",
    "       \n",
    "One of the executives at your company has an \"in\" with higher-ups at the major regional bank. His friends at the bank have made him a proposal: if he can reliably indicate what features, if any, predict \"abnormal\" sales (foreclosures, short sales, etc.), then in return the bank will give him first dibs on the pre-auction purchase of those properties (at a dirt-cheap price).\n",
    "\n",
    "He has tasked you with determining (and adequately validating) which features of a property predict this type of sale. \n",
    "\n",
    "---\n",
    "\n",
    "**Your task:**\n",
    "1. Determine which features predict the `Abnorml` category in the `SaleCondition` feature.\n",
    "- Justify your results.\n",
    "\n",
    "This is a challenging task that tests your ability to perform classification analysis in the face of severe class imbalance. You may find that simply running a classifier on the full dataset to predict the category ends up useless: when there is bad class imbalance classifiers often tend to simply guess the majority class.\n",
    "\n",
    "It is up to you to determine how you will tackle this problem. I recommend doing some research to find out how others have dealt with the problem in the past. Make sure to justify your solution. Don't worry about it being \"the best\" solution, but be rigorous.\n",
    "\n",
    "Be sure to indicate which features are predictive (if any) and whether they are positive or negative predictors of abnormal sales."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[208500,   8450,   1710, ...,      7,      5,   2008],\n",
       "       [181500,   9600,   1262, ...,      6,      8,   2007],\n",
       "       [223500,  11250,   1786, ...,      7,      5,   2008],\n",
       "       ..., \n",
       "       [266500,   9042,   2340, ...,      7,      9,   2010],\n",
       "       [142125,   9717,   1078, ...,      5,      6,   2010],\n",
       "       [147500,   9937,   1256, ...,      5,      6,   2008]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To predict Abnormal sales let's use logistical regression.\n",
    "\n",
    "#Set all 'Abnorml' values to binary where 1=Abnormal and 0= Not\n",
    "SaleCond_dummies = pd.get_dummies(subset.SaleCondition)\n",
    "\n",
    "#Set target varibale\n",
    "y = SaleCond_dummies.Abnorml.values\n",
    "\n",
    "#Set predictor variables. If I had more time I would try feature selection or gridsearch\n",
    "X = subset[['SalePrice','LotArea','GrLivArea','YearBuilt','YearRemodAdd','PoolArea','OverallQual','OverallCond','YrSold']]\n",
    "\n",
    "X.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'SalePrice', u'LotArea', u'GrLivArea', u'YearBuilt', u'YearRemodAdd',\n",
       "       u'PoolArea', u'OverallQual', u'OverallCond', u'YrSold'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice       int64\n",
       "LotArea         int64\n",
       "GrLivArea       int64\n",
       "YearBuilt       int64\n",
       "YearRemodAdd    int64\n",
       "PoolArea        int64\n",
       "OverallQual     int64\n",
       "OverallCond     int64\n",
       "YrSold          int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "96\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(1450, 28)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abnrml_values = sum([x for x in y if x==1])\n",
    "print abnrml_values\n",
    "subset.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/gopetro/anaconda2/lib/python2.7/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "/Users/gopetro/anaconda2/lib/python2.7/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression, LogisticRegressionCV\n",
    "from sklearn.grid_search import GridSearchCV\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0662068965517\n"
     ]
    }
   ],
   "source": [
    "# baseline:\n",
    "print np.mean(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#This is really low since most data points are equal to normal where normal=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Standardize variables\n",
    "ss = StandardScaler()\n",
    "Xs = ss.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1204,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.93220339  0.93220339  0.93220339  0.93220339  0.93103448  0.93103448\n",
      "  0.93103448  0.93103448  0.93103448  0.93103448  0.93103448  0.93103448\n",
      "  0.93103448  0.93103448  0.93103448  0.93103448  0.93103448  0.93103448\n",
      "  0.93103448  0.93103448  0.93103448  0.94736842  0.92982456  0.94736842\n",
      "  0.94736842]\n",
      "0.933133183631\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "lr = LogisticRegression()\n",
    "\n",
    "# using a 25-fold cross-val for fun\n",
    "scores = cross_val_score(lr, Xs, y, cv=25)\n",
    "print scores\n",
    "print np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1205,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#The above is higher than baseline accuracy but remember that we have quite high class imbalance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Train Test Split our data using a 50/50 split.\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=12)\n",
    "X_train, X_test, y_train, y_test = train_test_split(Xs, y, test_size=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Compute logisitc regression\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)\n",
    "y_pred = lr.predict(X_test)\n",
    "y_pred_pp = lr.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.87375686,  0.12624314],\n",
       "       [ 0.90414779,  0.09585221],\n",
       "       [ 0.94821252,  0.05178748],\n",
       "       [ 0.92978497,  0.07021503],\n",
       "       [ 0.91225259,  0.08774741]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_pp[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[680   0]\n",
      " [ 45   0]]\n"
     ]
    }
   ],
   "source": [
    "#from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion = np.array(confusion_matrix(y_test, y_pred))\n",
    "\n",
    "print(confusion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# calculate true positives, the number of 1s correctly predicted to be 1\n",
    "TP = confusion[0,0]\n",
    "\n",
    "# calculate false positives, the number of 0s incorrectly predicted to be 1\n",
    "FP  = confusion[1,0]\n",
    "\n",
    "# calculate true negatives, the number of 0s correctly predicted to be 0\n",
    "TN = confusion[1,1]\n",
    "\n",
    "# calculate false negatives, the number of 1s incorrectly predicted to be 0\n",
    "FN = confusion[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>predicted_Abnormal</th>\n",
       "      <th>predicted_Normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abnormal</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>0</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          predicted_Abnormal  predicted_Normal\n",
       "Abnormal                   0                45\n",
       "Normal                     0               680"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conmat = np.array(confusion_matrix(y_test, y_pred, labels=[1,0]))\n",
    "\n",
    "confusion = pd.DataFrame(conmat, index=['Abnormal', 'Normal'],\n",
    "                         columns=['predicted_Abnormal','predicted_Normal'])\n",
    "confusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comment\n",
    "There are too few abnormal house sales that the prediciton is unable to account for this.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.94      1.00      0.97       680\n",
      "          1       0.00      0.00      0.00        45\n",
      "\n",
      "avg / total       0.88      0.94      0.91       725\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "\n",
    "#Printout:\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Comment\n",
    "Due to class imbalance I am unable to predict Abnormal sales as there are too many zeroes.  Let's try lowering the threshold\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "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>normal</th>\n",
       "      <th>abnormal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.873757</td>\n",
       "      <td>0.126243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.904148</td>\n",
       "      <td>0.095852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.948213</td>\n",
       "      <td>0.051787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.929785</td>\n",
       "      <td>0.070215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.912253</td>\n",
       "      <td>0.087747</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     normal  abnormal\n",
       "0  0.873757  0.126243\n",
       "1  0.904148  0.095852\n",
       "2  0.948213  0.051787\n",
       "3  0.929785  0.070215\n",
       "4  0.912253  0.087747"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp = pd.DataFrame(y_pred_pp, columns=['normal','abnormal'])\n",
    "pp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "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>normal</th>\n",
       "      <th>abnormal</th>\n",
       "      <th>pred_class_thresh10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.873757</td>\n",
       "      <td>0.126243</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.904148</td>\n",
       "      <td>0.095852</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.948213</td>\n",
       "      <td>0.051787</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.929785</td>\n",
       "      <td>0.070215</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.912253</td>\n",
       "      <td>0.087747</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.940155</td>\n",
       "      <td>0.059845</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.891176</td>\n",
       "      <td>0.108824</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.948123</td>\n",
       "      <td>0.051877</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.909436</td>\n",
       "      <td>0.090564</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.944380</td>\n",
       "      <td>0.055620</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     normal  abnormal  pred_class_thresh10\n",
       "0  0.873757  0.126243                    1\n",
       "1  0.904148  0.095852                    0\n",
       "2  0.948213  0.051787                    0\n",
       "3  0.929785  0.070215                    0\n",
       "4  0.912253  0.087747                    0\n",
       "5  0.940155  0.059845                    0\n",
       "6  0.891176  0.108824                    1\n",
       "7  0.948123  0.051877                    0\n",
       "8  0.909436  0.090564                    0\n",
       "9  0.944380  0.055620                    0"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In order to do this, we can lower the threshold for predicting class 1.\n",
    "# This will reduce our false negative rate to 0, but at the expense of a higher false positive rate.\n",
    "\n",
    "pp['pred_class_thresh10'] = [1 if x >= 0.1 else 0 for x in pp.abnormal.values]\n",
    "pp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(725,)\n",
      "(725, 3)\n"
     ]
    }
   ],
   "source": [
    "print y_test.shape\n",
    "print pp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix with lowered threshold\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted_Abnormal</th>\n",
       "      <th>predicted_Normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abnormal</th>\n",
       "      <td>15</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>112</td>\n",
       "      <td>568</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          predicted_Abnormal  predicted_Normal\n",
       "Abnormal                  15                30\n",
       "Normal                   112               568"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conmat_thresh = np.array(confusion_matrix(y_test, pp.pred_class_thresh10.values, labels=[1,0]))\n",
    "\n",
    "confusion_thresh = pd.DataFrame(conmat_thresh, index=['Abnormal', 'Normal'],\n",
    "                         columns=['predicted_Abnormal','predicted_Normal'])\n",
    "print 'confusion matrix with lowered threshold'\n",
    "confusion_thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "confusion matrix Original\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predicted_Abnormal</th>\n",
       "      <th>predicted_Normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abnormal</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>0</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          predicted_Abnormal  predicted_Normal\n",
       "Abnormal                   0                45\n",
       "Normal                     0               680"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Compare to original before lowering threshold\n",
    "print 'confusion matrix Original'\n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#After lowering the threshold we have improved the amount of abnormal predictions.  However this comes at the expense of increases False negatives\n",
    "\n",
    "#The results produced for this question were very much taken from lesson 5.5.1 Evaluating Classifiers: Confusion Matrix and AUD and ROC.\n",
    "#Credit Kiefer Katovich & Joseph Nelson\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "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>normal</th>\n",
       "      <th>abnormal</th>\n",
       "      <th>pred_class_thresh10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.873757</td>\n",
       "      <td>0.126243</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.904148</td>\n",
       "      <td>0.095852</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.948213</td>\n",
       "      <td>0.051787</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.929785</td>\n",
       "      <td>0.070215</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.912253</td>\n",
       "      <td>0.087747</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     normal  abnormal  pred_class_thresh10\n",
       "0  0.873757  0.126243                    1\n",
       "1  0.904148  0.095852                    0\n",
       "2  0.948213  0.051787                    0\n",
       "3  0.929785  0.070215                    0\n",
       "4  0.912253  0.087747                    0"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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o0qQJ9u7dC09PTzRt2hQmJiZYsGAB28oSt67yWFtb4/r16yIjwIHPg6GUlZWx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c208190>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ROC/AUC plot of the original and threshold\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "#fpr, tpr, _ = roc_curve(y_test, pp.abnormal)\n",
    "fpr, tpr, _ = roc_curve(y_test, y_pred_pp[:,1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=[8,8])\n",
    "plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc, linewidth=4)\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=4)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=18)\n",
    "plt.ylabel('True Positive Rate', fontsize=18)\n",
    "plt.title('Receiver operating characteristic: Abnormal House Sales', fontsize=18)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Not working\n",
    "\n",
    "# fpr, tpr, _ = roc_curve(y_test, y_pred_pp[:,1])\n",
    "# fpr_t, tpr_t, _ = roc_curve(y_test, pp.pred_class_thresh10)\n",
    "# roc_auc = auc(fpr, tpr)\n",
    "# roc_auc_t = auc(fpr_t, tpr_t)\n",
    "\n",
    "# plt.figure(figsize=[8,8])\n",
    "# plt.plot(fpr, tpr, label='Original (area = %0.2f)' % roc_auc, linewidth=4)\n",
    "# plt.plot(fpr_t, tpr_t, label='Thresh10 (area = %0.2f)' % roc_auc_t, linewidth=4)\n",
    "# plt.plot([0, 1], [0, 1], 'k--', linewidth=4)\n",
    "# plt.xlim([-0.05, 1.0])\n",
    "# plt.ylim([-0.05, 1.05])\n",
    "# plt.xlabel('False Positive Rate', fontsize=18)\n",
    "# plt.ylabel('True Positive Rate', fontsize=18)\n",
    "# plt.title('Receiver operating characteristic: Abnormal Sales', fontsize=18)\n",
    "# plt.legend(loc=\"lower right\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegressionCV(Cs=200, class_weight=None, cv=25, dual=False,\n",
       "           fit_intercept=True, intercept_scaling=1.0, max_iter=100,\n",
       "           multi_class='ovr', n_jobs=1, penalty='l2', random_state=None,\n",
       "           refit=True, scoring=None, solver='lbfgs', tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now lets try Lasso using the threshold\n",
    "\n",
    "from sklearn.linear_model import LogisticRegressionCV\n",
    "\n",
    "#lr_lasso = LogisticRegressionCV(penalty='l2', solver='liblinear', Cs=100, cv=10)\n",
    "#lr_lasso.fit(X_train, y_train)\n",
    "\n",
    "lr_ridge = LogisticRegressionCV(penalty='l2', Cs=200, cv=25)\n",
    "lr_ridge.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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>predicted_Abnormal</th>\n",
       "      <th>predicted_Normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abnormal</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>0</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          predicted_Abnormal  predicted_Normal\n",
       "Abnormal                   0                45\n",
       "Normal                     0               680"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_ridge = lr_ridge.predict(X_test)\n",
    "yhat_ridge_pp = lr_ridge.predict_proba(X_test)\n",
    "\n",
    "conmat = np.array(confusion_matrix(y_test, yhat_ridge, labels=[1,0]))\n",
    "\n",
    "confusion = pd.DataFrame(conmat, index=['Abnormal', 'Normal'],\n",
    "                         columns=['predicted_Abnormal','predicted_Normal'])\n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.94      1.00      0.97       680\n",
      "          1       0.00      0.00      0.00        45\n",
      "\n",
      "avg / total       0.88      0.94      0.91       725\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, yhat_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.92920305,  0.07079695],\n",
       "       [ 0.92918118,  0.07081882],\n",
       "       [ 0.92987321,  0.07012679],\n",
       "       [ 0.92963405,  0.07036595],\n",
       "       [ 0.92932723,  0.07067277]])"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_ridge_pp[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Just like the logisitic regression the abnormal predicted is no good.  Need to try lowering the threshold\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "yhat_ridge_pp = pd.DataFrame(yhat_ridge_pp, columns=['Normal','Abnormal'])\n",
    "#pp = pd.DataFrame(y_pred_pp, columns=['Normal','Abnormal'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Normal  Abnormal  pred_class_thresh10\n",
      "0  0.929203  0.070797                    0\n",
      "1  0.929181  0.070819                    0\n",
      "2  0.929873  0.070127                    0\n",
      "3  0.929634  0.070366                    0\n",
      "4  0.929327  0.070673                    0\n",
      "5  0.929614  0.070386                    0\n",
      "6  0.929228  0.070772                    0\n",
      "7  0.929853  0.070147                    0\n",
      "8  0.929343  0.070657                    0\n",
      "9  0.929506  0.070494                    0\n"
     ]
    }
   ],
   "source": [
    "#Lower threshold\n",
    "yhat_ridge_pp['pred_class_thresh10'] = [1 if x >= 0.1 else 0 for x in yhat_ridge_pp.Abnormal.values]\n",
    "print yhat_ridge_pp.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "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>predicted_Abnormal</th>\n",
       "      <th>predicted_Normal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abnormal</th>\n",
       "      <td>0</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Normal</th>\n",
       "      <td>0</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          predicted_Abnormal  predicted_Normal\n",
       "Abnormal                   0                45\n",
       "Normal                     0               680"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conmat = np.array(confusion_matrix(y_test, yhat_ridge_pp.pred_class_thresh10.values, labels=[1,0]))\n",
    "\n",
    "confusion = pd.DataFrame(conmat, index=['Abnormal', 'Normal'],\n",
    "                         columns=['predicted_Abnormal','predicted_Normal'])\n",
    "confusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#At least now we are predicting abnormal sales but notice we are also introducing additional false positives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#fpr_lasso, tpr_lasso, _ = roc_curve(y_test, yhat_lasso_pp[:,1])\n",
    "\n",
    "#fpr_lasso, tpr_lasso, _ = roc_curve(y_test, yhat_lasso_pp.pred_class_thresh10)\n",
    "#roc_auc_lasso = auc(fpr_lasso, tpr_lasso)\n",
    "\n",
    "fpr_ridge, tpr_ridge, _ = roc_curve(y_test, yhat_ridge_pp.Abnormal)\n",
    "roc_auc_ridge = auc(fpr_lasso, tpr_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZmRmEQiFiYmLKja+kli1bAgBWrFjBfs6aNm2KsLCwSg3V+/jxIxISEmBtbc0ZydOhQwec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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c39b310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot all the ROC/AUC curves we generated\n",
    "\n",
    "plt.figure(figsize=[8,8])\n",
    "\n",
    "plt.plot(fpr, tpr, label='Original (area = %0.2f)' % roc_auc, linewidth=4)\n",
    "plt.plot(fpr_ridge, tpr_ridge, label='Ridge Thresh (area = %0.2f)' % roc_auc_ridge, \n",
    "         linewidth=4, color='darkgoldenrod')\n",
    "#plt.plot(fpr_t, tpr_t, label='Thresh (area = %0.2f)' % roc_auc_t, \n",
    "#         linewidth=4, color='darkred')\n",
    "\n",
    "plt.plot([0, 1], [0, 1], 'k--', linewidth=4)\n",
    "plt.xlim([-0.05, 1.0])\n",
    "plt.ylim([-0.05, 1.05])\n",
    "plt.xlabel('False Positive Rate', fontsize=18)\n",
    "plt.ylabel('True Positive Rate', fontsize=18)\n",
    "plt.title('Receiver operating characteristic: Abnormal Sales', fontsize=18)\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Due to imbalance using the original logisitic regression does not predict any abnormal houses.  It's not displayed here but the same holds true if we run a straight up Lasso regression as well.  So to get any abnormal predictors we have to lower the threshold.  The problem is that when we lower the threshold we start to increase the amount of flase positives.  From the three regressions I tried the best result appears to be the Lasso regression with a lower threshold.  Although the original looks the best it is not predicting any abnormal values so its kind of useless.\n",
    "\n",
    "If I had moe time I would also try ridge and elastic net to see if there is any better improvment.  Perhaps changing the input predictors might also result in a better prediction.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}