notebook/kaggle_house_price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T06:56:27.668267Z",
     "start_time": "2018-12-19T06:56:27.036379Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.20.1'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "sklearn.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T06:53:06.364515Z",
     "start_time": "2018-12-19T06:53:06.243907Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[31mdata_description.txt\u001b[m\u001b[m     \u001b[31msample_submission.csv\u001b[m\u001b[m    \u001b[31mtrain.csv\u001b[m\u001b[m\r\n",
      "kaggle_house_price.ipynb \u001b[31mtest.csv\u001b[m\u001b[m\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T06:53:21.347721Z",
     "start_time": "2018-12-19T06:53:21.323381Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T06:53:34.232934Z",
     "start_time": "2018-12-19T06:53:34.227402Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature extraction\n",
    "\n",
    "\n",
    "## From Wikipedia\n",
    "\n",
    "In **machine learning**, *pattern recognition*, and image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. Feature extraction is related to [dimensionality reduction](https://en.wikipedia.org/wiki/Feature_selection).\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "### Contents\n",
    "\n",
    "- General\n",
    "- Image processing\n",
    "- Implementations\n",
    "- See also\n",
    "- References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{align}\n",
    "\\nabla \\times \\vec{\\mathbf{B}} -\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{E}}}{\\partial t} & = \\frac{4\\pi}{c}\\vec{\\mathbf{j}} \\\\   \\nabla \\cdot \\vec{\\mathbf{E}} & = 4 \\pi \\rho \\\\\n",
    "\\nabla \\times \\vec{\\mathbf{E}}\\, +\\, \\frac1c\\, \\frac{\\partial\\vec{\\mathbf{B}}}{\\partial t} & = \\vec{\\mathbf{0}} \\\\\n",
    "\\nabla \\cdot \\vec{\\mathbf{B}} & = 0\n",
    "\\end{align}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T06:53:55.600856Z",
     "start_time": "2018-12-19T06:53:55.561895Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.get_dummies(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T06:54:00.782627Z",
     "start_time": "2018-12-19T06:54:00.756824Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>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>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 290 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0   1          60         65.0     8450            7            5       2003   \n",
       "1   2          20         80.0     9600            6            8       1976   \n",
       "2   3          60         68.0    11250            7            5       2001   \n",
       "3   4          70         60.0     9550            7            5       1915   \n",
       "4   5          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1          ...            \\\n",
       "0          2003       196.0         706          ...             \n",
       "1          1976         0.0         978          ...             \n",
       "2          2002       162.0         486          ...             \n",
       "3          1970         0.0         216          ...             \n",
       "4          2000       350.0         655          ...             \n",
       "\n",
       "   SaleType_ConLw  SaleType_New  SaleType_Oth  SaleType_WD  \\\n",
       "0             0.0           0.0           0.0          1.0   \n",
       "1             0.0           0.0           0.0          1.0   \n",
       "2             0.0           0.0           0.0          1.0   \n",
       "3             0.0           0.0           0.0          1.0   \n",
       "4             0.0           0.0           0.0          1.0   \n",
       "\n",
       "   SaleCondition_Abnorml  SaleCondition_AdjLand  SaleCondition_Alloca  \\\n",
       "0                    0.0                    0.0                   0.0   \n",
       "1                    0.0                    0.0                   0.0   \n",
       "2                    0.0                    0.0                   0.0   \n",
       "3                    1.0                    0.0                   0.0   \n",
       "4                    0.0                    0.0                   0.0   \n",
       "\n",
       "   SaleCondition_Family  SaleCondition_Normal  SaleCondition_Partial  \n",
       "0                   0.0                   1.0                    0.0  \n",
       "1                   0.0                   1.0                    0.0  \n",
       "2                   0.0                   1.0                    0.0  \n",
       "3                   0.0                   0.0                    0.0  \n",
       "4                   0.0                   1.0                    0.0  \n",
       "\n",
       "[5 rows x 290 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T06:55:40.746879Z",
     "start_time": "2018-12-19T06:55:40.462492Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/datalab/Applications/anaconda/lib/python3.5/site-packages/numpy/lib/function_base.py:3834: RuntimeWarning: Invalid value encountered in percentile\n",
      "  RuntimeWarning)\n"
     ]
    },
    {
     "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>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>...</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>0.083562</td>\n",
       "      <td>0.002055</td>\n",
       "      <td>0.867808</td>\n",
       "      <td>0.069178</td>\n",
       "      <td>0.002740</td>\n",
       "      <td>0.008219</td>\n",
       "      <td>0.013699</td>\n",
       "      <td>0.820548</td>\n",
       "      <td>0.085616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058440</td>\n",
       "      <td>0.276824</td>\n",
       "      <td>0.045299</td>\n",
       "      <td>0.338815</td>\n",
       "      <td>0.253844</td>\n",
       "      <td>0.052289</td>\n",
       "      <td>0.090317</td>\n",
       "      <td>0.116277</td>\n",
       "      <td>0.383862</td>\n",
       "      <td>0.279893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\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>...</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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>NaN</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>1.000000</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</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>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\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>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 290 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000          NaN    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000          NaN    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000          NaN   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000   \n",
       "25%       5.000000  1954.000000   1967.000000          NaN     0.000000   \n",
       "50%       5.000000  1973.000000   1994.000000          NaN   383.500000   \n",
       "75%       6.000000  2000.000000   2004.000000          NaN   712.250000   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
       "\n",
       "               ...            SaleType_ConLw  SaleType_New  SaleType_Oth  \\\n",
       "count          ...               1460.000000   1460.000000   1460.000000   \n",
       "mean           ...                  0.003425      0.083562      0.002055   \n",
       "std            ...                  0.058440      0.276824      0.045299   \n",
       "min            ...                  0.000000      0.000000      0.000000   \n",
       "25%            ...                  0.000000      0.000000      0.000000   \n",
       "50%            ...                  0.000000      0.000000      0.000000   \n",
       "75%            ...                  0.000000      0.000000      0.000000   \n",
       "max            ...                  1.000000      1.000000      1.000000   \n",
       "\n",
       "       SaleType_WD  SaleCondition_Abnorml  SaleCondition_AdjLand  \\\n",
       "count  1460.000000            1460.000000            1460.000000   \n",
       "mean      0.867808               0.069178               0.002740   \n",
       "std       0.338815               0.253844               0.052289   \n",
       "min       0.000000               0.000000               0.000000   \n",
       "25%       1.000000               0.000000               0.000000   \n",
       "50%       1.000000               0.000000               0.000000   \n",
       "75%       1.000000               0.000000               0.000000   \n",
       "max       1.000000               1.000000               1.000000   \n",
       "\n",
       "       SaleCondition_Alloca  SaleCondition_Family  SaleCondition_Normal  \\\n",
       "count           1460.000000           1460.000000           1460.000000   \n",
       "mean               0.008219              0.013699              0.820548   \n",
       "std                0.090317              0.116277              0.383862   \n",
       "min                0.000000              0.000000              0.000000   \n",
       "25%                0.000000              0.000000              1.000000   \n",
       "50%                0.000000              0.000000              1.000000   \n",
       "75%                0.000000              0.000000              1.000000   \n",
       "max                1.000000              1.000000              1.000000   \n",
       "\n",
       "       SaleCondition_Partial  \n",
       "count            1460.000000  \n",
       "mean                0.085616  \n",
       "std                 0.279893  \n",
       "min                 0.000000  \n",
       "25%                 0.000000  \n",
       "50%                 0.000000  \n",
       "75%                 0.000000  \n",
       "max                 1.000000  \n",
       "\n",
       "[8 rows x 290 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T06:57:34.554784Z",
     "start_time": "2018-12-19T06:57:34.091846Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['SalePrice'], bins = 50, histtype = 'step')\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('SalePrice', fontsize = 20)\n",
    "plt.ylabel('frequency', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:15:47.105958Z",
     "start_time": "2018-12-19T08:15:47.029473Z"
    }
   },
   "outputs": [],
   "source": [
    "df = df.fillna(df.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:15:49.206816Z",
     "start_time": "2018-12-19T08:15:49.200093Z"
    }
   },
   "outputs": [],
   "source": [
    "X = df.drop(['SalePrice', 'Id'], axis = 1)\n",
    "y = df['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:15:49.859850Z",
     "start_time": "2018-12-19T08:15:49.849446Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, random_state=1, train_size = 0.8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:15:51.493706Z",
     "start_time": "2018-12-19T08:15:51.242386Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "# Classifiers need integer or string labels.\n",
    "model = GaussianNB()\n",
    "model.fit(Xtrain, ytrain.astype(int))\n",
    "y_model = model.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:16:31.974068Z",
     "start_time": "2018-12-19T08:16:31.435569Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_model, ytest, 'ro', alpha = 0.2)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('y_pred', fontsize = 20)\n",
    "plt.ylabel('y_test', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T15:27:47.994748Z",
     "start_time": "2018-12-19T15:27:47.980237Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score, explained_variance_score, roc_auc_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def rmsel(y_true,y_pre):\n",
    "    return mean_squared_error(y_true,y_pre)**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:20:37.137738Z",
     "start_time": "2018-12-19T08:20:37.128911Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 55301.7450573\n",
      "R2 score: 0.571\n",
      "Variance score: 0.580\n"
     ]
    }
   ],
   "source": [
    "y_model = y_model.astype('int')\n",
    "ytest = ytest.astype('int')\n",
    "# model evaluation\n",
    "# https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter\n",
    "rmse = rmsel(ytest, y_model)\n",
    "print('RMSE:', rmse)\n",
    "# Explained variance score: 1 is perfect prediction\n",
    "print('R2 score: %.3f' % r2_score(ytest, y_model))\n",
    "print('Variance score: %.3f' % explained_variance_score(ytest, y_model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:20:11.017695Z",
     "start_time": "2018-12-19T08:20:09.811115Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.56477106747111616"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def r2_cv(model):\n",
    "    r2= cross_val_score(model, X, y, scoring=\"r2\", cv = 5)\n",
    "    return r2\n",
    "\n",
    "np.mean(r2_cv(model))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:21:34.822387Z",
     "start_time": "2018-12-19T08:21:33.649387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5740418611970538"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def r2_cv(model):\n",
    "    r2= cross_val_score(model, X, y, scoring=\"explained_variance\", cv = 5)\n",
    "    return r2\n",
    "\n",
    "np.mean(r2_cv(model))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:22:19.792015Z",
     "start_time": "2018-12-19T08:22:15.335730Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=200, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "forest = RandomForestRegressor(n_estimators = 200)\n",
    "forest.fit(Xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:22:32.076500Z",
     "start_time": "2018-12-19T08:22:32.050764Z"
    }
   },
   "outputs": [],
   "source": [
    "y_pred = forest.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:22:50.243899Z",
     "start_time": "2018-12-19T08:22:49.801788Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_pred, ytest, 'ro', alpha = 0.2)\n",
    "plt.xscale('log')\n",
    "plt.yscale('log')\n",
    "plt.xlabel('y_pred', fontsize = 20)\n",
    "plt.ylabel('y_test', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-30T04:42:49.202470Z",
     "start_time": "2018-12-30T04:42:48.660013Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score, explained_variance_score, roc_auc_score\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def rmse(y_true,y_pre):\n",
    "    return mean_squared_error(y_true,y_pre)**0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:23:42.819831Z",
     "start_time": "2018-12-19T08:23:42.812868Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 29553.4130396\n",
      "R2 score: 0.878\n",
      "Variance score: 0.878\n"
     ]
    }
   ],
   "source": [
    "rmse = rmsel(ytest, y_pred)\n",
    "print('RMSE:', rmse)\n",
    "print('R2 score: %.3f' % r2_score(ytest, y_pred))\n",
    "print('Variance score: %.3f' % explained_variance_score(ytest, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:26:15.668919Z",
     "start_time": "2018-12-19T08:25:54.438055Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85339663069809579"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def r2_cv(model):\n",
    "    r2= cross_val_score(model, X, y, scoring=\"r2\", cv = 5)\n",
    "    return r2\n",
    "\n",
    "np.mean(r2_cv(forest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-12-19T08:27:21.462066Z",
     "start_time": "2018-12-19T08:26:59.454445Z"
    }
   },
   "outputs": [
    {
     "data": {
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    "def r2_cv(model):\n",
    "    r2= cross_val_score(model, X, y, scoring=\"explained_variance\", cv = 5)\n",
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    "\n",
    "np.mean(r2_cv(forest))"
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