final_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center> Group Project 1</center> <br> <center> Machine Learning (CSE 627) <a href=\"https://github.com/Chitraketu/cse627project1.git\"><small>Repo</small></a></center>\n",
    "\n",
    "<br>\n",
    "<center> <b>Chitraketu Pandey</b> </center>\n",
    "<center> <b>Hui Shang</b> </center>\n",
    "<center> <b>Hemraj Ojha</b> </center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " For this assignment we selected a project from Kaggle competition. The project is a prediction of house sale prices based on different 79 explanatory variables(columns) describing almost every aspect of residential homes in Ames, Iowa. Some of the columns are total area, number of storeys, area of swimming pool, status of kitchen, restrooms, etc. This competition challenges to predict the final price of each home.The link of the kaggle competition is [here](https://www.kaggle.com/c/house-prices-advanced-regression-techniques)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This competition aimed at predicting the prices of houses using advanced regression techniques. \n",
    "For that we used algorithms like, RandomForestRegression, GradientBoosting, Ensembling of these\n",
    "models and also performed grid search for hyper parameter optimization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset for this project was taken from [here](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data). \n",
    "The small sample of our dataset is provided below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we took insights from the following\n",
    "We took insights to use gradient boosting and different pre-processing from [this]() kernel "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Methodology\n",
    "<img src=\"methodology_flowchart.png\" />"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## This notebook contains the following sections:\n",
    "###  1. <a href=\"#Imports\" > Imports </a>\n",
    "###  2. <a href=\"#viz\" >Visualization </a>\n",
    "###  3. <a href=\"#pre\" >Pre-processing </a>\n",
    "###  4. <a href=\"#pre\" >Training </a>\n",
    "###  5. <a href=\"#eval\">Evaluation </a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1. <a name=\"Imports\" /> Imports\n",
    "\n",
    "The necessary imports for the project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "from scipy.stats import norm\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from scipy import stats\n",
    "import warnings\n",
    "from sklearn.ensemble import GradientBoostingRegressor as GBR\n",
    "from sklearn.ensemble import RandomForestRegressor as RFG\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.metrics import mean_squared_error as mse\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <a name=\"viz\"/>2. Visualization\n",
    "The training dataset sample, list of columns, the description of target column, scatter plot of some columns vs. the target column, list of columns with more than one nulls, distribution of data in all the attributes, correlation between every combination of columns are provided.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>14115</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>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>143000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10084</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>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>307000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10382</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>Shed</td>\n",
       "      <td>350</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>RM</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6120</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>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>129900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>190</td>\n",
       "      <td>RL</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7420</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>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 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",
       "5   6          50       RL         85.0    14115   Pave   NaN      IR1   \n",
       "6   7          20       RL         75.0    10084   Pave   NaN      Reg   \n",
       "7   8          60       RL          NaN    10382   Pave   NaN      IR1   \n",
       "8   9          50       RM         51.0     6120   Pave   NaN      Reg   \n",
       "9  10         190       RL         50.0     7420   Pave   NaN      Reg   \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",
       "5         Lvl    AllPub    ...            0    NaN  MnPrv        Shed     700   \n",
       "6         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "7         Lvl    AllPub    ...            0    NaN    NaN        Shed     350   \n",
       "8         Lvl    AllPub    ...            0    NaN    NaN         NaN       0   \n",
       "9         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",
       "5     10   2009        WD         Normal     143000  \n",
       "6      8   2007        WD         Normal     307000  \n",
       "7     11   2009        WD         Normal     200000  \n",
       "8      4   2008        WD        Abnorml     129900  \n",
       "9      1   2008        WD         Normal     118000  \n",
       "\n",
       "[10 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('dataset/train.csv')\n",
    "df.head(10)\n",
    "#the first 10 rows from datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the list of columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      1460.000000\n",
       "mean     180921.195890\n",
       "std       79442.502883\n",
       "min       34900.000000\n",
       "25%      129975.000000\n",
       "50%      163000.000000\n",
       "75%      214000.000000\n",
       "max      755000.000000\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description of the target column which is 'SalePrice'\n",
    "df['SalePrice'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# OverallQual: Overall material and finish quality\n",
    "# Showing relation of an attribute 'OverallQual' with\n",
    "# the target column 'SalePrice'\n",
    "# X-axis represents the overallQual\n",
    "# Y-axis represents the corresponding SalePrice for that value of OverallQual\n",
    "var = \"OverallQual\"\n",
    "data = pd.concat([df['SalePrice'],df[var]], axis=1)  \n",
    "data.plot.scatter(x=var, y='SalePrice', ylim=(0,800000));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# TotalBSmtSF: Total square feet of basement area\n",
    "# Showing relation of an attribute 'TotalBSmtSF' with\n",
    "# the target column 'SalePrice'\n",
    "# X-axis represents the overallQual\n",
    "# Y-axis represents the corresponding SalePrice for that value of OverallQual\n",
    "var = 'TotalBsmtSF'\n",
    "data = pd.concat([df['SalePrice'], df[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='SalePrice', ylim=(0,800000));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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>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>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>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</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>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</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>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</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>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>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</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>0.000000</td>\n",
       "      <td>383.500000</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>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</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>166.000000</td>\n",
       "      <td>712.250000</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>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>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 × 38 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    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   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     0.000000     0.000000   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
       "\n",
       "           ...         WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  \\\n",
       "count      ...        1460.000000  1460.000000    1460.000000  1460.000000   \n",
       "mean       ...          94.244521    46.660274      21.954110     3.409589   \n",
       "std        ...         125.338794    66.256028      61.119149    29.317331   \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  1460.000000  1460.000000   1460.000000  1460.000000  1460.000000   \n",
       "mean     15.060959     2.758904     43.489041     6.321918  2007.815753   \n",
       "std      55.757415    40.177307    496.123024     2.703626     1.328095   \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    1460.000000  \n",
       "mean   180921.195890  \n",
       "std     79442.502883  \n",
       "min     34900.000000  \n",
       "25%    129975.000000  \n",
       "50%    163000.000000  \n",
       "75%    214000.000000  \n",
       "max    755000.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical summary of all the attributes of the given dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PoolQC          1453\n",
       "MiscFeature     1406\n",
       "Alley           1369\n",
       "Fence           1179\n",
       "FireplaceQu      690\n",
       "LotFrontage      259\n",
       "GarageCond        81\n",
       "GarageType        81\n",
       "GarageYrBlt       81\n",
       "GarageFinish      81\n",
       "GarageQual        81\n",
       "BsmtExposure      38\n",
       "BsmtFinType2      38\n",
       "BsmtFinType1      37\n",
       "BsmtCond          37\n",
       "BsmtQual          37\n",
       "MasVnrArea         8\n",
       "MasVnrType         8\n",
       "Electrical         1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying the attributes which have more than zero null values\n",
    "count_null = df.isnull().sum().sort_values(ascending=False)\n",
    "count_null = count_null[count_null > 0]\n",
    "count_null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# showing that the salesprice is not normally distributed\n",
    "sns.distplot(df['SalePrice'],fit=norm);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x2160 with 42 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# features distribution\n",
    "df.hist(figsize=(20,30))\n",
    "plt.figure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9ce606bd30>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation between every combination of attributes \n",
    "corrmat = df.corr()\n",
    "f, ax = plt.subplots(figsize=(20, 9))\n",
    "sns.heatmap(corrmat, vmax=0.8, square=True)\n",
    "# The figure shows that 'OverallQual' is most highly correlated with SalePrice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"pre\" /> 3. Preprocessing and Training\n",
    "\n",
    "After getting the datasets, pre-processing steps were performed as following:\n",
    "1. Removed the columns which had more than (threshold)50 empty cells.\n",
    "2. For categorical type of attributes two different ways of feeding to model was used.\n",
    "    > i. Replaced all the strings of each categorical attributes with integers <br>\n",
    "    > ii. Used One-Hot encoding for the categorical attributes\n",
    "3. Imputation:\n",
    "   Replaced all the null values for continuous data with median and all the categorical data with mode.\n",
    "4. Based on step(2) the columns were changed for training data\n",
    "    > i. For the replaced strings, the columns were used as it is <br>\n",
    "    > ii. For one-hot encoded attributes, the columns of train and test were matched by removing the extra columns of train dataset.\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "'''\n",
    "@params:\n",
    "    filename: a filename of the description.txt from kaggle competition describing each columns of dataset\n",
    "@returns:\n",
    "    dictionary of mapping for each string type of categorical values for each column with an integer\n",
    "'''\n",
    "def buildJSON(filename):\n",
    "    with open(filename,'r') as f:\n",
    "        d = dict()\n",
    "        temp = ''\n",
    "        count = 0\n",
    "        for l in f:\n",
    "            if l.strip() == '':continue\n",
    "            if ':' in l:\n",
    "                temp = l.split(':')[0]\n",
    "                d[temp] = dict()\n",
    "                count = 0\n",
    "            else:\n",
    "                lst = l.strip().split('\\t')\n",
    "                try:\n",
    "                    int(lst[0])\n",
    "                except ValueError:\n",
    "                    d[temp][lst[0]] = count\n",
    "                count += 1\n",
    "    return d\n",
    "#print(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "@params\n",
    "    df: a dataframe from which the null columns are to be removied\n",
    "    threshold: the total count of nulls in the column to decide whether to remove that column or not\n",
    "@returns:\n",
    "    dataframe with deleted columns based on the threshold from the provided dataframe 'df'\n",
    "'''\n",
    "def removeableCols(df,threshold):\n",
    "    count_null = sum(pd.isnull(df[df.columns]),axis=0)\n",
    "    return count_null[count_null > threshold].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "@params\n",
    "    model: the model used for evaluation\n",
    "    X: dataframe with input columns\n",
    "    y: Series with output column\n",
    "@returns: \n",
    "    average error of stratified k fold trained models\n",
    "'''\n",
    "def stratifiedKFoldEvaluate(model,X,y):\n",
    "    #using stratified k fold for evaluating the model\n",
    "    skf = StratifiedKFold(n_splits=5)\n",
    "    skf.get_n_splits(X, y)\n",
    "    errors = []\n",
    "    for train_index,test_index in skf.split(X,y):\n",
    "        X_train, X_test = X.iloc[train_index],X.iloc[test_index]\n",
    "        y_train, y_test = y.iloc[train_index],y.iloc[test_index]\n",
    "        r = model()\n",
    "        r.fit(X_train,y_train)\n",
    "        y_pred = r.predict(X_test)\n",
    "        #print(mse(y_test,y_pred))\n",
    "        errors.append(mse(y_test,y_pred))\n",
    "\n",
    "    return sum(errors)/len(errors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# array to store error of different models\n",
    "error_list = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Pre-processing step 2. (i)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1. Replacing all the categorical string data with integers\n",
    "filename = 'dataset/data_description.txt'\n",
    "d = buildJSON(filename)\n",
    "df = pd.read_csv('dataset/train.csv')\n",
    "removColList = removeableCols(df,50)\n",
    "processed_df = df.drop(removColList, axis=1)\n",
    "\n",
    "#replaced all the strings with integers\n",
    "processed_df = processed_df.replace(d)\n",
    "\n",
    "# filling all the empty cells with the median of the column\n",
    "processed_df = processed_df.fillna(processed_df.median(axis=0))\n",
    "y = processed_df['SalePrice']\n",
    "X = processed_df.drop('SalePrice',axis=1)\n",
    "cols = X.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting the test data set\n",
    "test_df = pandas.read_csv('dataset/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Learning models  from pre-processed data 2. (i)\n",
    "The regression models used were random forest regressor and gradient boosting regressor and ensembling both of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Model 1 : Random Forest Regressor\n",
    "\n",
    "\n",
    "#using stratified k fold for evaluating the model 1\n",
    "\n",
    "error_list.append(stratifiedKFoldEvaluate(RFG,X,y))\n",
    "\n",
    "rf = RFG(100)\n",
    "rf.fit(X,y)\n",
    "test_df = test_df[cols]\n",
    "test_df = test_df.replace(d)\n",
    "test_df = test_df.fillna(processed_df.median(axis=0))\n",
    "y_pred = rf.predict(test_df)\n",
    "\n",
    "#getting the submission 1\n",
    "submission = pandas.concat([test_df['Id'],pandas.Series(y_pred,name='SalePrice')],axis=1)\n",
    "submission.to_csv('dataset/submission_1.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Model 2 : Gradient Boosting Regressor \n",
    "\n",
    "\n",
    "#using stratified k fold for evaluating the model 2\n",
    "error_list.append(stratifiedKFoldEvaluate(GBR,X,y))\n",
    "\n",
    "\n",
    "gbr = GBR()\n",
    "gbr.fit(X,y)\n",
    "y_pred = gbr.predict(test_df)\n",
    "\n",
    "#getting the submission 2\n",
    "submission = pandas.concat([test_df['Id'],pandas.Series(y_pred,name='SalePrice')],axis=1)\n",
    "submission.to_csv('dataset/submission_2.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Model 3: Ensembling the above two models.\n",
    "\n",
    "#evaluating the error of ensemble model 3 \n",
    "error_list.append((error_list[0]+error_list[1])/2)\n",
    "# ensembling the output of random forest regressor and gradient boosting regressor\n",
    "y_pred = (gbr.predict(test_df)+rf.predict(test_df))/2\n",
    "\n",
    "#getting the submission 3\n",
    "submission = pandas.concat([test_df['Id'],pandas.Series(y_pred,name='SalePrice')],axis=1)\n",
    "submission.to_csv('dataset/submission_3.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Pre-processing step 2. (ii)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 251)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove columns which have more than 50(threshold) nulls\n",
    "removColList = removeableCols(df,50)\n",
    "df = df.drop(removColList,axis=1)\n",
    "\n",
    "#split dataframe into two with categorical and continuous\n",
    "cats = [col for col,x in df.dtypes.items() if x == 'object']\n",
    "cat_df = df[cats]\n",
    "\n",
    "cont_df = df.drop(cats,axis=1)\n",
    "cat_df = cat_df.fillna(cat_df.mode(axis=0))\n",
    "\n",
    "#create dictionary of columns to their modes for\n",
    "#categorical data\n",
    "modemaps = cat_df.mode(axis=0).to_dict()\n",
    "for k in modemaps:\n",
    "    modemaps[k] = modemaps[k][0]\n",
    "#print(modemaps)\n",
    "\n",
    "#create dictionary of columns to their median for\n",
    "#continuous data\n",
    "cont_df = cont_df.fillna(cont_df.median())\n",
    "medianmaps = cont_df.median(axis=0).to_dict()\n",
    "#print(medianmaps)\n",
    "\n",
    "# performing one hot encoding for categorical columns\n",
    "cat_df = pd.get_dummies(cat_df)\n",
    "\n",
    "# combining the categorical and continuous columns\n",
    "combined_df = pd.concat((cont_df,cat_df),axis=1)\n",
    "combined_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 235)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('dataset/test.csv')\n",
    "test_df = test_df.drop(removColList,axis=1)\n",
    "\n",
    "#split dataframe into cont and cat\n",
    "cat_test_df = test_df[cats]\n",
    "cont_test_df = test_df.drop(cats,axis=1)\n",
    "cat_test_df = cat_test_df.fillna(value=modemaps)\n",
    "cat_test_df = pd.get_dummies(cat_test_df)\n",
    "\n",
    "cont_test_df = cont_test_df.fillna(value=medianmaps)\n",
    "\n",
    "combined_test_df = pd.concat((cont_test_df,cat_test_df),axis=1)\n",
    "combined_test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 235)"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove the columns from training data which are not present in test data\n",
    "not_found_cols = list(set(combined_df.columns)-set(combined_test_df.columns))\n",
    "y = combined_df['SalePrice']\n",
    "combined_df = combined_df.drop(not_found_cols,axis=1)\n",
    "combined_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using Learning models  from pre-processed data 2. (ii)\n",
    "The regression models used were random forest regressor and gradient boosting regressor and ensembling both of the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using model RFG\n",
    "\n",
    "#using stratified k fold for evaluating the model 4\n",
    "error_list.append(stratifiedKFoldEvaluate(RFG,combined_df,y))\n",
    "\n",
    "rfg = RFG(100)\n",
    "rfg.fit(combined_df,y)\n",
    "y_pred = rfg.predict(combined_test_df)\n",
    "\n",
    "#getting the submission 4\n",
    "submission = pd.concat([test_df['Id'],pd.Series(y_pred,name='SalePrice')],axis=1)\n",
    "submission.to_csv('dataset/submission_4.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using model GBR\n",
    "\n",
    "#using stratified k fold for evaluating the model 5\n",
    "error_list.append(stratifiedKFoldEvaluate(GBR,combined_df,y))\n",
    "\n",
    "gbr = GBR()\n",
    "gbr.fit(combined_df,y)\n",
    "y_pred = gbr.predict(combined_test_df)\n",
    "\n",
    "#getting the submission 5\n",
    "submission = pd.concat([test_df['Id'],pd.Series(y_pred,name='SalePrice')],axis=1)\n",
    "submission.to_csv('dataset/submission_5.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using ensemble of two models\n",
    "\n",
    "#evaluating the error of ensemble model 6 \n",
    "error_list.append((error_list[3]+error_list[4])/2)\n",
    "\n",
    "y_pred = (rfg.predict(combined_test_df) + gbr.predict(combined_test_df))/2\n",
    "#getting the submission 6\n",
    "submission = pd.concat([test_df['Id'],pd.Series(y_pred,name='SalePrice')],axis=1)\n",
    "submission.to_csv('dataset/submission_6.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## <a name=\"eval\" />5. Evaluation\n",
    "The errors of the six different models used are plotted and compared. The plot shows that the model 5, which used Gradient Boosting Regressor over One-hot encoded data set has the minimum cross-validation error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1068970065.7256635,\n",
       " 833354858.407922,\n",
       " 951162462.0667927,\n",
       " 1116971909.3320913,\n",
       " 762617290.5501858,\n",
       " 939794599.9411385]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "error_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 1: Random Forest Regressor with Categorical attributes replaced with integers\n",
      "Model 2: Gradient Boosting Regressor with Categorical attributes replaced with integers\n",
      "Model 3: Ensembled Regressor with Categorical attributes replaced with integers\n",
      "Model 4: Random Forest Regressor with Categorical attributes one hot encoded\n",
      "Model 5: Gradient Boosting Regressor with Categorical attributes one hot encoded\n",
      "Model 6: Ensembled Regressor with Categorical attributes one hot encoded\n"
     ]
    }
   ],
   "source": [
    "plt.ylabel(\"Mean Squared Error\")\n",
    "plt.xlabel(\"Models\")\n",
    "plt.bar(range(1,len(error_list)+1),error_list)\n",
    "plt.show()\n",
    "print (\"Model 1: Random Forest Regressor with Categorical attributes replaced with integers\")\n",
    "print (\"Model 2: Gradient Boosting Regressor with Categorical attributes replaced with integers\")\n",
    "print (\"Model 3: Ensembled Regressor with Categorical attributes replaced with integers\")\n",
    "print (\"Model 4: Random Forest Regressor with Categorical attributes one hot encoded\")\n",
    "print (\"Model 5: Gradient Boosting Regressor with Categorical attributes one hot encoded\")\n",
    "print (\"Model 6: Ensembled Regressor with Categorical attributes one hot encoded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "In this project, we implemented different preprocessing and regression models for predicting the house price from the given dataset of Kaggle competition. We evaluated the models on the basis of mean squared errors. We made six different submissions for six different models. At the moemnt of submission, we received the best rank 2093 out of 4390 submissions. \n",
    "The accuracy of predicting the sale price could be improved using grid search through the different hyper-parameters. A sample of grid search for gradient boosting regression is shown below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the code takes a long duration to finish\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "parameters = {\n",
    "    \"loss\":[\"ls\",\"lad\"],\n",
    "    \"learning_rate\": [0.01, 0.025, 0.05, 0.075, 0.1, 0.15, 0.2],\n",
    "    \"min_samples_split\": np.linspace(0.1, 0.5, 12),\n",
    "    \"min_samples_leaf\": np.linspace(0.1, 0.5, 12),\n",
    "    \"max_depth\":[3,5,8],\n",
    "    \"max_features\":[\"log2\",\"sqrt\"],\n",
    "    \"criterion\": [\"friedman_mse\",  \"mae\"],\n",
    "    \"subsample\":[0.5, 0.618, 0.8, 0.85, 0.9, 0.95, 1.0],\n",
    "    \"n_estimators\":[100,150,200]\n",
    "    }\n",
    "reg = GridSearchCV(GBR(), parameters, cv=10, n_jobs=-1)\n",
    "reg.fit(X,y)\n",
    "y_pred = reg.predict(test_df)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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