Housing_price_demo_V0.1_by_Praveen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<b>Hands on Demo by Praveen Vasari. </b>\n",
    "<br></br>\n",
    "<b>Part1: ML Hands on Tutorial for beginners</b>\n",
    "\n",
    "<h3>Aim of this demo is to present you an over-view of building a \"Machine Learning Model\", the further continued demos will answer many of your questions raised out of this demo/discussion.</h3>\n",
    "\n",
    "\n",
    "<h4>Note:Basic understanding of ML / Statistical Modeling is expected.</h4>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>In this demo, and in further continued demos I am going to use a \"Housing price\" dataset.</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "<h3>Problem Definition:</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-danger\">\n",
    "\n",
    "<h3>About Dataset:  <br> </br> <br></br>\n",
    "This CSV dataset contains many features (features also can be called as columns/attributes/variables), one of them is target variable (Dependent Variable) i.e \"SalePrice\" here. And, this dataset contains many observations (observations can be called as rows/records/examples). For each and every observation(row), there is an associated \"SalePrice\" value. </h3>\n",
    "\n",
    "<h4>An \"observation / row\" in a dataset = values for set of Independent Variables +  values for (Usually) One Dependent Variable</h4>\n",
    "\n",
    "\n",
    "<h3>Our goal is to - use Machine Learning techniques, and predict a reasonable (close to reality) \"SalePrice\" value for any  \"new given observation\" ( Note that we are expected to predict a SalePrice value for this new row/record/observation. Obviously, we will not know the \"SalePrice\" value for a new observation upfront right?, a domain expert can guess/estimate it, else we may have to take help of ML or Statistical Modeling to guess/predict a SalePrice Value).</h3>\n",
    "\n",
    "\n",
    "<h4>A new given observation = values for set of Independent Variables + No Value for corresponding Dependent Variable ( this is what we are about to predict using ML techniques ) </h4>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<h4>As you see its a \"Regression\" problem that we are about to solve using Regression Algorithms\n",
    "<br></br> <br></br>\n",
    "Just to recollect, Regression problem is a \"supervised learning problem\", where the expected output (target value / dependent variable value) for every new record is a \"continuous value\". <br> </br> <br></br>The other kind of supervised learning problem is \"classifiction\" where the expected output value for every new record is either 1(usually means - Yes, Present, Positive), 0(usually means - No, not present, Negative) \n",
    "<br></br>\n",
    "If we don't have to predict any such target variable, then it will be treated as an \"Unsupervised Learning\" problem. Clustering, Dimensionalrity reduction (eg.PCA) are best examples of Unsupervised Learning problems.\n",
    "<br></br><br></br>\n",
    "Apart from these, \"Reinforcement Learning\",is an other populer area of Machine Learning.\n",
    " </h4>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Note that pandas, numpy, matplotlib, seaborn, and sklearn \n",
    "# are very popular python packages to deal with data operations & Machine Learning work,\n",
    "# we import these packages as and when needed\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the dataset to panda's DataFrame\n",
    "h_data = pd.read_csv(\"~/Desktop/MyModels/housing_price.csv\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<h2>Get to know some primary info about data</h2> \n",
    "</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 81)\n",
      "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')\n"
     ]
    }
   ],
   "source": [
    "print (h_data.shape)  #(1460, 81) i.e 1460 (including header ?) Rows and 81 Columns.\n",
    "print (h_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_data.describe() #Are you able to receive any thing useful from this output ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_data.head() #.head(n) -- shows \"n\" number of (top?)observations from data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      "Id               1460 non-null int64\n",
      "MSSubClass       1460 non-null int64\n",
      "MSZoning         1460 non-null object\n",
      "LotFrontage      1201 non-null float64\n",
      "LotArea          1460 non-null int64\n",
      "Street           1460 non-null object\n",
      "Alley            91 non-null object\n",
      "LotShape         1460 non-null object\n",
      "LandContour      1460 non-null object\n",
      "Utilities        1460 non-null object\n",
      "LotConfig        1460 non-null object\n",
      "LandSlope        1460 non-null object\n",
      "Neighborhood     1460 non-null object\n",
      "Condition1       1460 non-null object\n",
      "Condition2       1460 non-null object\n",
      "BldgType         1460 non-null object\n",
      "HouseStyle       1460 non-null object\n",
      "OverallQual      1460 non-null int64\n",
      "OverallCond      1460 non-null int64\n",
      "YearBuilt        1460 non-null int64\n",
      "YearRemodAdd     1460 non-null int64\n",
      "RoofStyle        1460 non-null object\n",
      "RoofMatl         1460 non-null object\n",
      "Exterior1st      1460 non-null object\n",
      "Exterior2nd      1460 non-null object\n",
      "MasVnrType       1452 non-null object\n",
      "MasVnrArea       1452 non-null float64\n",
      "ExterQual        1460 non-null object\n",
      "ExterCond        1460 non-null object\n",
      "Foundation       1460 non-null object\n",
      "BsmtQual         1423 non-null object\n",
      "BsmtCond         1423 non-null object\n",
      "BsmtExposure     1422 non-null object\n",
      "BsmtFinType1     1423 non-null object\n",
      "BsmtFinSF1       1460 non-null int64\n",
      "BsmtFinType2     1422 non-null object\n",
      "BsmtFinSF2       1460 non-null int64\n",
      "BsmtUnfSF        1460 non-null int64\n",
      "TotalBsmtSF      1460 non-null int64\n",
      "Heating          1460 non-null object\n",
      "HeatingQC        1460 non-null object\n",
      "CentralAir       1460 non-null object\n",
      "Electrical       1459 non-null object\n",
      "1stFlrSF         1460 non-null int64\n",
      "2ndFlrSF         1460 non-null int64\n",
      "LowQualFinSF     1460 non-null int64\n",
      "GrLivArea        1460 non-null int64\n",
      "BsmtFullBath     1460 non-null int64\n",
      "BsmtHalfBath     1460 non-null int64\n",
      "FullBath         1460 non-null int64\n",
      "HalfBath         1460 non-null int64\n",
      "BedroomAbvGr     1460 non-null int64\n",
      "KitchenAbvGr     1460 non-null int64\n",
      "KitchenQual      1460 non-null object\n",
      "TotRmsAbvGrd     1460 non-null int64\n",
      "Functional       1460 non-null object\n",
      "Fireplaces       1460 non-null int64\n",
      "FireplaceQu      770 non-null object\n",
      "GarageType       1379 non-null object\n",
      "GarageYrBlt      1379 non-null float64\n",
      "GarageFinish     1379 non-null object\n",
      "GarageCars       1460 non-null int64\n",
      "GarageArea       1460 non-null int64\n",
      "GarageQual       1379 non-null object\n",
      "GarageCond       1379 non-null object\n",
      "PavedDrive       1460 non-null object\n",
      "WoodDeckSF       1460 non-null int64\n",
      "OpenPorchSF      1460 non-null int64\n",
      "EnclosedPorch    1460 non-null int64\n",
      "3SsnPorch        1460 non-null int64\n",
      "ScreenPorch      1460 non-null int64\n",
      "PoolArea         1460 non-null int64\n",
      "PoolQC           7 non-null object\n",
      "Fence            281 non-null object\n",
      "MiscFeature      54 non-null object\n",
      "MiscVal          1460 non-null int64\n",
      "MoSold           1460 non-null int64\n",
      "YrSold           1460 non-null int64\n",
      "SaleType         1460 non-null object\n",
      "SaleCondition    1460 non-null object\n",
      "SalePrice        1460 non-null int64\n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "h_data.info() #Are you able to figure out different type of data types on this output?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's check how every attribute is - 1). reated with other attribute  2).contributing towards predicting a target value. (Hint: refer Correlation concept)\n",
    "\n",
    "#### (Optional, you may check this correlation concept later) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011156</td>\n",
       "      <td>-0.010601</td>\n",
       "      <td>-0.033226</td>\n",
       "      <td>-0.028365</td>\n",
       "      <td>0.012609</td>\n",
       "      <td>-0.012713</td>\n",
       "      <td>-0.021998</td>\n",
       "      <td>-0.050298</td>\n",
       "      <td>-0.005024</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029643</td>\n",
       "      <td>-0.000477</td>\n",
       "      <td>0.002889</td>\n",
       "      <td>-0.046635</td>\n",
       "      <td>0.001330</td>\n",
       "      <td>0.057044</td>\n",
       "      <td>-0.006242</td>\n",
       "      <td>0.021172</td>\n",
       "      <td>0.000712</td>\n",
       "      <td>-0.021917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>0.011156</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.386347</td>\n",
       "      <td>-0.139781</td>\n",
       "      <td>0.032628</td>\n",
       "      <td>-0.059316</td>\n",
       "      <td>0.027850</td>\n",
       "      <td>0.040581</td>\n",
       "      <td>0.022936</td>\n",
       "      <td>-0.069836</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.012579</td>\n",
       "      <td>-0.006100</td>\n",
       "      <td>-0.012037</td>\n",
       "      <td>-0.043825</td>\n",
       "      <td>-0.026030</td>\n",
       "      <td>0.008283</td>\n",
       "      <td>-0.007683</td>\n",
       "      <td>-0.013585</td>\n",
       "      <td>-0.021407</td>\n",
       "      <td>-0.084284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>-0.010601</td>\n",
       "      <td>-0.386347</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.426095</td>\n",
       "      <td>0.251646</td>\n",
       "      <td>-0.059213</td>\n",
       "      <td>0.123349</td>\n",
       "      <td>0.088866</td>\n",
       "      <td>0.193458</td>\n",
       "      <td>0.233633</td>\n",
       "      <td>...</td>\n",
       "      <td>0.088521</td>\n",
       "      <td>0.151972</td>\n",
       "      <td>0.010700</td>\n",
       "      <td>0.070029</td>\n",
       "      <td>0.041383</td>\n",
       "      <td>0.206167</td>\n",
       "      <td>0.003368</td>\n",
       "      <td>0.011200</td>\n",
       "      <td>0.007450</td>\n",
       "      <td>0.351799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>-0.033226</td>\n",
       "      <td>-0.139781</td>\n",
       "      <td>0.426095</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.105806</td>\n",
       "      <td>-0.005636</td>\n",
       "      <td>0.014228</td>\n",
       "      <td>0.013788</td>\n",
       "      <td>0.104160</td>\n",
       "      <td>0.214103</td>\n",
       "      <td>...</td>\n",
       "      <td>0.171698</td>\n",
       "      <td>0.084774</td>\n",
       "      <td>-0.018340</td>\n",
       "      <td>0.020423</td>\n",
       "      <td>0.043160</td>\n",
       "      <td>0.077672</td>\n",
       "      <td>0.038068</td>\n",
       "      <td>0.001205</td>\n",
       "      <td>-0.014261</td>\n",
       "      <td>0.263843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>-0.028365</td>\n",
       "      <td>0.032628</td>\n",
       "      <td>0.251646</td>\n",
       "      <td>0.105806</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.091932</td>\n",
       "      <td>0.572323</td>\n",
       "      <td>0.550684</td>\n",
       "      <td>0.411876</td>\n",
       "      <td>0.239666</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238923</td>\n",
       "      <td>0.308819</td>\n",
       "      <td>-0.113937</td>\n",
       "      <td>0.030371</td>\n",
       "      <td>0.064886</td>\n",
       "      <td>0.065166</td>\n",
       "      <td>-0.031406</td>\n",
       "      <td>0.070815</td>\n",
       "      <td>-0.027347</td>\n",
       "      <td>0.790982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>0.012609</td>\n",
       "      <td>-0.059316</td>\n",
       "      <td>-0.059213</td>\n",
       "      <td>-0.005636</td>\n",
       "      <td>-0.091932</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.375983</td>\n",
       "      <td>0.073741</td>\n",
       "      <td>-0.128101</td>\n",
       "      <td>-0.046231</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.003334</td>\n",
       "      <td>-0.032589</td>\n",
       "      <td>0.070356</td>\n",
       "      <td>0.025504</td>\n",
       "      <td>0.054811</td>\n",
       "      <td>-0.001985</td>\n",
       "      <td>0.068777</td>\n",
       "      <td>-0.003511</td>\n",
       "      <td>0.043950</td>\n",
       "      <td>-0.077856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>-0.012713</td>\n",
       "      <td>0.027850</td>\n",
       "      <td>0.123349</td>\n",
       "      <td>0.014228</td>\n",
       "      <td>0.572323</td>\n",
       "      <td>-0.375983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.592855</td>\n",
       "      <td>0.315707</td>\n",
       "      <td>0.249503</td>\n",
       "      <td>...</td>\n",
       "      <td>0.224880</td>\n",
       "      <td>0.188686</td>\n",
       "      <td>-0.387268</td>\n",
       "      <td>0.031355</td>\n",
       "      <td>-0.050364</td>\n",
       "      <td>0.004950</td>\n",
       "      <td>-0.034383</td>\n",
       "      <td>0.012398</td>\n",
       "      <td>-0.013618</td>\n",
       "      <td>0.522897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>-0.021998</td>\n",
       "      <td>0.040581</td>\n",
       "      <td>0.088866</td>\n",
       "      <td>0.013788</td>\n",
       "      <td>0.550684</td>\n",
       "      <td>0.073741</td>\n",
       "      <td>0.592855</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.179618</td>\n",
       "      <td>0.128451</td>\n",
       "      <td>...</td>\n",
       "      <td>0.205726</td>\n",
       "      <td>0.226298</td>\n",
       "      <td>-0.193919</td>\n",
       "      <td>0.045286</td>\n",
       "      <td>-0.038740</td>\n",
       "      <td>0.005829</td>\n",
       "      <td>-0.010286</td>\n",
       "      <td>0.021490</td>\n",
       "      <td>0.035743</td>\n",
       "      <td>0.507101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>-0.050298</td>\n",
       "      <td>0.022936</td>\n",
       "      <td>0.193458</td>\n",
       "      <td>0.104160</td>\n",
       "      <td>0.411876</td>\n",
       "      <td>-0.128101</td>\n",
       "      <td>0.315707</td>\n",
       "      <td>0.179618</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.264736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.159718</td>\n",
       "      <td>0.125703</td>\n",
       "      <td>-0.110204</td>\n",
       "      <td>0.018796</td>\n",
       "      <td>0.061466</td>\n",
       "      <td>0.011723</td>\n",
       "      <td>-0.029815</td>\n",
       "      <td>-0.005965</td>\n",
       "      <td>-0.008201</td>\n",
       "      <td>0.477493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>-0.005024</td>\n",
       "      <td>-0.069836</td>\n",
       "      <td>0.233633</td>\n",
       "      <td>0.214103</td>\n",
       "      <td>0.239666</td>\n",
       "      <td>-0.046231</td>\n",
       "      <td>0.249503</td>\n",
       "      <td>0.128451</td>\n",
       "      <td>0.264736</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.204306</td>\n",
       "      <td>0.111761</td>\n",
       "      <td>-0.102303</td>\n",
       "      <td>0.026451</td>\n",
       "      <td>0.062021</td>\n",
       "      <td>0.140491</td>\n",
       "      <td>0.003571</td>\n",
       "      <td>-0.015727</td>\n",
       "      <td>0.014359</td>\n",
       "      <td>0.386420</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Id  MSSubClass  LotFrontage   LotArea  OverallQual  \\\n",
       "Id            1.000000    0.011156    -0.010601 -0.033226    -0.028365   \n",
       "MSSubClass    0.011156    1.000000    -0.386347 -0.139781     0.032628   \n",
       "LotFrontage  -0.010601   -0.386347     1.000000  0.426095     0.251646   \n",
       "LotArea      -0.033226   -0.139781     0.426095  1.000000     0.105806   \n",
       "OverallQual  -0.028365    0.032628     0.251646  0.105806     1.000000   \n",
       "OverallCond   0.012609   -0.059316    -0.059213 -0.005636    -0.091932   \n",
       "YearBuilt    -0.012713    0.027850     0.123349  0.014228     0.572323   \n",
       "YearRemodAdd -0.021998    0.040581     0.088866  0.013788     0.550684   \n",
       "MasVnrArea   -0.050298    0.022936     0.193458  0.104160     0.411876   \n",
       "BsmtFinSF1   -0.005024   -0.069836     0.233633  0.214103     0.239666   \n",
       "\n",
       "              OverallCond  YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  \\\n",
       "Id               0.012609  -0.012713     -0.021998   -0.050298   -0.005024   \n",
       "MSSubClass      -0.059316   0.027850      0.040581    0.022936   -0.069836   \n",
       "LotFrontage     -0.059213   0.123349      0.088866    0.193458    0.233633   \n",
       "LotArea         -0.005636   0.014228      0.013788    0.104160    0.214103   \n",
       "OverallQual     -0.091932   0.572323      0.550684    0.411876    0.239666   \n",
       "OverallCond      1.000000  -0.375983      0.073741   -0.128101   -0.046231   \n",
       "YearBuilt       -0.375983   1.000000      0.592855    0.315707    0.249503   \n",
       "YearRemodAdd     0.073741   0.592855      1.000000    0.179618    0.128451   \n",
       "MasVnrArea      -0.128101   0.315707      0.179618    1.000000    0.264736   \n",
       "BsmtFinSF1      -0.046231   0.249503      0.128451    0.264736    1.000000   \n",
       "\n",
       "                ...      WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "Id              ...       -0.029643    -0.000477       0.002889  -0.046635   \n",
       "MSSubClass      ...       -0.012579    -0.006100      -0.012037  -0.043825   \n",
       "LotFrontage     ...        0.088521     0.151972       0.010700   0.070029   \n",
       "LotArea         ...        0.171698     0.084774      -0.018340   0.020423   \n",
       "OverallQual     ...        0.238923     0.308819      -0.113937   0.030371   \n",
       "OverallCond     ...       -0.003334    -0.032589       0.070356   0.025504   \n",
       "YearBuilt       ...        0.224880     0.188686      -0.387268   0.031355   \n",
       "YearRemodAdd    ...        0.205726     0.226298      -0.193919   0.045286   \n",
       "MasVnrArea      ...        0.159718     0.125703      -0.110204   0.018796   \n",
       "BsmtFinSF1      ...        0.204306     0.111761      -0.102303   0.026451   \n",
       "\n",
       "              ScreenPorch  PoolArea   MiscVal    MoSold    YrSold  SalePrice  \n",
       "Id               0.001330  0.057044 -0.006242  0.021172  0.000712  -0.021917  \n",
       "MSSubClass      -0.026030  0.008283 -0.007683 -0.013585 -0.021407  -0.084284  \n",
       "LotFrontage      0.041383  0.206167  0.003368  0.011200  0.007450   0.351799  \n",
       "LotArea          0.043160  0.077672  0.038068  0.001205 -0.014261   0.263843  \n",
       "OverallQual      0.064886  0.065166 -0.031406  0.070815 -0.027347   0.790982  \n",
       "OverallCond      0.054811 -0.001985  0.068777 -0.003511  0.043950  -0.077856  \n",
       "YearBuilt       -0.050364  0.004950 -0.034383  0.012398 -0.013618   0.522897  \n",
       "YearRemodAdd    -0.038740  0.005829 -0.010286  0.021490  0.035743   0.507101  \n",
       "MasVnrArea       0.061466  0.011723 -0.029815 -0.005965 -0.008201   0.477493  \n",
       "BsmtFinSF1       0.062021  0.140491  0.003571 -0.015727  0.014359   0.386420  \n",
       "\n",
       "[10 rows x 38 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_data.corr().head(10) #What is mean by \"Negative sign\" in output below ?\n",
    "#What can be max or min value when we measure correlation?\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Going through above correlation matrix is tideous, right? Let's visualize it through a heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bf819b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,16))\n",
    "sns.set_palette(\"PuBuGn_d\")\n",
    "hmp = sns.heatmap(h_data.corr(), linewidths=.5, cmap=\"YlGnBu\")\n",
    "lst1 = hmp.set_yticklabels(hmp.get_yticklabels(), rotation = 45)\n",
    "lst2 = hmp.set_xticklabels(hmp.get_xticklabels(), rotation = 45)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<h2>Split the dataset into TrainSet (80%) & TestSet (20%)</h2> \n",
    "<b> The primary purpose of splitting the dataset into training and test sets is to \n",
    "verify how well would our model perform on unseen data, \n",
    "train the model on training set and verify its performance on the test set.\n",
    "\n",
    "Usually over a given dataset, 60-80% representative split of data is good for training.\n",
    "The rest of 40-20% should be used as test dataset. </b>\n",
    "</div> \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the ideal way to split the data -- random? or in a certain order ? or some other way(s)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nThe primary purpose of splitting the dataset into training and test sets is to \\nverify how well would our model perform on unseen data, \\ntrain the model on training set and verify its performance on the test set.\\n\\nUsually over a given dataset, 60-80% representative split of data is good for training.\\nThe rest of 40-20% should be used as test dataset.\\n'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "\"\"\"\n",
    "The primary purpose of splitting the dataset into training and test sets is to \n",
    "verify how well would our model perform on unseen data, \n",
    "train the model on training set and verify its performance on the test set.\n",
    "\n",
    "Usually over a given dataset, 60-80% representative split of data is good for training.\n",
    "The rest of 40-20% should be used as test dataset.\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = h_data.drop(['SalePrice','Id'],axis=1) #Take all features (independent variables) to \"X\" variable i.e panda's dataframe\n",
    "#X.columns,X.shape\n",
    "y = h_data.SalePrice #Take target variable (dependent varaiable) to \"y\" variable i.e panda's series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=22) #splits train set - 80%, #test set - 20%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1168, 79)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape #;y_train.shape;X_test.shape;y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check whether we are able to build any primary/base models at this stage ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Abnorml'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-f86280439cc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mhlm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mhlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# Fails since the data set has categorical & null values, this is not expected by\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;31m#LinearRegression algorithm/model, ofcourse few other algorithms/models also would fail\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    480\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0;32m--> 482\u001b[0;31m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\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[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    571\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    572\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    446\u001b[0m         \u001b[0;31m# make sure we actually converted to numeric:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    447\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mdtype_numeric\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkind\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"O\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 448\u001b[0;31m             \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    449\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Abnorml'"
     ]
    }
   ],
   "source": [
    "hlm = LinearRegression()\n",
    "hlm.fit(X_train,y_train) # Fails since the data set has categorical & null values, this is not expected by\n",
    "#LinearRegression algorithm/model, ofcourse few other algorithms/models also would fail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Abnorml'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-3ff8430a842f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mDecisionTreeRegressor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mhtr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mhtr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1122\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1125\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_random_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    431\u001b[0m                                       force_all_finite)\n\u001b[1;32m    432\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Abnorml'"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor \n",
    "htr = DecisionTreeRegressor()\n",
    "htr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Abnorml'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-f6740236eaad>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msvm\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSVR\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mh_svr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mh_svr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    571\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    572\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    431\u001b[0m                                       force_all_finite)\n\u001b[1;32m    432\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 433\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    435\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Abnorml'"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVR \n",
    "h_svr = SVR()\n",
    "h_svr.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>As you see above, look at why each of those models is failing to fit, i.e throwing errors (not able to accept train data for learning). \n",
    "(Remember, most of the machine learning algorithms fail (throw errors) if there are any categorial values or nulls present in training/input data). Hence let's at least prepre the data such a way that the data wont contain any categorical values. (either you can drop the categorical features, else you can transform the categorical data to numerical using appropriate techniques)</b> \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<h2>Data Preparation</h2> \n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many columns / features have \"null\" values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19\n",
      "['LotFrontage', 'Alley', 'MasVnrType', 'MasVnrArea', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Electrical', 'FireplaceQu', 'GarageType', 'GarageYrBlt', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature']\n"
     ]
    }
   ],
   "source": [
    "nu_featurs = X_train.isnull().any(axis=0)\n",
    "nu_lst_ftrs = [x for x in nu_featurs.index if nu_featurs[x]]\n",
    "print (len(nu_lst_ftrs))\n",
    "print (nu_lst_ftrs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many features belong to categorial datatype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "43\n",
      "Index(['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities',\n",
      "       'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2',\n",
      "       'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st',\n",
      "       'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation',\n",
      "       'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2',\n",
      "       'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual',\n",
      "       'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual',\n",
      "       'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature',\n",
      "       'SaleType', 'SaleCondition'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "cat_features = X_train.select_dtypes(include=['object']).columns\n",
    "print (len(cat_features))\n",
    "print (cat_features)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many categorical features have \"null\" values - any way we are going to drop all categorical features, need not to check this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Alley\n",
      "MasVnrType\n",
      "BsmtQual\n",
      "BsmtCond\n",
      "BsmtExposure\n",
      "BsmtFinType1\n",
      "BsmtFinType2\n",
      "Electrical\n",
      "FireplaceQu\n",
      "GarageType\n",
      "GarageFinish\n",
      "GarageQual\n",
      "GarageCond\n",
      "PoolQC\n",
      "Fence\n",
      "MiscFeature\n",
      "16\n"
     ]
    }
   ],
   "source": [
    "ncat_features = X_train.select_dtypes(include=['object']).isnull().any(axis=0)\n",
    "\n",
    "temp_list = []\n",
    "for feature in ncat_features.index:\n",
    "    if nu_featurs[feature]:\n",
    "        temp_list.append(feature)\n",
    "        print (feature)\n",
    "\n",
    "print (len(temp_list))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Drop all categorial features in the train data</b> \n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Later,We may revist to this step just to make sure we are not dropping out any valuabe categorial feature from model.\n",
    "In case we are considering any categorical feature to keep, we have to convert it to numerical labels.\n",
    "\n",
    "Find out what are the techniques will help us to convert a categorical feature to numerical feature.</b> \n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getCatFeatures(df):\n",
    "    cat_features = df.select_dtypes(include=['object']).columns \n",
    "    return cat_features\n",
    "\n",
    "def dropFeatures(df, lst_featrues):\n",
    "    for feature in lst_featrues:\n",
    "            print('Droping --- {}'.format(feature))\n",
    "            df.drop(feature, axis=1, inplace=True) \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Droping --- MSZoning\n",
      "Droping --- Street\n",
      "Droping --- Alley\n",
      "Droping --- LotShape\n",
      "Droping --- LandContour\n",
      "Droping --- Utilities\n",
      "Droping --- LotConfig\n",
      "Droping --- LandSlope\n",
      "Droping --- Neighborhood\n",
      "Droping --- Condition1\n",
      "Droping --- Condition2\n",
      "Droping --- BldgType\n",
      "Droping --- HouseStyle\n",
      "Droping --- RoofStyle\n",
      "Droping --- RoofMatl\n",
      "Droping --- Exterior1st\n",
      "Droping --- Exterior2nd\n",
      "Droping --- MasVnrType\n",
      "Droping --- ExterQual\n",
      "Droping --- ExterCond\n",
      "Droping --- Foundation\n",
      "Droping --- BsmtQual\n",
      "Droping --- BsmtCond\n",
      "Droping --- BsmtExposure\n",
      "Droping --- BsmtFinType1\n",
      "Droping --- BsmtFinType2\n",
      "Droping --- Heating\n",
      "Droping --- HeatingQC\n",
      "Droping --- CentralAir\n",
      "Droping --- Electrical\n",
      "Droping --- KitchenQual\n",
      "Droping --- Functional\n",
      "Droping --- FireplaceQu\n",
      "Droping --- GarageType\n",
      "Droping --- GarageFinish\n",
      "Droping --- GarageQual\n",
      "Droping --- GarageCond\n",
      "Droping --- PavedDrive\n",
      "Droping --- PoolQC\n",
      "Droping --- Fence\n",
      "Droping --- MiscFeature\n",
      "Droping --- SaleType\n",
      "Droping --- SaleCondition\n"
     ]
    }
   ],
   "source": [
    "cless_X_train = dropFeatures(X_train,getCatFeatures(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "still_cat_features = cless_X_train.select_dtypes(include=['object']).columns\n",
    "print (len(still_cat_features))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1168, 36)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cless_X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-3c28c023ecfd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcless_hlm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcless_hlm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcless_X_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    480\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[0;32m--> 482\u001b[0;31m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[1;32m    483\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    484\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\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[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    571\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    572\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    451\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    452\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     42\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     43\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 44\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "cless_hlm = LinearRegression()\n",
    "cless_hlm.fit(cless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float32').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-c83499d0f909>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcless_htr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDecisionTreeRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcless_htr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcless_X_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m   1122\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[1;32m   1125\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/tree/tree.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    114\u001b[0m         \u001b[0mrandom_state\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_random_state\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mDTYPE\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    117\u001b[0m             \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_2d\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0missparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    451\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    452\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     42\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     43\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 44\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float32')."
     ]
    }
   ],
   "source": [
    "cless_htr = DecisionTreeRegressor()\n",
    "cless_htr.fit(cless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-25fe1f61026a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcless_h_svr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSVR\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mcless_h_svr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcless_X_train\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/svm/base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    147\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msparse\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 149\u001b[0;31m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'C'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    150\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_targets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    571\u001b[0m     X = check_array(X, accept_sparse, dtype, order, copy, force_all_finite,\n\u001b[1;32m    572\u001b[0m                     \u001b[0mensure_2d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mallow_nd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mensure_min_samples\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 573\u001b[0;31m                     ensure_min_features, warn_on_dtype, estimator)\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    575\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[1;32m    451\u001b[0m                              % (array.ndim, estimator_name))\n\u001b[1;32m    452\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m             \u001b[0m_assert_all_finite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     \u001b[0mshape_repr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_shape_repr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[0;34m(X)\u001b[0m\n\u001b[1;32m     42\u001b[0m             and not np.isfinite(X).all()):\n\u001b[1;32m     43\u001b[0m         raise ValueError(\"Input contains NaN, infinity\"\n\u001b[0;32m---> 44\u001b[0;31m                          \" or a value too large for %r.\" % X.dtype)\n\u001b[0m\u001b[1;32m     45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
     ]
    }
   ],
   "source": [
    "cless_h_svr = SVR()\n",
    "cless_h_svr.fit(cless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<h3>Again, check why each of above 3 models is failing (throwing errors), are those errors same as earlier ?</h3> \n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Notice that, after dropping (removing) all categorical features, we tried to fit the models above, but they failed since there are some null values in data. As we know this is not expected by most of ML models.</b> \n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<h3>Check how many features have \"null\" values and what are they ? -- 1). Do we have to delete all the features/columns which have nulls ?   or 2). Do we have to delete all the observations (records/rows) which have nulls ? or 3).Do we have to fill-up those \"null\"s with some appropriate values ?   -- Take a reasonable call</h3> \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LotFrontage\n",
      "MasVnrArea\n",
      "GarageYrBlt\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "nu_featurs = cless_X_train.isnull().any(axis=0)\n",
    "\n",
    "temp_list = []\n",
    "\n",
    "for feature in nu_featurs.index:\n",
    "    if nu_featurs[feature]:\n",
    "        temp_list.append(feature)\n",
    "        print (feature)\n",
    "\n",
    "print (len(temp_list))\n",
    "# we can use this statement too \n",
    "#[x for x in nu_featurs.index if nu_featurs[x]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For now, I wanted to impute the nulls suach a way that all the nulls are replaced by their respective mean values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Impute the missing values using \"imputer\"\n",
    "\n",
    "check what are the other ways we have to perform imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import Imputer\n",
    "\n",
    "imp = Imputer(missing_values='NaN', strategy='mean', axis=0)\n",
    "imp.fit(cless_X_train) # What is the meaning of \"fit\" here, are we building any model here ?\n",
    "ncless_X_train= imp.transform(cless_X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(ncless_X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#pd.notna(ncless_X_train)\n",
    "np.isnan(ncless_X_train).any() #Are there any Null's in given ndarray ? -- If yes, return TRUE. If not, return FALSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1168, 36)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncless_X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<h2>Linear Regression Model</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "nc_lnr = LinearRegression()\n",
    "nc_lnr.fit(ncless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Good that this time, model did fit with out any error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Co-efficients:  [-1.94944960e+02 -7.64465608e+01  4.18194591e-01  1.81416463e+04\n",
      "  4.40480777e+03  2.96853129e+02  1.48619871e+02  3.37856857e+01\n",
      "  8.91586280e+00 -6.13469104e-01 -2.76223172e-01  8.02617053e+00\n",
      "  1.82504054e+01  2.07079654e+01 -1.14551111e+01  2.75032597e+01\n",
      "  1.06217791e+04  4.19600448e+03  3.53163437e+03 -2.13691064e+03\n",
      " -1.03707259e+04 -1.15356648e+04  5.39499921e+03  3.30227397e+03\n",
      "  8.45672666e+01  1.13157239e+04 -5.47764905e+00  2.59425128e+01\n",
      " -4.26173056e+00  8.89750691e+00  3.72789438e+01  5.82644744e+01\n",
      " -2.14183532e+01 -9.78449337e-01 -2.07231250e+02 -5.30025886e+02]\n",
      "Intercept: -35404.70315483044\n"
     ]
    }
   ],
   "source": [
    "print ('Co-efficients: ' ,nc_lnr.coef_)\n",
    "print ('Intercept:' ,nc_lnr.intercept_)\n",
    "#print (nc_lnr.get_params()) -- model's default hyper parameters "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Looking at these co-efficients, Can you figure out which are the least contributing features?\n",
    "Can we remove those features from model? - We will decide it in our up-coming session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('MSSubClass', -194.9449596681271),\n",
       " ('LotFrontage', -76.4465608048302),\n",
       " ('LotArea', 0.41819459066477904),\n",
       " ('OverallQual', 18141.646314631744),\n",
       " ('OverallCond', 4404.80776766004),\n",
       " ('YearBuilt', 296.85312941177915),\n",
       " ('YearRemodAdd', 148.61987147768065),\n",
       " ('MasVnrArea', 33.78568573861331),\n",
       " ('BsmtFinSF1', 8.915862796354133),\n",
       " ('BsmtFinSF2', -0.6134691039892459),\n",
       " ('BsmtUnfSF', -0.27622317249915795),\n",
       " ('TotalBsmtSF', 8.026170527779868),\n",
       " ('1stFlrSF', 18.250405367864005),\n",
       " ('2ndFlrSF', 20.70796542085054),\n",
       " ('LowQualFinSF', -11.455111053951086),\n",
       " ('GrLivArea', 27.50325973307008),\n",
       " ('BsmtFullBath', 10621.779066230682),\n",
       " ('BsmtHalfBath', 4196.004480685913),\n",
       " ('FullBath', 3531.634368915114),\n",
       " ('HalfBath', -2136.910641082251),\n",
       " ('BedroomAbvGr', -10370.725947812618),\n",
       " ('KitchenAbvGr', -11535.664804473663),\n",
       " ('TotRmsAbvGrd', 5394.9992098013145),\n",
       " ('Fireplaces', 3302.273974904605),\n",
       " ('GarageYrBlt', 84.56726661758395),\n",
       " ('GarageCars', 11315.723882319578),\n",
       " ('GarageArea', -5.477649045333692),\n",
       " ('WoodDeckSF', 25.942512828995632),\n",
       " ('OpenPorchSF', -4.261730564286843),\n",
       " ('EnclosedPorch', 8.897506908969604),\n",
       " ('3SsnPorch', 37.27894383399878),\n",
       " ('ScreenPorch', 58.264474366151454),\n",
       " ('PoolArea', -21.418353162328884),\n",
       " ('MiscVal', -0.9784493374936574),\n",
       " ('MoSold', -207.2312495631174),\n",
       " ('YrSold', -530.0258856356226)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(zip(cless_X_train.columns, nc_lnr.coef_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>0</th>\n",
       "      <th>Coefficient Estimate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MSSubClass</td>\n",
       "      <td>-194.944960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LotFrontage</td>\n",
       "      <td>-76.446561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LotArea</td>\n",
       "      <td>0.418195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>18141.646315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>OverallCond</td>\n",
       "      <td>4404.807768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>YearBuilt</td>\n",
       "      <td>296.853129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>YearRemodAdd</td>\n",
       "      <td>148.619871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>33.785686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>BsmtFinSF1</td>\n",
       "      <td>8.915863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>BsmtFinSF2</td>\n",
       "      <td>-0.613469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BsmtUnfSF</td>\n",
       "      <td>-0.276223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TotalBsmtSF</td>\n",
       "      <td>8.026171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1stFlrSF</td>\n",
       "      <td>18.250405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2ndFlrSF</td>\n",
       "      <td>20.707965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>LowQualFinSF</td>\n",
       "      <td>-11.455111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>GrLivArea</td>\n",
       "      <td>27.503260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BsmtFullBath</td>\n",
       "      <td>10621.779066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>BsmtHalfBath</td>\n",
       "      <td>4196.004481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>FullBath</td>\n",
       "      <td>3531.634369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HalfBath</td>\n",
       "      <td>-2136.910641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>BedroomAbvGr</td>\n",
       "      <td>-10370.725948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>KitchenAbvGr</td>\n",
       "      <td>-11535.664804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>TotRmsAbvGrd</td>\n",
       "      <td>5394.999210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Fireplaces</td>\n",
       "      <td>3302.273975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>GarageYrBlt</td>\n",
       "      <td>84.567267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>11315.723882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>GarageArea</td>\n",
       "      <td>-5.477649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>WoodDeckSF</td>\n",
       "      <td>25.942513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>OpenPorchSF</td>\n",
       "      <td>-4.261731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>EnclosedPorch</td>\n",
       "      <td>8.897507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>3SsnPorch</td>\n",
       "      <td>37.278944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>ScreenPorch</td>\n",
       "      <td>58.264474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>PoolArea</td>\n",
       "      <td>-21.418353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>MiscVal</td>\n",
       "      <td>-0.978449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>MoSold</td>\n",
       "      <td>-207.231250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>YrSold</td>\n",
       "      <td>-530.025886</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0  Coefficient Estimate\n",
       "0      MSSubClass           -194.944960\n",
       "1     LotFrontage            -76.446561\n",
       "2         LotArea              0.418195\n",
       "3     OverallQual          18141.646315\n",
       "4     OverallCond           4404.807768\n",
       "5       YearBuilt            296.853129\n",
       "6    YearRemodAdd            148.619871\n",
       "7      MasVnrArea             33.785686\n",
       "8      BsmtFinSF1              8.915863\n",
       "9      BsmtFinSF2             -0.613469\n",
       "10      BsmtUnfSF             -0.276223\n",
       "11    TotalBsmtSF              8.026171\n",
       "12       1stFlrSF             18.250405\n",
       "13       2ndFlrSF             20.707965\n",
       "14   LowQualFinSF            -11.455111\n",
       "15      GrLivArea             27.503260\n",
       "16   BsmtFullBath          10621.779066\n",
       "17   BsmtHalfBath           4196.004481\n",
       "18       FullBath           3531.634369\n",
       "19       HalfBath          -2136.910641\n",
       "20   BedroomAbvGr         -10370.725948\n",
       "21   KitchenAbvGr         -11535.664804\n",
       "22   TotRmsAbvGrd           5394.999210\n",
       "23     Fireplaces           3302.273975\n",
       "24    GarageYrBlt             84.567267\n",
       "25     GarageCars          11315.723882\n",
       "26     GarageArea             -5.477649\n",
       "27     WoodDeckSF             25.942513\n",
       "28    OpenPorchSF             -4.261731\n",
       "29  EnclosedPorch              8.897507\n",
       "30      3SsnPorch             37.278944\n",
       "31    ScreenPorch             58.264474\n",
       "32       PoolArea            -21.418353\n",
       "33        MiscVal             -0.978449\n",
       "34         MoSold           -207.231250\n",
       "35         YrSold           -530.025886"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas import Series,DataFrame\n",
    "coeff = DataFrame(cless_X_train.columns)\n",
    "coeff['Coefficient Estimate'] = Series(nc_lnr.coef_)\n",
    "coeff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "***Shall we check how our model is performing on \"Training Data\" ? Let's make predictions on Training data itself.*** \n",
    "<br><br>\n",
    "***Do you think a machine learning model can learn 100% and predict 100% ?  Are there any such models ? If yes, Do we trust such model (s)*** ?\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_X_train = nc_lnr.predict(ncless_X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "***Let's see, how better the model has learnt over training dataset. Let's measure it's performance over training data it self for now.***   \n",
    "<br>\n",
    "<b>1). What are the performance measure metrics we have to relay on ? </b>\n",
    "<br>\n",
    "<b>2). What is the ideal value of R2 (R Square) metric ?</b>\n",
    "<br>\n",
    "<b>3).What RMSE value is ideal ?</b>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error X Train :  35228.601398497056\n",
      "R-Squre X Train :  0.8045716237395569\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score \n",
    "mse_X_train = mean_squared_error(y_train, pred_X_train)\n",
    "r2_X_train = r2_score(y_train, pred_X_train)\n",
    "print('Root Mean Squared Error X Train : ', np.sqrt(mse_X_train))\n",
    "print('R-Squre X Train : ', r2_X_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the meaning of RMSE value 35228.6013 here ? \n",
    "#### R2 value (ranges between \"0 to 1\", can it be negative ?) tells - How much of variability in target variable (y) is explained by model's input (independent) variables (x's)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Code for Adj_R2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The only drawback of R2 is that if new predictors (X) are added to our model, R2 only increases or remains constant but it never decreases. We can not judge that by increasing complexity of our model, are we making it more accurate?\n",
    "\n",
    "That is why, we use “Adjusted R-Square”.\n",
    "\n",
    "The Adjusted R-Square is the modified form of R-Square that has been adjusted for the number of predictors in the model. It incorporates model’s degree of freedom. The adjusted R-Square only increases if the new term improves the model accuracy.\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2017/06/a-comprehensive-guide-for-linear-ridge-and-lasso-regression/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adj_r2_score(model,y,yhat):\n",
    "        \"\"\"Adjusted R square — put fitted linear model, y value, estimated y value in order\n",
    "        \n",
    "            Example:\n",
    "            In [142]: metrics.r2_score(diabetes_y_train,yhat)\n",
    "            Out[142]: 0.51222621477934993\n",
    "        \n",
    "            In [144]: adj_r2_score(lm,diabetes_y_train,yhat)\n",
    "            Out[144]: 0.50035823946984515\"\"\"\n",
    "        from sklearn import metrics\n",
    "        adj = 1 - float(len(y)-1)/(len(y)-len(model.coef_)-1)*(1 - metrics.r2_score(y,yhat))\n",
    "        return adj\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adjusted R-Square X Train:  0.7983510918691981\n"
     ]
    }
   ],
   "source": [
    "print ('Adjusted R-Square X Train: ',adj_r2_score(nc_lnr,y_train,pred_X_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LET's TEST OUR LINEAR REGRESSION MODEL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(292, 79)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>What ever the transformations we performed on \"Training Set\" should be performed on \"Test Set\" too.</b> \n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "For our usecase here, we did these transformations over trainig data -- 1). Dropeed the categorical features,  2).Filled-up the null values with respective Mean values using Imputer.transform(TrainData)   (Note that, we already estimated Mean using Imputer.fit(), we MUST use the same estimated mean for test data too .. think Why? )\n",
    "</dev>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "Usually we have to make use of scikit-learn's Pipleline technique to automate the sequence of several such transformations, so that we can safely relay on that Pipleline to apply it over on Train & Test data. It saves time and volume of code.\n",
    "</dev>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Droping --- MSZoning\n",
      "Droping --- Street\n",
      "Droping --- Alley\n",
      "Droping --- LotShape\n",
      "Droping --- LandContour\n",
      "Droping --- Utilities\n",
      "Droping --- LotConfig\n",
      "Droping --- LandSlope\n",
      "Droping --- Neighborhood\n",
      "Droping --- Condition1\n",
      "Droping --- Condition2\n",
      "Droping --- BldgType\n",
      "Droping --- HouseStyle\n",
      "Droping --- RoofStyle\n",
      "Droping --- RoofMatl\n",
      "Droping --- Exterior1st\n",
      "Droping --- Exterior2nd\n",
      "Droping --- MasVnrType\n",
      "Droping --- ExterQual\n",
      "Droping --- ExterCond\n",
      "Droping --- Foundation\n",
      "Droping --- BsmtQual\n",
      "Droping --- BsmtCond\n",
      "Droping --- BsmtExposure\n",
      "Droping --- BsmtFinType1\n",
      "Droping --- BsmtFinType2\n",
      "Droping --- Heating\n",
      "Droping --- HeatingQC\n",
      "Droping --- CentralAir\n",
      "Droping --- Electrical\n",
      "Droping --- KitchenQual\n",
      "Droping --- Functional\n",
      "Droping --- FireplaceQu\n",
      "Droping --- GarageType\n",
      "Droping --- GarageFinish\n",
      "Droping --- GarageQual\n",
      "Droping --- GarageCond\n",
      "Droping --- PavedDrive\n",
      "Droping --- PoolQC\n",
      "Droping --- Fence\n",
      "Droping --- MiscFeature\n",
      "Droping --- SaleType\n",
      "Droping --- SaleCondition\n"
     ]
    }
   ],
   "source": [
    "cless_X_test = dropFeatures(X_test,getCatFeatures(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(292, 36)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cless_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['LotFrontage', 'MasVnrArea', 'GarageYrBlt']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nu_featurs = cless_X_test.isnull().any(axis=0)\n",
    "[x for x in nu_featurs.index if nu_featurs[x]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ncless_X_test= imp.transform(cless_X_test) \n",
    "# Why are we not doing .fit here, look above where we already applied imputation\n",
    "#If you observed, we performed \"imp.fit(cless_X_train)\" for train set, but we are not performing it over \"test set\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.isnan(ncless_X_test).any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(292, 36)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncless_X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(292,)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root Mean Squared Error X TEST :  30797.68915361465\n",
      "R-Squre X TEST :  0.8453155098730738\n"
     ]
    }
   ],
   "source": [
    "pred_X_test = nc_lnr.predict(ncless_X_test)\n",
    "mse_X_test =  mean_squared_error(y_test, pred_X_test)\n",
    "r2_X_test = r2_score(y_test, pred_X_test)\n",
    "print('Root Mean Squared Error X TEST : ', np.sqrt(mse_X_test))\n",
    "print('R-Squre X TEST : ', r2_X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Check the after-training (train set) RMSE value, and compare it with prediction's (test set ) RMSE value. Check the R-Square value as well.</b>\n",
    "<b>Why there is a difference ?</b> \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<h4>What if you \"train & test\" this model on another (random) sample of housing price dataset?\n",
    "Do we see same error or it will change - Why?</h4>\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>Are we satisfied with this Model Evaluation technique (Train/Test split - measure Training error & Testing error)</b> - Check about K-Fold Cross Validation technique\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>It's time to understand the meaning of \"Bias & Variance\" -- Explore little bit about it.</b> \n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<h2>Support Vector Regression Model</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_svr = SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma='auto',\n",
       "  kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_svr.fit(ncless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING METRICS:    \n",
      "\n",
      "Root Mean Squared Error X Train :  81648.4393031009\n",
      "R-Squre X Train :  -0.04976622464934599 \n",
      "\n",
      "TESTING METRICS:  \n",
      "\n",
      "Root Mean Squared Error X TEST :  80458.54887199096\n",
      "R-Squre X TEST :  -0.055734948859778255\n"
     ]
    }
   ],
   "source": [
    "print (\"TRAINING METRICS:    \\n\")\n",
    "pred_X_train = h_svr.predict(ncless_X_train)\n",
    "mse_X_train = mean_squared_error(y_train, pred_X_train)\n",
    "r2_X_train = r2_score(y_train, pred_X_train)\n",
    "print('Root Mean Squared Error X Train : ', np.sqrt(mse_X_train))\n",
    "print('R-Squre X Train : ', r2_X_train, \"\\n\")\n",
    "#print ('Adjusted R-Square X Train: ',adj_r2_score(h_svr,y_train,pred_X_train))\n",
    "\n",
    "print (\"TESTING METRICS:  \\n\")\n",
    "pred_X_test = h_svr.predict(ncless_X_test)\n",
    "mse_X_test =  mean_squared_error(y_test, pred_X_test)\n",
    "r2_X_test = r2_score(y_test, pred_X_test)\n",
    "print('Root Mean Squared Error X TEST : ', np.sqrt(mse_X_test))\n",
    "print('R-Squre X TEST : ', r2_X_test)\n",
    "#print ('Adjusted R-Square X Train: ',adj_r2_score(h_svr,y_test,pred_X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "<b>Check the after-training (train set) RMSE value, and compare it with prediction's (test set ) RMSE value, check the R-Square value as well (what is mean by Negative value here ?) </b>\n",
    "\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Compare SVR model RMSE & R2 values with LinearRegression Model -- What is your interpretation -- which model is better out of these two ?</b>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<h2>DecisionTree Regression Model</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "h_tr_reg = DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=None, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h_tr_reg.fit(ncless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING METRICS:    \n",
      "\n",
      "Root Mean Squared Error X Train :  171.72822188499134\n",
      "R-Squre X Train :  0.9999953561287928 \n",
      "\n",
      "TESTING METRICS:  \n",
      "\n",
      "Root Mean Squared Error X TEST :  40153.84311107769\n",
      "R-Squre X TEST :  0.7370551052061677\n"
     ]
    }
   ],
   "source": [
    "print (\"TRAINING METRICS:    \\n\")\n",
    "pred_X_train = h_tr_reg.predict(ncless_X_train)\n",
    "mse_X_train = mean_squared_error(y_train, pred_X_train)\n",
    "r2_X_train = r2_score(y_train, pred_X_train)\n",
    "print('Root Mean Squared Error X Train : ', np.sqrt(mse_X_train))\n",
    "print('R-Squre X Train : ', r2_X_train, \"\\n\")\n",
    "#print ('Adjusted R-Square X Train: ',adj_r2_score(h_tr_reg,y_train,pred_X_train))\n",
    "\n",
    "print (\"TESTING METRICS:  \\n\")\n",
    "pred_X_test = h_tr_reg.predict(ncless_X_test)\n",
    "mse_X_test =  mean_squared_error(y_test, pred_X_test)\n",
    "r2_X_test = r2_score(y_test, pred_X_test)\n",
    "print('Root Mean Squared Error X TEST : ', np.sqrt(mse_X_test))\n",
    "print('R-Squre X TEST : ', r2_X_test)\n",
    "#print ('Adjusted R-Square X Train: ',adj_r2_score(h_tr_reg,y_test,pred_X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "\n",
    "<b>Check the after-training (train set) RMSE value, and compare it with prediction's (test set ) RMSE value, check the R-Square value as well </b>\n",
    "\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-block alert-warning\">\n",
    "<b>Compare all the three model's RMSE & R2 values with each other -- What is your interpretation -- which model is better out of these three</b>\n",
    "</div>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<h2>SGD Regression (Optimisation)</h2>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import SGDRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(alpha=0.0001, average=False, epsilon=0.1, eta0=0.01,\n",
       "       fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',\n",
       "       loss='squared_loss', max_iter=None, n_iter=None, penalty='l2',\n",
       "       power_t=0.25, random_state=None, shuffle=True, tol=None, verbose=0,\n",
       "       warm_start=False)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "#eta0=0.0001, max_iter=1000 , tol=None\n",
    "sgd_reg = SGDRegressor() \n",
    "sgd_reg.fit(ncless_X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING METRICS:    \n",
      "\n",
      "Root Mean Squared Error X Train :  3.7391197302333824e+18\n",
      "R-Squre X Train :  -2.2015855396064736e+27\n",
      "Adjusted R-Square X Train:  -2.2716625329095973e+27 \n",
      "\n",
      "TESTING METRICS:  \n",
      "\n",
      "Root Mean Squared Error X TEST :  2.8112998145817856e+18\n",
      "R-Squre X TEST :  -1.288916654928598e+27\n",
      "Adjusted R-Square X Train:  -1.470881359153812e+27\n"
     ]
    }
   ],
   "source": [
    "print (\"TRAINING METRICS:    \\n\")\n",
    "pred_X_train = sgd_reg.predict(ncless_X_train)\n",
    "mse_X_train = mean_squared_error(y_train, pred_X_train)\n",
    "r2_X_train = r2_score(y_train, pred_X_train)\n",
    "print('Root Mean Squared Error X Train : ', np.sqrt(mse_X_train))\n",
    "print('R-Squre X Train : ', r2_X_train)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(sgd_reg,y_train,pred_X_train),\"\\n\")\n",
    "\n",
    "print (\"TESTING METRICS:  \\n\")\n",
    "pred_X_test = sgd_reg.predict(ncless_X_test)\n",
    "mse_X_test =  mean_squared_error(y_test, pred_X_test)\n",
    "r2_X_test = r2_score(y_test, pred_X_test)\n",
    "print('Root Mean Squared Error X TEST : ', np.sqrt(mse_X_test))\n",
    "print('R-Squre X TEST : ', r2_X_test)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(sgd_reg,y_test,pred_X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-success\">\n",
    "<h2> Lasso, Ridge, ElasticNet Regressions (Regularisation -- Needs Hyperparameters)</h2>\n",
    "<b>Lasso (Least Absolute Shrinkage Selector Operator) - L1 Regularisation, Ridge - L2 Regularisation</b>\n",
    "</div>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "(Elaborated discussion on \"Regularisation\" on next demo)\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regularisation helps to solve the problem of over-fitting (i.e high variance)\n",
    "### https://courses.cs.washington.edu/courses/cse416/18sp/slides/L4b_lasso-regression.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso, Ridge, ElasticNet\n",
    "\n",
    "lasso_reg = Lasso()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=1000,\n",
       "   normalize=False, positive=False, precompute=False, random_state=None,\n",
       "   selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_reg.fit(ncless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING METRICS:    \n",
      "\n",
      "Root Mean Squared Error X Train :  35228.602914974355\n",
      "R-Squre X Train :  0.8045716069144381\n",
      "Adjusted R-Square X Train:  0.7983510745085316 \n",
      "\n",
      "TESTING METRICS:  \n",
      "\n",
      "Root Mean Squared Error X TEST :  30797.143573002522\n",
      "R-Squre X TEST :  0.8453209902914831\n",
      "Adjusted R-Square X Train:  0.8234839536267513\n"
     ]
    }
   ],
   "source": [
    "print (\"TRAINING METRICS:    \\n\")\n",
    "pred_X_train = lasso_reg.predict(ncless_X_train)\n",
    "mse_X_train = mean_squared_error(y_train, pred_X_train)\n",
    "r2_X_train = r2_score(y_train, pred_X_train)\n",
    "print('Root Mean Squared Error X Train : ', np.sqrt(mse_X_train))\n",
    "print('R-Squre X Train : ', r2_X_train)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(lasso_reg,y_train,pred_X_train),\"\\n\")\n",
    "\n",
    "print (\"TESTING METRICS:  \\n\")\n",
    "pred_X_test = lasso_reg.predict(ncless_X_test)\n",
    "mse_X_test =  mean_squared_error(y_test, pred_X_test)\n",
    "r2_X_test = r2_score(y_test, pred_X_test)\n",
    "print('Root Mean Squared Error X TEST : ', np.sqrt(mse_X_test))\n",
    "print('R-Squre X TEST : ', r2_X_test)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(lasso_reg,y_test,pred_X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=False, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rig_reg = Ridge()\n",
    "rig_reg.fit(ncless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING METRICS:    \n",
      "\n",
      "Root Mean Squared Error X Train :  35228.68244537608\n",
      "R-Squre X Train :  0.8045707245339366\n",
      "Adjusted R-Square X Train:  0.7983501640416482 \n",
      "\n",
      "TESTING METRICS:  \n",
      "\n",
      "Root Mean Squared Error X TEST :  30793.340561731868\n",
      "R-Squre X TEST :  0.8453591892689847\n",
      "Adjusted R-Square X Train:  0.8235275454010766\n"
     ]
    }
   ],
   "source": [
    "print (\"TRAINING METRICS:    \\n\")\n",
    "pred_X_train = rig_reg.predict(ncless_X_train)\n",
    "mse_X_train = mean_squared_error(y_train, pred_X_train)\n",
    "r2_X_train = r2_score(y_train, pred_X_train)\n",
    "print('Root Mean Squared Error X Train : ', np.sqrt(mse_X_train))\n",
    "print('R-Squre X Train : ', r2_X_train)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(rig_reg,y_train,pred_X_train),\"\\n\")\n",
    "\n",
    "print (\"TESTING METRICS:  \\n\")\n",
    "pred_X_test = rig_reg.predict(ncless_X_test)\n",
    "mse_X_test =  mean_squared_error(y_test, pred_X_test)\n",
    "r2_X_test = r2_score(y_test, pred_X_test)\n",
    "print('Root Mean Squared Error X TEST : ', np.sqrt(mse_X_test))\n",
    "print('R-Squre X TEST : ', r2_X_test)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(rig_reg,y_test,pred_X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=0.5, copy_X=True, fit_intercept=True, max_iter=None,\n",
       "   normalize=True, random_state=None, solver='auto', tol=0.001)"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rig_reg1 = Ridge(alpha=0.5,normalize=True)\n",
    "rig_reg1.fit(ncless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING METRICS:    \n",
      "\n",
      "Root Mean Squared Error X Train :  36817.8493337206\n",
      "R-Squre X Train :  0.7865414020392592\n",
      "Adjusted R-Square X Train:  0.7797469639078828 \n",
      "\n",
      "TESTING METRICS:  \n",
      "\n",
      "Root Mean Squared Error X TEST :  31875.60018120333\n",
      "R-Squre X TEST :  0.8342981912027327\n",
      "Adjusted R-Square X Train:  0.810904994666648\n"
     ]
    }
   ],
   "source": [
    "print (\"TRAINING METRICS:    \\n\")\n",
    "pred_X_train = rig_reg1.predict(ncless_X_train)\n",
    "mse_X_train = mean_squared_error(y_train, pred_X_train)\n",
    "r2_X_train = r2_score(y_train, pred_X_train)\n",
    "print('Root Mean Squared Error X Train : ', np.sqrt(mse_X_train))\n",
    "print('R-Squre X Train : ', r2_X_train)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(rig_reg1,y_train,pred_X_train),\"\\n\")\n",
    "\n",
    "print (\"TESTING METRICS:  \\n\")\n",
    "pred_X_test = rig_reg1.predict(ncless_X_test)\n",
    "mse_X_test =  mean_squared_error(y_test, pred_X_test)\n",
    "r2_X_test = r2_score(y_test, pred_X_test)\n",
    "print('Root Mean Squared Error X TEST : ', np.sqrt(mse_X_test))\n",
    "print('R-Squre X TEST : ', r2_X_test)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(rig_reg1,y_test,pred_X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ElasticNet(alpha=1.0, copy_X=True, fit_intercept=True, l1_ratio=0.5,\n",
       "      max_iter=1000, normalize=False, positive=False, precompute=False,\n",
       "      random_state=None, selection='cyclic', tol=0.0001, warm_start=False)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elst_net_reg = ElasticNet()\n",
    "elst_net_reg.fit(ncless_X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAINING METRICS:    \n",
      "\n",
      "Root Mean Squared Error X Train :  36390.406782981954\n",
      "R-Squre X Train :  0.7914689928284145\n",
      "Adjusted R-Square X Train:  0.7848314010882049 \n",
      "\n",
      "TESTING METRICS:  \n",
      "\n",
      "Root Mean Squared Error X TEST :  31367.082501122495\n",
      "R-Squre X TEST :  0.8395429660432716\n",
      "Adjusted R-Square X Train:  0.8168902083082041\n"
     ]
    }
   ],
   "source": [
    "print (\"TRAINING METRICS:    \\n\")\n",
    "pred_X_train = elst_net_reg.predict(ncless_X_train)\n",
    "mse_X_train = mean_squared_error(y_train, pred_X_train)\n",
    "r2_X_train = r2_score(y_train, pred_X_train)\n",
    "print('Root Mean Squared Error X Train : ', np.sqrt(mse_X_train))\n",
    "print('R-Squre X Train : ', r2_X_train)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(elst_net_reg,y_train,pred_X_train),\"\\n\")\n",
    "\n",
    "print (\"TESTING METRICS:  \\n\")\n",
    "pred_X_test = elst_net_reg.predict(ncless_X_test)\n",
    "mse_X_test =  mean_squared_error(y_test, pred_X_test)\n",
    "r2_X_test = r2_score(y_test, pred_X_test)\n",
    "print('Root Mean Squared Error X TEST : ', np.sqrt(mse_X_test))\n",
    "print('R-Squre X TEST : ', r2_X_test)\n",
    "print ('Adjusted R-Square X Train: ',adj_r2_score(elst_net_reg,y_test,pred_X_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<h3>Great that we learned to use Regression Algorithms. Lets move to Hands-On Demo2 to understand and apply some more machine learning concepts</h3>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-warning\">\n",
    "<h2>Please post your questions to \"praveenatspark@gmail.com\", it will be my pleasure to help.</h2>\n",
    "</div>"
   ]
  }
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