an-easy-approach-to-regularized-linear-regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a5f517ff5988ce6007b8c751e459835c800d8641"
   },
   "source": [
    "# Regularized Linear Regression \n",
    "\n",
    "**This competition helps alot to understand the methodology of a Data Science project and is a good option as a starter in this field.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "6e56eeb154f9cf98e79d66e0fec10be389c8c599"
   },
   "source": [
    "#### Importing some important libraries to work with"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd\n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import Lasso, Ridge, ElasticNet\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from scipy.stats import norm\n",
    "from scipy import stats \n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e565bfade6d11c74b0ef3e09fb1786bf7896d7d2"
   },
   "source": [
    "**Load datasets.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "8dbc40c3451affcb835ec90ef56d3d60bc47e6d8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "shape of train dataset: (1460, 81)\n",
      "shape of test dataset: (1459, 80)\n"
     ]
    }
   ],
   "source": [
    "train= pd.read_csv('train.csv')\n",
    "test= pd.read_csv('test.csv')\n",
    "\n",
    "print(\"shape of train dataset:\", train.shape)\n",
    "print(\"shape of test dataset:\", test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_uuid": "e044b84c02e8538c2262fdcbdfc42402867761de"
   },
   "outputs": [
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       "      <td>Y</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
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       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
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       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
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       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
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       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
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       "      <td>GasA</td>\n",
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       "      <td>Y</td>\n",
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       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning    ...     SaleType  SaleCondition SalePrice\n",
       "0   1          60       RL    ...           WD         Normal    208500\n",
       "1   2          20       RL    ...           WD         Normal    181500\n",
       "2   3          60       RL    ...           WD         Normal    223500\n",
       "3   4          70       RL    ...           WD        Abnorml    140000\n",
       "4   5          60       RL    ...           WD         Normal    250000\n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "4c08e4754900964af6990f5c59f5f8b8d8f95227"
   },
   "source": [
    "# Data Preprocessing\n",
    "\n",
    "#### Visualization of target variable SalePrice data distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "2ea892c4263483c77ce556de1bada66f92b80b84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4be89e40b8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "sns.distplot(train.SalePrice, fit=norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "1e50c3172800915bdb1970436dcc1758b8234c6a"
   },
   "source": [
    "The target variable is right skewed. So we need to transform this variable and make it more normally distributed.\n",
    "\n",
    "In case of positive skewness, log transformation works well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "5726f29810941277b6a3cf07d4ea556b9fc04af2"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4be8912748>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,6])\n",
    "sns.distplot(np.log(train.SalePrice), fit=norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c1d078e5423e7df397907abc49e04e8f86de4318"
   },
   "source": [
    "The data is now normally distributed as we corrected skew."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "cba60361ac6780944b72b0c6505fe33b0b8dac8d"
   },
   "source": [
    "#### Let's visualize relationship of features with SalePrice using Seaborn's Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "bc8e9c9788f5a22d1ada56fc075566a865373004"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bdec6efd0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "correlation= train.corr()\n",
    "plt.figure(figsize=[12,8])\n",
    "plt.title('Correlation of Numeric Features with Sale Price')\n",
    "sns.heatmap(correlation,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "cfbb8a82e077e21b0cd25a8c90953fd675450e6e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice       1.000000\n",
       "OverallQual     0.790982\n",
       "GrLivArea       0.708624\n",
       "GarageCars      0.640409\n",
       "GarageArea      0.623431\n",
       "TotalBsmtSF     0.613581\n",
       "1stFlrSF        0.605852\n",
       "FullBath        0.560664\n",
       "TotRmsAbvGrd    0.533723\n",
       "YearBuilt       0.522897\n",
       "YearRemodAdd    0.507101\n",
       "GarageYrBlt     0.486362\n",
       "MasVnrArea      0.477493\n",
       "Fireplaces      0.466929\n",
       "BsmtFinSF1      0.386420\n",
       "LotFrontage     0.351799\n",
       "WoodDeckSF      0.324413\n",
       "2ndFlrSF        0.319334\n",
       "OpenPorchSF     0.315856\n",
       "HalfBath        0.284108\n",
       "LotArea         0.263843\n",
       "BsmtFullBath    0.227122\n",
       "BsmtUnfSF       0.214479\n",
       "BedroomAbvGr    0.168213\n",
       "ScreenPorch     0.111447\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "correlation= train.corr()\n",
    "correlation=correlation['SalePrice'].sort_values(ascending=False)\n",
    "pos_correlation=correlation.head(25)\n",
    "pos_correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "3df8c81f588812dc6a756cd01d90783c209d1ad9"
   },
   "source": [
    "#### Visualizing some highly correlated features to get better understanding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "95c57e5cb8b2ebb6ee163371fdf4e8587a399c44"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bd8d60b00>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "sns.regplot(train['OverallQual'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "172425eb409addb3ca6e8d0faa4d2bd63a871959"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bd8f99630>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "sns.regplot(train['GrLivArea'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "5f79abf194614690d94c83c6cc98f3cc0bc33f47"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bd8f7d4e0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "sns.regplot(train['GarageArea'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "dc1bc7d2c989214af13ee75496cdb61251902480"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bd8ee46d8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[8,5])\n",
    "sns.regplot(train['TotalBsmtSF'], train['SalePrice'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "a09b3e781865d3fd82d42ebf287da853f4d7cf6f"
   },
   "source": [
    "## Data Cleaning\n",
    "\n",
    "### Dealing with Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_uuid": "34d791a285a0f8d1e3d169b06c779b0dbd9b8fc2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Before: (1460, 81)\n",
      "After: (1457, 81)\n"
     ]
    }
   ],
   "source": [
    "print(\"Before:\",train.shape)\n",
    "\n",
    "train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index, inplace=True)\n",
    "train.drop(train[(train['GarageArea']>1200) & (train['SalePrice']<100000)].index, inplace=True)\n",
    "train.drop(train[(train['TotalBsmtSF']>6000) & (train['SalePrice']<200000)].index, inplace=True)\n",
    "train.reset_index(drop=True, inplace=True)\n",
    "\n",
    "print(\"After:\",train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "26a4844631e0a6b63d0d40491b703659571c0d32"
   },
   "source": [
    "### Let's move to handle missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_uuid": "434e2b745b6e202ba85e6afbfdf4a9a201be16a4"
   },
   "outputs": [],
   "source": [
    "data= pd.concat([train.drop(['SalePrice'], axis=1), test])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "858b4b600c5c9d46bf82d78a925fb2265823adc6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bd8e4a208>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx= (data.isnull().sum())/len(data)*100\n",
    "xx=xx.sort_values(ascending=False).head(30)\n",
    "\n",
    "plt.figure(figsize=(15, 6))\n",
    "plt.xticks(rotation=\"90\")\n",
    "sns.barplot(xx.keys(), xx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b13d0ac8dcd8f471a5a37c38993b1f4da3448826"
   },
   "source": [
    "#### Get percentage of these missing values in features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "6737e5d7b16b7b958d43e72827ee904698974bf6"
   },
   "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>Total missing count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>2907</td>\n",
       "      <td>99.691358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>2812</td>\n",
       "      <td>96.433471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>2718</td>\n",
       "      <td>93.209877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>2345</td>\n",
       "      <td>80.418381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>1419</td>\n",
       "      <td>48.662551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>486</td>\n",
       "      <td>16.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>159</td>\n",
       "      <td>5.452675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>159</td>\n",
       "      <td>5.452675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>159</td>\n",
       "      <td>5.452675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>159</td>\n",
       "      <td>5.452675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>157</td>\n",
       "      <td>5.384088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>82</td>\n",
       "      <td>2.812071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>82</td>\n",
       "      <td>2.812071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>81</td>\n",
       "      <td>2.777778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>80</td>\n",
       "      <td>2.743484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>79</td>\n",
       "      <td>2.709191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>24</td>\n",
       "      <td>0.823045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>23</td>\n",
       "      <td>0.788752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>4</td>\n",
       "      <td>0.137174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>2</td>\n",
       "      <td>0.068587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>2</td>\n",
       "      <td>0.068587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>2</td>\n",
       "      <td>0.068587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>2</td>\n",
       "      <td>0.068587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.034294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>1</td>\n",
       "      <td>0.034294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>1</td>\n",
       "      <td>0.034294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.034294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>1</td>\n",
       "      <td>0.034294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>1</td>\n",
       "      <td>0.034294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType</th>\n",
       "      <td>1</td>\n",
       "      <td>0.034294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Total missing count  Percentage \n",
       "PoolQC                       2907    99.691358\n",
       "MiscFeature                  2812    96.433471\n",
       "Alley                        2718    93.209877\n",
       "Fence                        2345    80.418381\n",
       "FireplaceQu                  1419    48.662551\n",
       "LotFrontage                   486    16.666667\n",
       "GarageCond                    159     5.452675\n",
       "GarageQual                    159     5.452675\n",
       "GarageYrBlt                   159     5.452675\n",
       "GarageFinish                  159     5.452675\n",
       "GarageType                    157     5.384088\n",
       "BsmtCond                       82     2.812071\n",
       "BsmtExposure                   82     2.812071\n",
       "BsmtQual                       81     2.777778\n",
       "BsmtFinType2                   80     2.743484\n",
       "BsmtFinType1                   79     2.709191\n",
       "MasVnrType                     24     0.823045\n",
       "MasVnrArea                     23     0.788752\n",
       "MSZoning                        4     0.137174\n",
       "BsmtHalfBath                    2     0.068587\n",
       "Utilities                       2     0.068587\n",
       "Functional                      2     0.068587\n",
       "BsmtFullBath                    2     0.068587\n",
       "BsmtFinSF1                      1     0.034294\n",
       "Exterior1st                     1     0.034294\n",
       "Exterior2nd                     1     0.034294\n",
       "BsmtFinSF2                      1     0.034294\n",
       "BsmtUnfSF                       1     0.034294\n",
       "TotalBsmtSF                     1     0.034294\n",
       "SaleType                        1     0.034294"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total=data.isnull().sum().sort_values(ascending=False)\n",
    "percent=((data.isnull().sum()/data.isnull().count())*100).sort_values(ascending=False)\n",
    "missing= pd.concat([total,percent], axis=1, join='outer', keys=['Total missing count', 'Percentage '])\n",
    "missing.head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "27c5f18a6a019f64c425890f6a9bc3aeeef45ec6"
   },
   "source": [
    "#### Updating Garage features\n",
    "\n",
    "\n",
    "Fill all missing values of Garage features with 'None' as they have no Garage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_uuid": "d352c03a4c5a2d57ccb1039898a2e795c9ec3d36"
   },
   "outputs": [],
   "source": [
    "train['GarageQual'].fillna('None', inplace=True)\n",
    "test['GarageQual'].fillna('None', inplace=True)\n",
    "train['GarageFinish'].fillna('None', inplace=True)\n",
    "test['GarageFinish'].fillna('None', inplace=True)\n",
    "train['GarageYrBlt'].fillna('None', inplace=True)\n",
    "test['GarageYrBlt'].fillna('None', inplace=True)\n",
    "train['GarageType'].fillna('None', inplace=True)\n",
    "test['GarageType'].fillna('None', inplace=True)\n",
    "train['GarageCond'].fillna('None', inplace=True)\n",
    "test['GarageCond'].fillna('None', inplace=True)\n",
    "test.loc[test['Id']==2577, 'GarageType']='None'\n",
    "\n",
    "test['GarageCars'].fillna(0, inplace=True)\n",
    "test['GarageArea'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
    "collapsed": true
   },
   "source": [
    "#### Updating Basement features\n",
    "\n",
    "Let's first have a look at BsmtCond. There are some rows having Basement but no Basement condition. Let's dig out more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "1abf3a5f04b9532c2e33dfd1fc95b3d489aa4819"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>TotalBsmtSF</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>580</th>\n",
       "      <td>2041</td>\n",
       "      <td>1426.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>1044.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>2121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>2186</td>\n",
       "      <td>1127.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>BLQ</td>\n",
       "      <td>1033.0</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064</th>\n",
       "      <td>2525</td>\n",
       "      <td>995.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>755.0</td>\n",
       "      <td>240.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  TotalBsmtSF BsmtCond    ...    BsmtFinType1 BsmtFinSF1 BsmtUnfSF\n",
       "580   2041       1426.0      NaN    ...             GLQ     1044.0       0.0\n",
       "660   2121          NaN      NaN    ...             NaN        NaN       NaN\n",
       "725   2186       1127.0      NaN    ...             BLQ     1033.0      94.0\n",
       "1064  2525        995.0      NaN    ...             ALQ      755.0     240.0\n",
       "\n",
       "[4 rows x 8 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['TotalBsmtSF']!=0) & (data['BsmtCond'].isnull()==True)][['Id','TotalBsmtSF','BsmtCond','BsmtQual','BsmtExposure',\n",
    "                                                                    'BsmtFinType1','BsmtFinSF1','BsmtUnfSF']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "eda51947a6582205240a9e53cd756b1e46973629"
   },
   "source": [
    "Fill these BsmtCond null values with BsmtQual and others with None as they have no Basement at all."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_uuid": "e53d7fdd429de4abe309b4f16bb67feaaeb87c3e"
   },
   "outputs": [],
   "source": [
    "test.loc[test['Id']==2041, 'BsmtCond']='Gd'\n",
    "test.loc[test['Id']==2186, 'BsmtCond']='TA'\n",
    "test.loc[test['Id']==2525, 'BsmtCond']='TA'\n",
    "train['BsmtCond'].fillna('None', inplace=True)\n",
    "test['BsmtCond'].fillna('None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "608ef7e1f692667d61ffd515cac60dc2a9f4c57e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>949</td>\n",
       "      <td>936.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>936.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>1488</td>\n",
       "      <td>1595.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1595.0</td>\n",
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       "      <th>660</th>\n",
       "      <td>2121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>888</th>\n",
       "      <td>2349</td>\n",
       "      <td>725.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>725.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id  TotalBsmtSF BsmtCond    ...    BsmtFinType1 BsmtFinSF1 BsmtUnfSF\n",
       "947   949        936.0       TA    ...             Unf        0.0     936.0\n",
       "27   1488       1595.0       TA    ...             Unf        0.0    1595.0\n",
       "660  2121          NaN      NaN    ...             NaN        NaN       NaN\n",
       "888  2349        725.0       TA    ...             Unf        0.0     725.0\n",
       "\n",
       "[4 rows x 8 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['TotalBsmtSF']!=0) & (data['BsmtExposure'].isnull()==True)][['Id','TotalBsmtSF','BsmtCond','BsmtQual','BsmtExposure',\n",
    "                                                                    'BsmtFinType1','BsmtFinSF1','BsmtUnfSF']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e632bb16a22261de488a525a221f0bb2fd026a13"
   },
   "source": [
    "Fill these BsmtExposure null values with values of BsmtQual and other with None."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_uuid": "6d94f2675a53082644324ae7ad8835e75660a7be"
   },
   "outputs": [],
   "source": [
    "train.loc[train['Id']==949, 'BsmtExposure']='Gd'\n",
    "test.loc[test['Id']==1488, 'BsmtExposure']='Gd'\n",
    "test.loc[test['Id']==2349, 'BsmtExposure']='Gd'\n",
    "train['BsmtExposure'].fillna('None', inplace=True)\n",
    "test['BsmtExposure'].fillna('None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "ba143755e7a4f495738cdcdfdf1ad5715f040f7a"
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>2218</td>\n",
       "      <td>173.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fa</td>\n",
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       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>356.0</td>\n",
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       "  </tbody>\n",
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      ],
      "text/plain": [
       "       Id  TotalBsmtSF BsmtQual    ...    BsmtFinType1 BsmtFinSF1 BsmtUnfSF\n",
       "660  2121          NaN      NaN    ...             NaN        NaN       NaN\n",
       "757  2218        173.0      NaN    ...             Unf        0.0     173.0\n",
       "758  2219        356.0      NaN    ...             Unf        0.0     356.0\n",
       "\n",
       "[3 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['TotalBsmtSF']!=0) & (data['BsmtQual'].isnull()==True)][['Id','TotalBsmtSF','BsmtQual','BsmtCond','BsmtExposure',\n",
    "                                                                    'BsmtFinType1','BsmtFinSF1','BsmtUnfSF']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "438ce333b1e7e6884368a0ee9f5b3b82b914708c"
   },
   "source": [
    "Fill these BsmtQual null values with values of BsmtCond and others with None."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_uuid": "cefbca70eeadcbbb76789af3364cf21890c4ac31"
   },
   "outputs": [],
   "source": [
    "test.loc[test['Id']==2218, 'BsmtQual']='Fa'\n",
    "test.loc[test['Id']==2219, 'BsmtQual']='TA'\n",
    "train['BsmtQual'].fillna('None', inplace=True)\n",
    "test['BsmtQual'].fillna('None', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "3939969aede0c2da0a9fc9c8f5c83d1f70ec1b2a"
   },
   "outputs": [
    {
     "data": {
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       "       Id  TotalBsmtSF BsmtQual    ...    BsmtFinType1 BsmtFinSF1 BsmtUnfSF\n",
       "660  2121          NaN      NaN    ...             NaN        NaN       NaN\n",
       "\n",
       "[1 rows x 8 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "data[(data['TotalBsmtSF']!=0) & (data['BsmtFinType1'].isnull()==True)][['Id','TotalBsmtSF','BsmtQual','BsmtCond','BsmtExposure',\n",
    "                                                                        'BsmtFinType1','BsmtFinSF1','BsmtUnfSF']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "653da8f5343349591dc48a8cd50db47d80bb4346"
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   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>2121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Id  TotalBsmtSF     ...      BsmtExposure BsmtFullBath\n",
       "660  2121          NaN     ...               NaN          NaN\n",
       "\n",
       "[1 rows x 6 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[(data['TotalBsmtSF']!=0) & (data['BsmtFullBath'].isnull()==True)][['Id','TotalBsmtSF','BsmtQual','BsmtCond',\n",
    "                                                                        'BsmtExposure','BsmtFullBath']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "e467d118f54e086537480748dfb29f06c545803f"
   },
   "source": [
    "Fill all other Basement features missing values with None or Zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_uuid": "418bd378cc936b03812e5f2e28124629e565d3c7"
   },
   "outputs": [],
   "source": [
    "train['BsmtFinType1'].fillna('None', inplace=True)\n",
    "test['BsmtFinType1'].fillna('None', inplace=True)\n",
    "test['BsmtFinSF1'].fillna(0, inplace=True)\n",
    "train['BsmtFinType2'].fillna('None', inplace=True)\n",
    "test['BsmtFinType2'].fillna('None', inplace=True)\n",
    "test['BsmtUnfSF'].fillna(0, inplace=True)\n",
    "test['TotalBsmtSF'].fillna(0, inplace=True)\n",
    "\n",
    "test['BsmtFullBath'].fillna(0, inplace=True)\n",
    "test['BsmtHalfBath'].fillna(0, inplace=True)\n",
    "\n",
    "test['BsmtFinSF1'].fillna(0, inplace=True)\n",
    "test['BsmtFinSF2'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "541c1beb372cc4eccb763e6d35aac5dec0980990"
   },
   "source": [
    "#### Updating FireplaceQu, LotFrontage, MasVnrType and MasVnrArea, PoolQC, MiscFeature, Alley, Fence, Electrical, Functional, SaleType, Exterior1st, KitchenQual, Exterior2nd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_uuid": "e4f3d78f79b0502d47fba5072f664d0e65018b66"
   },
   "outputs": [],
   "source": [
    "train['FireplaceQu'].fillna('None', inplace=True)\n",
    "test['FireplaceQu'].fillna('None', inplace=True)\n",
    "\n",
    "train.loc[train['LotFrontage'].isnull()==True, 'LotFrontage']= train['LotFrontage'].mean()\n",
    "test.loc[test['LotFrontage'].isnull()==True, 'LotFrontage']= test['LotFrontage'].mean()\n",
    "\n",
    "train['MasVnrType'].fillna('None', inplace=True)\n",
    "test['MasVnrType'].fillna('None', inplace=True)\n",
    "\n",
    "train['MasVnrArea'].fillna(0, inplace=True)\n",
    "test['MasVnrArea'].fillna(0, inplace=True)\n",
    "\n",
    "train['PoolQC'].fillna('None', inplace=True)\n",
    "test['PoolQC'].fillna('None', inplace=True)\n",
    "\n",
    "train['MiscFeature'].fillna('None', inplace=True)\n",
    "test['MiscFeature'].fillna('None', inplace=True)\n",
    "\n",
    "train['Alley'].fillna('None', inplace=True)\n",
    "test['Alley'].fillna('None', inplace=True)\n",
    "\n",
    "train['Fence'].fillna('None', inplace=True)\n",
    "test['Fence'].fillna('None', inplace=True)\n",
    "\n",
    "train['MSZoning'].fillna('RL', inplace=True)\n",
    "test['MSZoning'].fillna('RL', inplace=True)\n",
    "\n",
    "train['Electrical'].fillna('SBrkr', inplace=True)\n",
    "test['Functional'].fillna('Typ', inplace=True)\n",
    "test['SaleType'].fillna('WD', inplace=True)\n",
    "test['Exterior1st'].fillna('VinylSd', inplace=True)\n",
    "test['KitchenQual'].fillna('TA', inplace=True)\n",
    "test['Exterior2nd'].fillna('VinylSd', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_uuid": "3b29d5dcad8c6071da95f49227218da6a4258176"
   },
   "outputs": [],
   "source": [
    "train.drop('Utilities', axis=1, inplace=True)\n",
    "test.drop('Utilities', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "c25c0d29233f957e54c6d6750ceda97ddf421034"
   },
   "source": [
    "#### Check again if we have any feature left with missing vale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_uuid": "405f64bb9b02079ef9af3c4a26be5941ce3867b5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train dataset:\n",
      " SalePrice        0\n",
      "SaleCondition    0\n",
      "RoofMatl         0\n",
      "Exterior1st      0\n",
      "Exterior2nd      0\n",
      "dtype: int64\n",
      "\n",
      "\n",
      "Test dataset:\n",
      " SaleCondition    0\n",
      "Foundation       0\n",
      "RoofMatl         0\n",
      "Exterior1st      0\n",
      "Exterior2nd      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Train dataset:\\n\",train.isnull().sum().sort_values(ascending=False).head(5))\n",
    "print(\"\\n\\nTest dataset:\\n\",test.isnull().sum().sort_values(ascending=False).head(5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering and Transformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['renovated']= 1\n",
    "train.loc[(train['YearBuilt']!=train['YearRemodAdd']),'renovated' ]=0\n",
    "test['renovated']= 1\n",
    "test.loc[(test['YearBuilt']!=test['YearRemodAdd']),'renovated' ]=0\n",
    "\n",
    "train['Total_porch']= (train['OpenPorchSF'])+train['ScreenPorch']+train['3SsnPorch']\n",
    "train['BsmtFinSF_total']= (train['BsmtFinSF1']**2)+train['BsmtFinSF2']\n",
    "train['Bath']= (train['FullBath']**2)+ train['HalfBath']\n",
    "train['BsmtBath']= (train['BsmtFullBath'])+train['BsmtHalfBath']\n",
    "train['TotalSF'] = train['TotalBsmtSF'] + train['1stFlrSF'] + train['2ndFlrSF']\n",
    "train['Total_garage']= (train['GarageCars']**5)+ train['GarageArea']\n",
    "\n",
    "\n",
    "test['Total_porch']= (test['OpenPorchSF'])+test['ScreenPorch']+test['3SsnPorch']\n",
    "test['BsmtFinSF_total']= (test['BsmtFinSF1']**2)+test['BsmtFinSF2']\n",
    "test['Bath']= (test['FullBath']**2)+ test['HalfBath']\n",
    "test['BsmtBath']= (test['BsmtFullBath'])+test['BsmtHalfBath']\n",
    "test['TotalSF'] = test['TotalBsmtSF'] + test['1stFlrSF'] + test['2ndFlrSF']\n",
    "test['Total_garage']= (test['GarageCars']**5)+ test['GarageArea']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "223f4adce5768c757c5ee80a9e6ac178eea0ac89"
   },
   "source": [
    "#### Apply log tranformation to target variable and Transform categorical features values into dummies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "0970700d87fccae470e2b64d4e38348608aa9394"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1457, 409), (1459, 409))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=np.asarray(train['SalePrice'])\n",
    "y=np.log(y+1)\n",
    "\n",
    "X_train = pd.get_dummies(pd.concat((train.drop([\"SalePrice\", \"Id\"], axis=1),\n",
    "                                          test.drop([\"Id\"], axis=1)), axis=0)).iloc[: train.shape[0]]\n",
    "X_test = pd.get_dummies(pd.concat((train.drop([\"SalePrice\", \"Id\"], axis=1),\n",
    "                                         test.drop([\"Id\"], axis=1)), axis=0)).iloc[train.shape[0]:]\n",
    "\n",
    "X_train.shape, X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "0ec80a5a845d4a33cbb8038cf62f025fd7a61f29"
   },
   "source": [
    "# Model Building\n",
    "\n",
    "Let's build our model using:\n",
    "\n",
    "* Ridge\n",
    "* Lasso\n",
    "* Elastic Net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_rmse(model):\n",
    "    return np.sqrt(-cross_val_score(model, X_train, y, cv=5, scoring=\"neg_mean_squared_error\")).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "f8a8ec813488b73ccfed7f339f6f198fd4485586"
   },
   "source": [
    "### 1. Ridge Regression \n",
    "\n",
    "Ridge regression uses L2 penalty which means it adds penalty of `squared magnitude` of coefficients to it's `cost function`.\n",
    "\n",
    "* **alpha** is used for regularization strength\n",
    "\n",
    "* If it is zero, it works same as `LinearRegression`\n",
    "\n",
    "* Increase in alpha increases smoothness (reduces complexity by decreasing variance)\n",
    "\n",
    "* Decrease in alpha increases magnitude of coefficients (increases complexity by decreasing bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "b46ae6d6a82ddf6c5bf51d1240cde2127809ca9f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1149522103540869"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RR= Ridge(alpha=15)\n",
    "RR.fit(X_train, y)\n",
    "return_rmse(RR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "649655338474eee7c8b718c2297a79924efb943c"
   },
   "source": [
    "### 2. Lasso Regression\n",
    "\n",
    "Lasso (Least Absolute Shrinkage and Selection Operator) uses L1 penalty which means it adds penalty of `absolute value of magnitude` of coefficients to it's `cost function`. Unlike L2, it can lead to zero coefficients. So in this case some features are completely neglected thus less prone to overfit. By assigning zero coefficients to less important features it helps in feature selection\n",
    "\n",
    "* **alpha** works same as `Ridge`\n",
    "\n",
    "* If it is zero, it works same as `LinearRegression`\n",
    "\n",
    "* Increase in alpha increases smoothness (reduces complexity by decreasing variance)\n",
    "\n",
    "* Decrease in alpha increases magnitude of coefficients (increases complexity by decreasing bias)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "b147a124c9f1ac073a445f06e0d03cbc85810dc9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11258435694840833"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LSR = Lasso(alpha=0.0005)\n",
    "LSR.fit(X_train, y)\n",
    "return_rmse(LSR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "b67faa1cba802bd9822550584f982ff1cf2f4be5"
   },
   "source": [
    "### 3. ElasticNet\n",
    "\n",
    "Elastic Net uses `both L1 and L2` penalty like it's a `combination of LASSO and Ridge`. It works well on `large datasets`\n",
    "\n",
    "* **alpha** works same as in `Ridge and Lasso` \n",
    "\n",
    "* **l1_ratio** is to control penalty \n",
    "\n",
    "* if it is `0` , it is `L2`\n",
    "\n",
    "* if it is `1`, it is `L1`\n",
    "\n",
    "* if it is `0 < l1_ratio < 1`, it is combination of `L1 and L2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "4c6a6053cd7a9de948eb29e84058a8360e541f82"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11613646463449893"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EN = ElasticNet(alpha=0.01,l1_ratio=0.1)\n",
    "EN.fit(X_train, y)\n",
    "return_rmse(EN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "32145cd2da98b39d841d1574ce0ba492ac26fe78"
   },
   "source": [
    "### Handling Outliers where our model predicted poor results using Z-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "a7c1d1a1a9cddd58e5094e18682e1e033be3d05f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  13,   30,   48,   66,   88,  107,  142,  218,  277,  308,  328,\n",
       "        365,  410,  431,  462,  473,  479,  488,  495,  532,  559,  580,\n",
       "        587,  627,  631,  657,  680,  687,  709,  713,  727,  737,  739,\n",
       "        773,  811,  873,  884,  915,  934,  967,  969, 1060, 1120, 1179,\n",
       "       1209, 1321, 1334, 1429, 1450], dtype=int64)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = RR.predict(X_train)\n",
    "residual = y - y_pred\n",
    "z = np.abs(stats.zscore(residual))\n",
    "outliers1=np.where(abs(z) > abs(z).std() * 3)[0]\n",
    "outliers1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "3d1b9f87bfebcd994e2c40aef77441ca3a1d9397"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  30,   66,   88,  107,  142,  218,  277,  308,  318,  328,  410,\n",
       "        431,  451,  457,  462,  479,  495,  532,  559,  580,  587,  627,\n",
       "        631,  665,  680,  687,  709,  710,  713,  727,  737,  771,  773,\n",
       "        811,  873,  884,  915,  967,  969, 1060, 1120, 1179, 1209, 1321,\n",
       "       1322, 1334, 1380, 1383, 1420, 1429, 1450], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = LSR.predict(X_train)\n",
    "residual = y - y_pred\n",
    "z = np.abs(stats.zscore(residual))\n",
    "outliers2=np.where(abs(z) > abs(z).std() * 3)[0]\n",
    "outliers2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "f546591cd7825a3a36939bc8626ac97b6cc6d170"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  30,   66,   70,   88,   93,  142,  218,  277,  328,  410,  431,\n",
       "        457,  462,  479,  495,  532,  559,  580,  587,  627,  631,  661,\n",
       "        665,  680,  687,  709,  713,  727,  737,  771,  773,  802,  811,\n",
       "        863,  873,  884,  897,  915,  967,  969, 1060, 1066, 1179, 1209,\n",
       "       1321, 1334, 1420, 1429, 1450], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = EN.predict(X_train)\n",
    "residual = y - y_pred\n",
    "z = np.abs(stats.zscore(residual))\n",
    "outliers3=list(np.where(abs(z) > abs(z).std() * 3))[0]\n",
    "outliers3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "8c5c0a52e436e81d43b15ffec972cf863d550ed7"
   },
   "source": [
    "#### Let's drop these outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers = []\n",
    "for i in outliers1:\n",
    "    if (i in outliers2) & (i in outliers3):\n",
    "        outliers.append(i)     \n",
    "\n",
    "train = train.drop(outliers)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train models again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = train[\"SalePrice\"]\n",
    "y = np.log(y+1)\n",
    "\n",
    "X_train = pd.get_dummies(pd.concat((train.drop([\"SalePrice\", \"Id\"], axis=1),\n",
    "                                    test.drop([\"Id\"], axis=1)), axis=0)).iloc[: train.shape[0]]\n",
    "X_test = pd.get_dummies(pd.concat((train.drop([\"SalePrice\", \"Id\"], axis=1),\n",
    "                                   test.drop([\"Id\"], axis=1)), axis=0)).iloc[train.shape[0]:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08827628040537376"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RR= Ridge(alpha=15)\n",
    "RR.fit(X_train, y)\n",
    "return_rmse(RR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08842108837063836"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LSR = Lasso(alpha=0.0004)\n",
    "LSR.fit(X_train, y)\n",
    "return_rmse(LSR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08864352728041995"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EN = ElasticNet(alpha=0.001,l1_ratio=0.5)\n",
    "EN.fit(X_train, y)\n",
    "return_rmse(EN)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our models performance got better!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Combine these models for final prediction on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "predict = 0.4 * RR.predict(X_test) + 0.3 * EN.predict(X_test) + 0.3 * LSR.predict(X_test)\n",
    "\n",
    "predict= np.exp(predict)-1\n",
    "sample_submission= pd.DataFrame({'Id':np.asarray(test.Id), 'SalePrice':predict})\n",
    "sample_submission.to_csv(\"submit.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_uuid": "fad0fe4fdad4f13e9fc65f682d44b7620b513694"
   },
   "source": [
    "#### And its done!"
   ]
  }
 ],
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