house price.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train=pd.read_csv('C:/Users/hp/Downloads/house-prices-advanced-regression-techniques/train.csv')\n",
    "test=pd.read_csv('C:/Users/hp/Downloads/house-prices-advanced-regression-techniques/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1460, 81), (1459, 80))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.shape    ,    test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission=pd.read_csv('C:/Users/hp/Downloads/house-prices-advanced-regression-techniques/sample_submission.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>Predictions_FAIL</th>\n",
       "      <th>Predictions_FURTHER INSPECTION REQUIRED</th>\n",
       "      <th>Predictions_INSPECTION OVERRULED</th>\n",
       "      <th>Predictions_PASS</th>\n",
       "      <th>Predictions_PASS(CONDITIONAL)</th>\n",
       "      <th>Predictions_SHUT-DOWN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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      "text/plain": [
       "   Predictions_FAIL  Predictions_FURTHER INSPECTION REQUIRED  \\\n",
       "0                 0                                        0   \n",
       "1                 0                                        0   \n",
       "2                 0                                        0   \n",
       "3                 0                                        0   \n",
       "4                 1                                        0   \n",
       "\n",
       "   Predictions_INSPECTION OVERRULED  Predictions_PASS  \\\n",
       "0                                 0                 1   \n",
       "1                                 0                 1   \n",
       "2                                 0                 1   \n",
       "3                                 0                 1   \n",
       "4                                 0                 0   \n",
       "\n",
       "   Predictions_PASS(CONDITIONAL)  Predictions_SHUT-DOWN  \n",
       "0                              0                      0  \n",
       "1                              0                      0  \n",
       "2                              0                      0  \n",
       "3                              0                      0  \n",
       "4                              0                      0  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_submission.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=train.drop(['PoolQC','Fence','MiscFeature','Alley','Id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=test.drop(['PoolQC','Fence','MiscFeature','Alley','Id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['LotFrontage']=df_train['LotFrontage'].fillna(df_train['LotFrontage'].mean())\n",
    "df_test['LotFrontage']=df_test['LotFrontage'].fillna(df_test['LotFrontage'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['BsmtCond']=df_train['BsmtCond'].fillna(df_train['BsmtCond'].mode()[0])\n",
    "df_train['BsmtQual']=df_train['BsmtQual'].fillna(df_train['BsmtQual'].mode()[0])\n",
    "df_test['BsmtQual']=df_test['BsmtQual'].fillna(df_test['BsmtQual'].mode()[0])\n",
    "df_test['BsmtCond']=df_test['BsmtCond'].fillna(df_test['BsmtCond'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['FireplaceQu']=df_train['FireplaceQu'].fillna(df_train['FireplaceQu'].mode()[0])\n",
    "df_test['FireplaceQu']=df_test['FireplaceQu'].fillna(df_test['FireplaceQu'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['GarageType']=df_train['GarageType'].fillna(df_train['GarageType'].mode()[0])\n",
    "df_test['GarageType']=df_test['GarageType'].fillna(df_test['GarageType'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['GarageYrBlt']=df_train['GarageYrBlt'].fillna(df_train['GarageYrBlt'].mode()[0])\n",
    "df_test['GarageYrBlt']=df_test['GarageYrBlt'].fillna(df_test['GarageYrBlt'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['GarageFinish']=df_train['GarageFinish'].fillna(df_train['GarageFinish'].mode()[0])\n",
    "df_test['GarageFinish']=df_test['GarageFinish'].fillna(df_test['GarageFinish'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['GarageQual']=df_train['GarageQual'].fillna(df_train['GarageQual'].mode()[0])\n",
    "df_test['GarageQual']=df_test['GarageQual'].fillna(df_test['GarageQual'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['GarageCond']=df_train['GarageCond'].fillna(df_train['GarageCond'].mode()[0])\n",
    "df_test['GarageCond']=df_test['GarageCond'].fillna(df_test['GarageCond'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21086b6bfd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_train.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['MasVnrType']=df_train['MasVnrType'].fillna(df_train['MasVnrType'].mode()[0])\n",
    "df_train['BsmtExposure']=df_train['BsmtExposure'].fillna(df_train['BsmtExposure'].mode()[0])\n",
    "df_train['BsmtFinType1']=df_train['BsmtFinType1'].fillna(df_train['BsmtFinType1'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x210874d4588>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df_test.isnull())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test['MasVnrArea']=df_test['MasVnrArea'].fillna(df_test['MasVnrArea'].mean())\n",
    "df_test['BsmtExposure']=df_test['BsmtExposure'].fillna(df_test['BsmtExposure'].mode()[0])\n",
    "df_test['BsmtFinType1']=df_test['BsmtFinType1'].fillna(df_test['BsmtFinType1'].mode()[0])\n",
    "df_test['BsmtFullBath']=df_test['BsmtFullBath'].fillna(df_test['BsmtFullBath'].mode()[0])\n",
    "df_test['BsmtFinType2']=df_test['BsmtFinType2'].fillna(df_test['BsmtFinType2'].mode()[0])\n",
    "df_test['MasVnrType']=df_test['MasVnrType'].fillna(df_test['MasVnrType'].mode()[0])\n",
    "df_test['BsmtHalfBath']=df_test['BsmtHalfBath'].fillna(df_test['BsmtHalfBath'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1460, 76), (1459, 75))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape    ,    df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=pd.get_dummies(df_train,drop_first=True)\n",
    "df_test=pd.get_dummies(df_test,drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 237)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 220)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df=pd.concat([df_train,df_test],axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>BldgType_2fmCon</th>\n",
       "      <th>BldgType_Duplex</th>\n",
       "      <th>BldgType_Twnhs</th>\n",
       "      <th>BldgType_TwnhsE</th>\n",
       "      <th>BsmtCond_Gd</th>\n",
       "      <th>BsmtCond_Po</th>\n",
       "      <th>...</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>Street_Pave</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Utilities_NoSeWa</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>YrSold</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
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       "      <td>3</td>\n",
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       "      <td>8</td>\n",
       "      <td>856.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
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       "      <td>1262.0</td>\n",
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       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>2007</td>\n",
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       "      <th>2</th>\n",
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       "      <td>3</td>\n",
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       "      <td>920.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2002</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
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       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>756.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1145.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>192</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 237 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   1stFlrSF  2ndFlrSF  3SsnPorch  BedroomAbvGr  BldgType_2fmCon  \\\n",
       "0       856       854          0             3                0   \n",
       "1      1262         0          0             3                0   \n",
       "2       920       866          0             3                0   \n",
       "3       961       756          0             3                0   \n",
       "4      1145      1053          0             4                0   \n",
       "\n",
       "   BldgType_Duplex  BldgType_Twnhs  BldgType_TwnhsE  BsmtCond_Gd  BsmtCond_Po  \\\n",
       "0                0               0                0            0            0   \n",
       "1                0               0                0            0            0   \n",
       "2                0               0                0            0            0   \n",
       "3                0               0                0            1            0   \n",
       "4                0               0                0            0            0   \n",
       "\n",
       "   ...  SaleType_WD  ScreenPorch  Street_Pave  TotRmsAbvGrd  TotalBsmtSF  \\\n",
       "0  ...            1            0            1             8        856.0   \n",
       "1  ...            1            0            1             6       1262.0   \n",
       "2  ...            1            0            1             6        920.0   \n",
       "3  ...            1            0            1             7        756.0   \n",
       "4  ...            1            0            1             9       1145.0   \n",
       "\n",
       "   Utilities_NoSeWa  WoodDeckSF  YearBuilt  YearRemodAdd  YrSold  \n",
       "0               0.0           0       2003          2003    2008  \n",
       "1               0.0         298       1976          1976    2007  \n",
       "2               0.0           0       2001          2002    2008  \n",
       "3               0.0           0       1915          1970    2006  \n",
       "4               0.0         192       2000          2000    2008  \n",
       "\n",
       "[5 rows x 237 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.loc[:,~df_test.columns.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 220)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train=df.iloc[:1460,:]\n",
    "df_test=df.iloc[1460:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1460, 220), (1459, 220))"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape    ,    df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\pandas\\core\\frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "df_test.drop(['SalePrice'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train=df_train['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=df_train.drop(['SalePrice'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb=XGBRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\xgboost\\core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[12:30:44] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "       importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_pred=xgb.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9602026736042394"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb=xgboost.XGBRegressor(base_score=0.75, booster='gbtree', colsample_bylevel=1,\n",
    "       colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
    "       importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
    "       max_depth=2, min_child_weight=1, missing=None, n_estimators=1500,\n",
    "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
    "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
    "       silent=None, subsample=1, verbosity=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "booster=['gbtree','gblinear']\n",
    "base_score=[0.25,0.5,0.75,1]\n",
    "n_estimators=[100,500,900,1100,1500]\n",
    "max_depth=[2,3,5,10,15]\n",
    "learning_rate=[0.05,0.1,0.15,0.20]\n",
    "min_child_weight=[1,2,3,4]\n",
    "\n",
    "# define the grid of hyperparameters to search\n",
    "\n",
    "hyperparameter_grid={\n",
    "    'n_estimators':n_estimators,\n",
    "    'base_score':base_score,\n",
    "    'booster':booster,\n",
    "    'max_depth':max_depth,\n",
    "    'learning_rate':learning_rate,\n",
    "    'min_child_weight':min_child_weight\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_cv=RandomizedSearchCV(estimator=xgb,\n",
    "                            param_distributions=hyperparameter_grid,\n",
    "                            cv=10,n_iter=500,\n",
    "                            scoring='neg_mean_absolute_error',n_jobs=4,\n",
    "                            verbose=5,\n",
    "                            return_train_score=True,\n",
    "                            random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 500 candidates, totalling 5000 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n",
      "[Parallel(n_jobs=4)]: Done  10 tasks      | elapsed:   43.3s\n",
      "[Parallel(n_jobs=4)]: Done  64 tasks      | elapsed:  7.5min\n",
      "[Parallel(n_jobs=4)]: Done 154 tasks      | elapsed: 21.4min\n",
      "[Parallel(n_jobs=4)]: Done 280 tasks      | elapsed: 35.3min\n",
      "[Parallel(n_jobs=4)]: Done 442 tasks      | elapsed: 52.8min\n",
      "[Parallel(n_jobs=4)]: Done 640 tasks      | elapsed: 64.9min\n",
      "[Parallel(n_jobs=4)]: Done 874 tasks      | elapsed: 103.5min\n",
      "[Parallel(n_jobs=4)]: Done 1144 tasks      | elapsed: 144.7min\n",
      "[Parallel(n_jobs=4)]: Done 1450 tasks      | elapsed: 180.3min\n",
      "[Parallel(n_jobs=4)]: Done 1792 tasks      | elapsed: 226.0min\n",
      "[Parallel(n_jobs=4)]: Done 2170 tasks      | elapsed: 270.1min\n",
      "[Parallel(n_jobs=4)]: Done 2584 tasks      | elapsed: 319.3min\n",
      "[Parallel(n_jobs=4)]: Done 3034 tasks      | elapsed: 350.4min\n",
      "[Parallel(n_jobs=4)]: Done 3520 tasks      | elapsed: 393.0min\n",
      "[Parallel(n_jobs=4)]: Done 4042 tasks      | elapsed: 442.4min\n",
      "[Parallel(n_jobs=4)]: Done 4600 tasks      | elapsed: 479.9min\n",
      "[Parallel(n_jobs=4)]: Done 5000 out of 5000 | elapsed: 512.7min finished\n",
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\xgboost\\core.py:587: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  if getattr(data, 'base', None) is not None and \\\n",
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\xgboost\\core.py:588: FutureWarning: Series.base is deprecated and will be removed in a future version\n",
      "  data.base is not None and isinstance(data, np.ndarray) \\\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:24:32] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10, error_score='raise-deprecating',\n",
       "          estimator=XGBRegressor(base_score=0.75, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "       importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=2, min_child_weight=1, missing=None, n_estimators=1500,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=None, subsample=1, verbosity=1),\n",
       "          fit_params=None, iid='warn', n_iter=500, n_jobs=4,\n",
       "          param_distributions={'n_estimators': [100, 500, 900, 1100, 1500], 'base_score': [0.25, 0.5, 0.75, 1], 'booster': ['gbtree', 'gblinear'], 'max_depth': [2, 3, 5, 10, 15], 'learning_rate': [0.05, 0.1, 0.15, 0.2], 'min_child_weight': [1, 2, 3, 4]},\n",
       "          pre_dispatch='2*n_jobs', random_state=42, refit=True,\n",
       "          return_train_score=True, scoring='neg_mean_absolute_error',\n",
       "          verbose=5)"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_cv.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.25, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "       importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=2, min_child_weight=1, missing=None, n_estimators=900,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_cv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[18:24:54] WARNING: src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.75, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
       "       importance_type='gain', learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=2, min_child_weight=1, missing=None, n_estimators=1500,\n",
       "       n_jobs=1, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=None, subsample=1, verbosity=1)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "xgb_pred=xgb.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9896839706909206"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.score(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission['SalePrice']=xgb_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission.to_csv('C:/Users/hp/Downloads/house-prices-advanced-regression-techniques/sample_submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense,LeakyReLU,PReLU,ELU\n",
    "from keras.layers import Dropout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as k\n",
    "def root_mean_squared_error(df_true,df_pred):\n",
    "    return k.sqrt(k.mean(k.square(df_pred-df_true)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", input_dim=219, units=50, kernel_initializer=\"he_uniform\")`\n",
      "  \"\"\"\n",
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\ipykernel_launcher.py:8: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=25, kernel_initializer=\"he_uniform\")`\n",
      "  \n",
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\ipykernel_launcher.py:11: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(activation=\"relu\", units=50, kernel_initializer=\"he_uniform\")`\n",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\ipykernel_launcher.py:13: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(units=1, kernel_initializer=\"he_uniform\")`\n",
      "  del sys.path[0]\n"
     ]
    }
   ],
   "source": [
    "# Initialising the ANN\n",
    "classifier = Sequential()\n",
    "\n",
    "# Adding the input layer and the first hidden layer\n",
    "classifier.add(Dense(output_dim = 50, init = 'he_uniform',activation='relu',input_dim = 219))\n",
    "\n",
    "# Adding the second hidden layer\n",
    "classifier.add(Dense(output_dim = 25, init = 'he_uniform',activation='relu'))\n",
    "\n",
    "# Adding the third hidden layer\n",
    "classifier.add(Dense(output_dim = 50, init = 'he_uniform',activation='relu'))\n",
    "# Adding the output layer\n",
    "classifier.add(Dense(output_dim = 1, init = 'he_uniform'))\n",
    "\n",
    "# Compiling the ANN\n",
    "classifier.compile(loss=root_mean_squared_error, optimizer='Adamax')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\hp\\Anaconda3\\3.7\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 1168 samples, validate on 292 samples\n",
      "Epoch 1/100\n",
      "1168/1168 [==============================] - 3s 3ms/step - loss: nan - val_loss: nan\n",
      "Epoch 2/100\n",
      "1168/1168 [==============================] - 0s 360us/step - loss: nan - val_loss: nan\n",
      "Epoch 3/100\n",
      "1168/1168 [==============================] - 1s 485us/step - loss: nan - val_loss: nan\n",
      "Epoch 4/100\n",
      "1168/1168 [==============================] - 0s 415us/step - loss: nan - val_loss: nan\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "# Fitting the ANN to the Training set\n",
    "model_history=classifier.fit(x_train, y_train,validation_split=0.20, batch_size = 10, epochs = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "classifier_pred=classifier.predict(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission['SalePrice']=classifier_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_submission.to_csv('C:/Users/hp/Downloads/house-prices-advanced-regression-techniques/sample_submission.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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