learn1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.impute import SimpleImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data_path = 'datasets/train.csv'\n",
    "test_data_path = 'datasets/test.csv'\n",
    "train_data = pd.read_csv(train_data_path)\n",
    "test_data = pd.read_csv(test_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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(train_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = train_data.SalePrice\n",
    "test_bkp = test_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.drop(['Id','SalePrice'],axis=1)\n",
    "test_data = test_data.drop(['Id'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass         int64\n",
       "MSZoning          object\n",
       "LotFrontage      float64\n",
       "LotArea            int64\n",
       "Street            object\n",
       "Alley             object\n",
       "LotShape          object\n",
       "LandContour       object\n",
       "Utilities         object\n",
       "LotConfig         object\n",
       "LandSlope         object\n",
       "Neighborhood      object\n",
       "Condition1        object\n",
       "Condition2        object\n",
       "BldgType          object\n",
       "HouseStyle        object\n",
       "OverallQual        int64\n",
       "OverallCond        int64\n",
       "YearBuilt          int64\n",
       "YearRemodAdd       int64\n",
       "RoofStyle         object\n",
       "RoofMatl          object\n",
       "Exterior1st       object\n",
       "Exterior2nd       object\n",
       "MasVnrType        object\n",
       "MasVnrArea       float64\n",
       "ExterQual         object\n",
       "ExterCond         object\n",
       "Foundation        object\n",
       "BsmtQual          object\n",
       "                  ...   \n",
       "HalfBath           int64\n",
       "BedroomAbvGr       int64\n",
       "KitchenAbvGr       int64\n",
       "KitchenQual       object\n",
       "TotRmsAbvGrd       int64\n",
       "Functional        object\n",
       "Fireplaces         int64\n",
       "FireplaceQu       object\n",
       "GarageType        object\n",
       "GarageYrBlt      float64\n",
       "GarageFinish      object\n",
       "GarageCars         int64\n",
       "GarageArea         int64\n",
       "GarageQual        object\n",
       "GarageCond        object\n",
       "PavedDrive        object\n",
       "WoodDeckSF         int64\n",
       "OpenPorchSF        int64\n",
       "EnclosedPorch      int64\n",
       "3SsnPorch          int64\n",
       "ScreenPorch        int64\n",
       "PoolArea           int64\n",
       "PoolQC            object\n",
       "Fence             object\n",
       "MiscFeature       object\n",
       "MiscVal            int64\n",
       "MoSold             int64\n",
       "YrSold             int64\n",
       "SaleType          object\n",
       "SaleCondition     object\n",
       "Length: 79, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Alley', 'MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Electrical', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature']\n"
     ]
    }
   ],
   "source": [
    "cols_with_missing = [col for col in train_data.columns if train_data[col].isnull().any() and train_data[col].dtype=='object']\n",
    "print(cols_with_missing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.drop(cols_with_missing,axis=1)\n",
    "test_data = test_data.drop(cols_with_missing,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'ExterQual', 'ExterCond', 'Foundation', 'Heating', 'HeatingQC', 'CentralAir', 'KitchenQual', 'Functional', 'PavedDrive', 'SaleType', 'SaleCondition']\n"
     ]
    }
   ],
   "source": [
    "low_cardinality_cols = [col for col in test_data.columns if test_data[col].dtype=='object']\n",
    "print(low_cardinality_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n"
     ]
    }
   ],
   "source": [
    "numeric_cols = [col for col in test_data.columns if test_data[col].dtype in ['int64','float64']]\n",
    "print(numeric_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_cols = low_cardinality_cols+numeric_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_predictors = train_data[my_cols]\n",
    "test_predictors = test_data[my_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BsmtHalfBath       int64\n",
       "BsmtFinSF2         int64\n",
       "MasVnrArea       float64\n",
       "HalfBath           int64\n",
       "LandSlope         object\n",
       "ExterCond         object\n",
       "GarageArea         int64\n",
       "Neighborhood      object\n",
       "GrLivArea          int64\n",
       "EnclosedPorch      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_predictors.dtypes.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "one_hot_encoded_training_predictors = pd.get_dummies(train_predictors)\n",
    "one_hot_encoded_test_predictors = pd.get_dummies(test_predictors)\n",
    "final_train, final_test = one_hot_encoded_training_predictors.align(one_hot_encoded_test_predictors,join='left',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 218)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 218)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_imputer = SimpleImputer()\n",
    "train_data = my_imputer.fit_transform(final_train)\n",
    "test_data = my_imputer.transform(final_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(train_data))\n",
    "print(type(final_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 218)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1459, 218)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#train_X1, test_X1, train_y1, test_y1 = train_test_split(train_data, train_y, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.05, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=115,\n",
       "       n_jobs=8, nthread=None, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xg_model = XGBRegressor(n_estimators=115,learning_rate=0.05,n_jobs=8)\n",
    "#xg_model.fit(train_data,train_y,early_stopping_rounds=5,eval_set=[(test_X1,test_y1)],verbose=False)\n",
    "xg_model.fit(train_data,train_y,verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_preds = xg_model.predict(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame({'Id': test_bkp.Id,'SalePrice': test_preds})\n",
    "output.to_csv('submission4.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pred = xg_model.predict(test_X1)\n",
    "#mean_absolute_error(test_y1,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pred = xg_model.predict(test_X1)\n",
    "#mean_absolute_error(test_y1,pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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