house-price-prediction-289.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-09-14T08:10:34.893337Z","iopub.execute_input":"2021-09-14T08:10:34.893676Z","iopub.status.idle":"2021-09-14T08:10:34.905012Z","shell.execute_reply.started":"2021-09-14T08:10:34.893642Z","shell.execute_reply":"2021-09-14T08:10:34.904176Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Importing Dataset","metadata":{}},{"cell_type":"code","source":"X_full = pd.read_csv('../input/home-data-for-ml-course/train.csv', index_col='Id')\nX_test_full = pd.read_csv('../input/home-data-for-ml-course/test.csv', index_col='Id')","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:10:34.930264Z","iopub.execute_input":"2021-09-14T08:10:34.930503Z","iopub.status.idle":"2021-09-14T08:10:34.987004Z","shell.execute_reply.started":"2021-09-14T08:10:34.930476Z","shell.execute_reply":"2021-09-14T08:10:34.986125Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Dropping samples with missing target and dropping Target variable from training dataset","metadata":{}},{"cell_type":"code","source":"X_full.dropna(axis=0,subset=['SalePrice'],inplace=True)\ny=X_full['SalePrice']\nX_full.drop(['SalePrice'],axis=1,inplace=True)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:10:34.990838Z","iopub.execute_input":"2021-09-14T08:10:34.991071Z","iopub.status.idle":"2021-09-14T08:10:35.001023Z","shell.execute_reply.started":"2021-09-14T08:10:34.991043Z","shell.execute_reply":"2021-09-14T08:10:35.000175Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Splitting dataset into training and validation set","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train_full,X_valid_full,y_train,y_valid=train_test_split(X_full,y,train_size=0.8,test_size=0.2,random_state=0)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:10:35.031251Z","iopub.execute_input":"2021-09-14T08:10:35.03152Z","iopub.status.idle":"2021-09-14T08:10:35.041412Z","shell.execute_reply.started":"2021-09-14T08:10:35.03149Z","shell.execute_reply":"2021-09-14T08:10:35.040867Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Select categorical columns with relatively low cardinality (convenient but arbitrary)","metadata":{}},{"cell_type":"code","source":"cat_cols=[c for c in X_train_full if X_train_full[c].nunique()<10 and X_train_full[c].dtype=='object']","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:10:35.064726Z","iopub.execute_input":"2021-09-14T08:10:35.065516Z","iopub.status.idle":"2021-09-14T08:10:35.086614Z","shell.execute_reply.started":"2021-09-14T08:10:35.065471Z","shell.execute_reply":"2021-09-14T08:10:35.085913Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Select numerical columns","metadata":{}},{"cell_type":"code","source":"num_cols=[c for c in X_train_full if X_train_full[c].dtype in ['int64','float64']]","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:10:35.101914Z","iopub.execute_input":"2021-09-14T08:10:35.102126Z","iopub.status.idle":"2021-09-14T08:10:35.106923Z","shell.execute_reply.started":"2021-09-14T08:10:35.102102Z","shell.execute_reply":"2021-09-14T08:10:35.106154Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"my_cols=cat_cols+num_cols\nX_train=X_train_full[my_cols].copy()\nX_valid=X_valid_full[my_cols].copy()\nX_test=X_test_full[my_cols].copy()","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:10:35.136845Z","iopub.execute_input":"2021-09-14T08:10:35.137067Z","iopub.status.idle":"2021-09-14T08:10:35.149366Z","shell.execute_reply.started":"2021-09-14T08:10:35.137042Z","shell.execute_reply":"2021-09-14T08:10:35.148627Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Preprocessing","metadata":{}},{"cell_type":"code","source":"from sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.pipeline import Pipeline\n\nnum_trans=SimpleImputer(strategy='median')\ncat_trans=Pipeline(steps=[\n    ('imp',SimpleImputer(strategy='most_frequent')),\n    ('ohe',OneHotEncoder(handle_unknown='ignore'))\n])\npreprocesssor=ColumnTransformer(transformers=[\n    ('numerical',num_trans,num_cols),\n    ('categorical',cat_trans,cat_cols)\n])\n","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:10:35.171789Z","iopub.execute_input":"2021-09-14T08:10:35.172411Z","iopub.status.idle":"2021-09-14T08:10:35.17878Z","shell.execute_reply.started":"2021-09-14T08:10:35.172371Z","shell.execute_reply":"2021-09-14T08:10:35.177795Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Hyperparameter Tuning","metadata":{}},{"cell_type":"code","source":"from catboost import CatBoostRegressor\nfrom xgboost import XGBRegressor","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:10:35.222393Z","iopub.execute_input":"2021-09-14T08:10:35.222837Z","iopub.status.idle":"2021-09-14T08:10:35.226744Z","shell.execute_reply.started":"2021-09-14T08:10:35.222803Z","shell.execute_reply":"2021-09-14T08:10:35.225818Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.model_selection import RandomizedSearchCV\n\npipeline1=Pipeline(steps=[\n    ('preprocessor',preprocesssor),\n    ('model',XGBRegressor())\n])\nparam_grid={\n        'model__n_estimators':np.arange(300,1500,100),\n        'model__max_depth':np.arange(3,10),\n        'model__learning_rate':[0.05,0.1,0.15]\n    }\n   \n        \n    \n    \n\n\ngbmodel=RandomizedSearchCV(\n    estimator=pipeline1,\n    param_distributions=param_grid,\n    n_iter=10,\n    verbose=10,\n    n_jobs=1,\n    cv=5\n)\n\ngbmodel.fit(X_train,y_train)\n","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:10:35.273936Z","iopub.execute_input":"2021-09-14T08:10:35.274724Z","iopub.status.idle":"2021-09-14T08:11:22.452513Z","shell.execute_reply.started":"2021-09-14T08:10:35.274679Z","shell.execute_reply":"2021-09-14T08:11:22.449368Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"gbmodel.best_params_","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:11:22.453992Z","iopub.status.idle":"2021-09-14T08:11:22.454361Z","shell.execute_reply.started":"2021-09-14T08:11:22.454192Z","shell.execute_reply":"2021-09-14T08:11:22.45421Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.ensemble import GradientBoostingRegressor\npipeline2=Pipeline(steps=[\n    ('preprocessor',preprocesssor),\n    ('model',GradientBoostingRegressor())\n])\nparam_grid={\n        'model__n_estimators':np.arange(300,1000,100),\n        'model__max_depth':np.arange(3,10),\n        'model__learning_rate':[0.05,0.1]\n    }\n   \n        \n    \n    \n\nmodel=RandomizedSearchCV(\n    estimator=pipeline2,\n    param_distributions=param_grid,\n    n_iter=10,\n    verbose=10,\n    n_jobs=1,\n    cv=5\n)\n\nmodel.fit(X_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:11:22.455594Z","iopub.status.idle":"2021-09-14T08:11:22.456043Z","shell.execute_reply.started":"2021-09-14T08:11:22.455809Z","shell.execute_reply":"2021-09-14T08:11:22.455831Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.best_params_","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:11:22.457319Z","iopub.status.idle":"2021-09-14T08:11:22.457751Z","shell.execute_reply.started":"2021-09-14T08:11:22.457507Z","shell.execute_reply":"2021-09-14T08:11:22.457529Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pipeline3=Pipeline(steps=[\n    ('preprocessor',preprocesssor),\n    ('model',CatBoostRegressor())\n])\nparam_grid={\n        'model__n_estimators':[300,500,700,1000],\n        'model__max_depth':np.arange(3,10,2),\n        'model__learning_rate':[0.05,0.1]\n    }   \n\nmodel2=RandomizedSearchCV(\n    estimator=pipeline3,\n    param_distributions=param_grid,\n    n_iter=10,\n    verbose=10,\n    n_jobs=1,\n    cv=5\n)\n\nmodel2.fit(X_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:11:22.459029Z","iopub.status.idle":"2021-09-14T08:11:22.459504Z","shell.execute_reply.started":"2021-09-14T08:11:22.459238Z","shell.execute_reply":"2021-09-14T08:11:22.459262Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model2.best_params_","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:11:22.460628Z","iopub.status.idle":"2021-09-14T08:11:22.461049Z","shell.execute_reply.started":"2021-09-14T08:11:22.460818Z","shell.execute_reply":"2021-09-14T08:11:22.460838Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Random Forest","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestRegressor\nrf=RandomForestRegressor(n_estimators=700,random_state=42)\nrf_pipeline=Pipeline(steps=[\n    ('preprocessor',preprocesssor),\n    ('rf',rf)\n])","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:11:42.820579Z","iopub.execute_input":"2021-09-14T08:11:42.821084Z","iopub.status.idle":"2021-09-14T08:11:42.827992Z","shell.execute_reply.started":"2021-09-14T08:11:42.821046Z","shell.execute_reply":"2021-09-14T08:11:42.827472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Catboost","metadata":{}},{"cell_type":"code","source":"cb=GradientBoostingRegressor(n_estimators=300,max_depth=7,learning_rate=0.1)\ncb_pipeline=Pipeline(steps=[\n    ('preprocesssor',preprocesssor),\n    ('cb',cb)\n])","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:11:46.38809Z","iopub.execute_input":"2021-09-14T08:11:46.388637Z","iopub.status.idle":"2021-09-14T08:11:46.393183Z","shell.execute_reply.started":"2021-09-14T08:11:46.388574Z","shell.execute_reply":"2021-09-14T08:11:46.392297Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cb_pipeline.fit(X_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:11:49.299741Z","iopub.execute_input":"2021-09-14T08:11:49.300589Z","iopub.status.idle":"2021-09-14T08:11:55.349316Z","shell.execute_reply.started":"2021-09-14T08:11:49.30055Z","shell.execute_reply":"2021-09-14T08:11:55.348693Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cb_pred=cb_pipeline.predict(X_valid)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:11:59.610122Z","iopub.execute_input":"2021-09-14T08:11:59.61071Z","iopub.status.idle":"2021-09-14T08:11:59.633444Z","shell.execute_reply.started":"2021-09-14T08:11:59.610675Z","shell.execute_reply":"2021-09-14T08:11:59.632947Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Gradient Boosting","metadata":{}},{"cell_type":"code","source":"\ngb=GradientBoostingRegressor(n_estimators=900,max_depth=4,learning_rate=0.1)\ngb_pipeline=Pipeline(steps=[\n    ('preprocesssor',preprocesssor),\n    ('gb',gb)\n])","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:12:03.196818Z","iopub.execute_input":"2021-09-14T08:12:03.197092Z","iopub.status.idle":"2021-09-14T08:12:03.201234Z","shell.execute_reply.started":"2021-09-14T08:12:03.197064Z","shell.execute_reply":"2021-09-14T08:12:03.200562Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"rf_pipeline.fit(X_train,y_train)\ngb_pipeline.fit(X_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:12:09.554979Z","iopub.execute_input":"2021-09-14T08:12:09.555368Z","iopub.status.idle":"2021-09-14T08:12:37.30992Z","shell.execute_reply.started":"2021-09-14T08:12:09.55533Z","shell.execute_reply":"2021-09-14T08:12:37.309008Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Random forest + Gradient Boosting","metadata":{}},{"cell_type":"code","source":"rf_pred=rf_pipeline.predict(X_valid)\ngb_pred=gb_pipeline.predict(X_valid)\nrfgb=(rf_pred+gb_pred)/2","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:12:43.124745Z","iopub.execute_input":"2021-09-14T08:12:43.125022Z","iopub.status.idle":"2021-09-14T08:12:43.253777Z","shell.execute_reply.started":"2021-09-14T08:12:43.124994Z","shell.execute_reply":"2021-09-14T08:12:43.25305Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_error\nimport numpy as np\nrfgb_error=np.sqrt(mean_squared_error(y_valid,rfgb))\nprint(rfgb_error)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:12:45.476328Z","iopub.execute_input":"2021-09-14T08:12:45.47657Z","iopub.status.idle":"2021-09-14T08:12:45.483081Z","shell.execute_reply.started":"2021-09-14T08:12:45.476546Z","shell.execute_reply":"2021-09-14T08:12:45.482034Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Catboost+Gradient boost","metadata":{}},{"cell_type":"code","source":"cbgb=(cb_pred+gb_pred)/2\ncbgb_error=np.sqrt(mean_squared_error(y_valid,cbgb))\nprint(cbgb_error)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:12:50.860216Z","iopub.execute_input":"2021-09-14T08:12:50.860497Z","iopub.status.idle":"2021-09-14T08:12:50.865053Z","shell.execute_reply.started":"2021-09-14T08:12:50.860458Z","shell.execute_reply":"2021-09-14T08:12:50.864534Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# GB+RF+CB","metadata":{}},{"cell_type":"code","source":"p3=(gb_pred+rf_pred+cb_pred)/3\ncxb_error=np.sqrt(mean_squared_error(y_valid,p3))\nprint(cxb_error)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:12:56.661676Z","iopub.execute_input":"2021-09-14T08:12:56.662072Z","iopub.status.idle":"2021-09-14T08:12:56.667481Z","shell.execute_reply.started":"2021-09-14T08:12:56.662044Z","shell.execute_reply":"2021-09-14T08:12:56.666682Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# XGBoost","metadata":{}},{"cell_type":"code","source":"from xgboost import XGBRegressor\nxgb = XGBRegressor(n_estimators=1400,max_depth=7,learning_rate=0.05, n_jobs=4)\nxgb_pipeline=Pipeline(steps=[\n    ('preprocesssor',preprocesssor),\n    ('xgb',xgb)\n])\n","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:12:59.868885Z","iopub.execute_input":"2021-09-14T08:12:59.869196Z","iopub.status.idle":"2021-09-14T08:12:59.873879Z","shell.execute_reply.started":"2021-09-14T08:12:59.869168Z","shell.execute_reply":"2021-09-14T08:12:59.873306Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**The problem is that pipelines do not fit eval_set. So, as you said, you need to preprocess X_valid. To do that the easiest way is using your pipeline without the 'model' step. Use the following code before fitting your pipeline:**","metadata":{}},{"cell_type":"code","source":"# Make a copy to avoid changing original data\nX_valid_eval=X_valid.copy()\n# Remove the model from pipeline\neval_set_pipe = Pipeline(steps = [('preprocesssor', preprocesssor)])\n# fit transform X_valid.copy()\nX_valid_eval = eval_set_pipe.fit(X_train, y_train).transform (X_valid_eval)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:13:02.956141Z","iopub.execute_input":"2021-09-14T08:13:02.956721Z","iopub.status.idle":"2021-09-14T08:13:03.057788Z","shell.execute_reply.started":"2021-09-14T08:13:02.956687Z","shell.execute_reply":"2021-09-14T08:13:03.057138Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"xgb_pipeline.fit(X_train, y_train, \n             xgb__early_stopping_rounds=5, \n             xgb__eval_set=[(X_valid_eval, y_valid)],xgb__verbose=False \n             )   ","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:13:06.436374Z","iopub.execute_input":"2021-09-14T08:13:06.436748Z","iopub.status.idle":"2021-09-14T08:13:08.095058Z","shell.execute_reply.started":"2021-09-14T08:13:06.436715Z","shell.execute_reply":"2021-09-14T08:13:08.094032Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# GB+XGB","metadata":{}},{"cell_type":"code","source":"xgb_pred=xgb_pipeline.predict(X_valid)\ngbx=(gb_pred+xgb_pred)/2\ngbx_error=np.sqrt(mean_squared_error(y_valid,gbx))\nprint(gbx_error)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:13:15.107679Z","iopub.execute_input":"2021-09-14T08:13:15.108468Z","iopub.status.idle":"2021-09-14T08:13:15.141878Z","shell.execute_reply.started":"2021-09-14T08:13:15.108431Z","shell.execute_reply":"2021-09-14T08:13:15.141245Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# CB+XGB","metadata":{}},{"cell_type":"code","source":"cxb=(cb_pred+xgb_pred)/2\ncxb_error=np.sqrt(mean_squared_error(y_valid,cxb))\nprint(cxb_error)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:13:29.925791Z","iopub.execute_input":"2021-09-14T08:13:29.926076Z","iopub.status.idle":"2021-09-14T08:13:29.931826Z","shell.execute_reply.started":"2021-09-14T08:13:29.926049Z","shell.execute_reply":"2021-09-14T08:13:29.931232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# GB+XGB+CB","metadata":{}},{"cell_type":"code","source":"p3=(gb_pred+xgb_pred+cb_pred)/3\ncxb_error=np.sqrt(mean_squared_error(y_valid,p3))\nprint(cxb_error)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:13:32.333932Z","iopub.execute_input":"2021-09-14T08:13:32.334644Z","iopub.status.idle":"2021-09-14T08:13:32.340597Z","shell.execute_reply.started":"2021-09-14T08:13:32.334579Z","shell.execute_reply":"2021-09-14T08:13:32.340042Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# GB+RF+CB","metadata":{}},{"cell_type":"code","source":"p3=(gb_pred+rf_pred+cb_pred)/3\ncxb_error=np.sqrt(mean_squared_error(y_valid,p3))\nprint(cxb_error)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:13:34.781784Z","iopub.execute_input":"2021-09-14T08:13:34.782875Z","iopub.status.idle":"2021-09-14T08:13:34.789451Z","shell.execute_reply.started":"2021-09-14T08:13:34.782822Z","shell.execute_reply":"2021-09-14T08:13:34.788705Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# GB+CB+XGB+RF","metadata":{}},{"cell_type":"code","source":"p4=(gb_pred+xgb_pred+cb_pred+rf_pred)/4\ncxb_error=np.sqrt(mean_squared_error(y_valid,p4))\nprint(cxb_error)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:13:37.093198Z","iopub.execute_input":"2021-09-14T08:13:37.093496Z","iopub.status.idle":"2021-09-14T08:13:37.100336Z","shell.execute_reply.started":"2021-09-14T08:13:37.093458Z","shell.execute_reply":"2021-09-14T08:13:37.099426Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred1=xgb_pipeline.predict(X_test)\npred2=gb_pipeline.predict(X_test)\npred=(pred1+pred2)/2","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:14:05.063031Z","iopub.execute_input":"2021-09-14T08:14:05.063855Z","iopub.status.idle":"2021-09-14T08:14:05.17917Z","shell.execute_reply.started":"2021-09-14T08:14:05.063817Z","shell.execute_reply":"2021-09-14T08:14:05.178428Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Save test predictions to file\noutput = pd.DataFrame({'Id': X_test.index,\n                       'SalePrice': pred})\noutput.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2021-09-14T08:14:08.102029Z","iopub.execute_input":"2021-09-14T08:14:08.102822Z","iopub.status.idle":"2021-09-14T08:14:08.114217Z","shell.execute_reply.started":"2021-09-14T08:14:08.102777Z","shell.execute_reply":"2021-09-14T08:14:08.113505Z"},"trusted":true},"execution_count":null,"outputs":[]}]}