initial commit

preprocessing.py

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+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import OneHotEncoder
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_absolute_error
+
+# Preprocessing for numerical data
+numerical_transformer = SimpleImputer(strategy='constant')
+
+# Preprocessing for categorical data
+categorical_transformer = Pipeline(steps=[
+    ('imputer', SimpleImputer(strategy='most_frequent')),
+    ('onehot', OneHotEncoder(handle_unknown='ignore'))
+])
+
+# Bundle preprocessing for numerical and categorical data
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', numerical_transformer, numerical_cols),
+        ('cat', categorical_transformer, categorical_cols)
+    ])
+
+# Define model
+model = RandomForestRegressor(n_estimators=100, random_state=0)
+
+# Bundle preprocessing and modeling code in a pipeline
+clf = Pipeline(steps=[('preprocessor', preprocessor),
+                      ('model', model)
+                     ])
+
+# Preprocessing of training data, fit model 
+clf.fit(X_train, y_train)
+
+# Preprocessing of validation data, get predictions
+preds = clf.predict(X_valid)
+
+print('MAE:', mean_absolute_error(y_valid, preds))