Kaggle competition , my profile https://www.kaggle.com/ouassini

Real-Estate_prediction_model-2.py

import pandas as pd
from sklearn.model_selection import train_test_split
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
from xgboost import XGBRegressor

# Read the data
X_full = pd.read_csv('train.csv', index_col='Id')
X_test_full = pd.read_csv('test.csv', index_col='Id')

# Remove rows with missing target, separate target from predictors
X_full.dropna(axis=0, subset=['SalePrice'], inplace=True)
y = X_full.SalePrice
X_full.drop(['SalePrice'], axis=1, inplace=True)

# Break off validation set from training data
X_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)

#cols_with_missing = ['Alley','PoolQC','MiscFeature','Fence','FireplaceQu','LotFrontage']
cols_with_missing = [col for col in X_train_full.columns
                     if X_train_full[col].isnull().any()]

print(X_train_full.shape)
missing_val_count_by_column = (X_train_full.isnull().sum())
print(missing_val_count_by_column[missing_val_count_by_column > 0])



# Drop columns in training and validation data
X_train_full = X_train_full.drop(cols_with_missing, axis=1)
X_valid_full = X_valid_full.drop(cols_with_missing, axis=1)
X_test_full  = X_test_full.drop(cols_with_missing, axis=1)

missing_val_count_by_column = (X_train_full.isnull().sum())
print(missing_val_count_by_column[missing_val_count_by_column > 0])



# "Cardinality" means the number of unique values in a column
# Select categorical columns with relatively low cardinality (convenient but arbitrary)
categorical_cols = [cname for cname in X_train_full.columns if
                    X_train_full[cname].nunique() < 10 and
                    X_train_full[cname].dtype == "object"]



# Select numerical columns
numerical_cols = [cname for cname in X_train_full.columns if
                X_train_full[cname].dtype in ['int64', 'float64']]





# Keep selected columns only
my_cols = categorical_cols + numerical_cols
X_train = X_train_full[my_cols].copy()
X_valid = X_valid_full[my_cols].copy()
X_test = X_test_full[my_cols].copy()



# Preprocessing for numerical data
numerical_transformer = SimpleImputer(strategy='median')

# Preprocessing for categorical data
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant')),
    ('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 = XGBRegressor(n_estimators=2000, learning_rate=0.02, n_jobs=4)

# Bundle preprocessing and modeling code in a pipeline
MyPip = Pipeline(steps=[('preprocessor', preprocessor),
                     ('model', model)
                    ])
#
#  Preprocessing of training data, fit model
MyPip.fit(X_train, y_train)


# Preprocessing of validation data, get predictions
preds = MyPip.predict(X_valid)
# preds = MyPip.predict(X_test)

print('MAE:', mean_absolute_error(y_valid, preds))
#
# # Save test predictions to file
#  output = pd.DataFrame({'Id': X_test.index,
#                        'SalePrice': preds})
# output.to_csv('submission.csv', index=False)