Upload train.py

The script I used to train my dataset.

train.py

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+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error
+from joblib import dump
+
+# Load the dataset
+df = pd.read_csv('cleaned_housesTRAIN.csv')
+
+# Apply label encoding to 'Area' and 'Suburb'
+le_area = LabelEncoder()
+df['Area'] = le_area.fit_transform(df['Area'])
+
+le_suburb = LabelEncoder()
+df['Suburb'] = le_suburb.fit_transform(df['Suburb'])
+
+# Save the label encoders
+dump(le_area, 'le_area.joblib')
+dump(le_suburb, 'le_suburb.joblib')
+
+# Shuffle the dataframe
+df = df.sample(frac=1)
+
+# Split the data into features (X) and target (y)
+X = df.drop('Rent', axis=1)
+y = df['Rent']
+
+# Split the data into training and test sets (90/10 split)
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
+
+# Create a Gradient Boosting regressor with specified hyperparameters
+gb = GradientBoostingRegressor(n_estimators=850, learning_rate=0.195, max_depth=7, random_state=42)
+
+# Train the model
+gb.fit(X_train, y_train)
+
+# Make predictions on the test set
+y_pred = gb.predict(X_test)
+
+# Calculate MAE, MSE, and R2
+mae = mean_absolute_error(y_test, y_pred)
+mse = mean_squared_error(y_test, y_pred)
+r2 = r2_score(y_test, y_pred)
+medae = median_absolute_error(y_test, y_pred)
+
+print(f"MAE: {mae}, MSE: {mse}, R2: {r2}, MedAE: {medae}")
+
+# Save the model
+dump(gb, 'bestmodelyet.joblib')
+