davidkariuki
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90ae9e0
Upload train.py
Browse filesThe script I used to train my dataset.
train.py
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import pandas as pd
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from sklearn.preprocessing import LabelEncoder
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from sklearn.model_selection import train_test_split
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from sklearn.ensemble import GradientBoostingRegressor
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from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score, median_absolute_error
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from joblib import dump
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# Load the dataset
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df = pd.read_csv('cleaned_housesTRAIN.csv')
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# Apply label encoding to 'Area' and 'Suburb'
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le_area = LabelEncoder()
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df['Area'] = le_area.fit_transform(df['Area'])
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le_suburb = LabelEncoder()
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df['Suburb'] = le_suburb.fit_transform(df['Suburb'])
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# Save the label encoders
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dump(le_area, 'le_area.joblib')
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dump(le_suburb, 'le_suburb.joblib')
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# Shuffle the dataframe
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df = df.sample(frac=1)
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# Split the data into features (X) and target (y)
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X = df.drop('Rent', axis=1)
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y = df['Rent']
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# Split the data into training and test sets (90/10 split)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
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# Create a Gradient Boosting regressor with specified hyperparameters
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gb = GradientBoostingRegressor(n_estimators=850, learning_rate=0.195, max_depth=7, random_state=42)
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# Train the model
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gb.fit(X_train, y_train)
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# Make predictions on the test set
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y_pred = gb.predict(X_test)
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# Calculate MAE, MSE, and R2
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mae = mean_absolute_error(y_test, y_pred)
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mse = mean_squared_error(y_test, y_pred)
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r2 = r2_score(y_test, y_pred)
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medae = median_absolute_error(y_test, y_pred)
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print(f"MAE: {mae}, MSE: {mse}, R2: {r2}, MedAE: {medae}")
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# Save the model
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dump(gb, 'bestmodelyet.joblib')
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