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import lightgbm as lgb
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.metrics import mean_squared_error
import numpy as np
# Load the data
train_data = pd.read_csv("./input/train.csv")
test_data = pd.read_csv("./input/test.csv")
sample_submission = pd.read_csv("./input/sample_submission.csv")
# Prepare the data
X = train_data.drop(["MedHouseVal", "id"], axis=1)
y = train_data["MedHouseVal"]
X_test = test_data.drop("id", axis=1)
# Prepare cross-validation
kf = KFold(n_splits=10, shuffle=True, random_state=42)
rmse_scores = []
# Perform 10-fold cross-validation
for train_index, val_index in kf.split(X):
X_train, X_val = X.iloc[train_index], X.iloc[val_index]
y_train, y_val = y.iloc[train_index], y.iloc[val_index]
# Create LightGBM datasets
train_set = lgb.Dataset(X_train, label=y_train)
val_set = lgb.Dataset(X_val, label=y_val)
# Train the model
params = {"objective": "regression", "metric": "rmse", "verbosity": -1}
model = lgb.train(
params, train_set, valid_sets=[train_set, val_set], verbose_eval=False
)
# Predict on validation set
y_pred = model.predict(X_val, num_iteration=model.best_iteration)
# Calculate RMSE
rmse = np.sqrt(mean_squared_error(y_val, y_pred))
rmse_scores.append(rmse)
# Print the average RMSE across the folds
print(f"Average RMSE: {np.mean(rmse_scores)}")
# Train the model on the full dataset
full_train_set = lgb.Dataset(X, label=y)
final_model = lgb.train(params, full_train_set, verbose_eval=False)
# Predict on the test set
predictions = final_model.predict(X_test, num_iteration=final_model.best_iteration)
# Prepare the submission file
submission = pd.DataFrame({"id": test_data["id"], "MedHouseVal": predictions})
submission.to_csv("./working/submission.csv", index=False)
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