--- a
+++ b/strokepred.py
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+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.impute import SimpleImputer
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from sklearn.metrics import f1_score, classification_report
+from sklearn.model_selection import GridSearchCV
+
+# Load the datasets
+train_set = pd.read_csv('/path/to/stroke_train_set.csv')
+test_set = pd.read_csv('/path/to/stroke_test_set_nogt.csv')
+
+# Data Preprocessing
+# Separating the target variable and features in the training set
+X = train_set.drop('stroke', axis=1)
+y = train_set['stroke']
+
+# Identifying numerical and categorical columns
+numerical_cols = X.select_dtypes(include=['float64', 'int64']).columns
+categorical_cols = X.select_dtypes(include=['object']).columns
+
+# Creating a preprocessor with transformations for different column types
+preprocessor = ColumnTransformer(
+    transformers=[
+        ('num', SimpleImputer(strategy='median'), numerical_cols),
+        ('cat', OneHotEncoder(), categorical_cols)
+    ])
+
+# Creating a pipeline with preprocessing and a classifier
+model = Pipeline(steps=[('preprocessor', preprocessor),
+                        ('classifier', RandomForestClassifier(random_state=42))])
+
+# Splitting the dataset into training and validation sets
+X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Fit the model
+model.fit(X_train, y_train)
+
+# Predict on validation set and evaluate
+y_pred = model.predict(X_val)
+f1 = f1_score(y_val, y_pred)
+print(f"F1 Score: {f1}")
+print(classification_report(y_val, y_pred))
+
+# Hyperparameter Tuning and Alternative Model
+model_gb = Pipeline(steps=[
+    ('preprocessor', preprocessor),
+    ('classifier', GradientBoostingClassifier(random_state=42))
+])
+
+param_grid = {
+    'classifier__n_estimators': [100, 200],
+    'classifier__learning_rate': [0.01, 0.1],
+    'classifier__max_depth': [3, 5]
+}
+
+grid_search = GridSearchCV(model_gb, param_grid, cv=5, scoring='f1', n_jobs=-1)
+grid_search.fit(X_train, y_train)
+
+# Best parameters and model performance
+best_params = grid_search.best_params_
+y_pred_gb = grid_search.predict(X_val)
+f1_gb = f1_score(y_val, y_pred_gb)
+print(f"Best Parameters: {best_params}")
+print(f"F1 Score (Gradient Boosting): {f1_gb}")
+print(classification_report(y_val, y_pred_gb))
+
+# Preparing final predictions on the test set
+final_predictions = model.predict(test_set)  # Using the best model from above
+submission_df = pd.DataFrame({'ID': test_set.index, 'stroke': final_predictions})
+
+# Saving the submission file
+submission_df.to_csv('/path/to/final_submission.csv', index=False)