k-fold-random-forest

import pandas as pd
from sklearn.model_selection import StratifiedKFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# Load your dataset (replace 'your_dataset.csv' with the file path of your dataset)
file_path = 'C:\Users\user\Downloads\success3.csv'
data = pd.read_csv(file_path)

# Assuming 'X' represents features and 'y' represents the target variable
X = data.drop('target_column', axis=1) # Replace 'target_column' with the name of your target
variable
y = data['target_column']

# Initialize the Random Forest model
model = RandomForestClassifier()

# Define the number of splits (k)
num_splits = 5 # Choose the desired number of folds

# Create stratified k-fold cross-validation
skf = StratifiedKFold(n_splits=num_splits, shuffle=True, random_state=42)

fold_scores = []
fold_counter = 1
for train_index, test_index in skf.split(X, y):
X_train, X_test = X.iloc[train_index], X.iloc[test_index]
y_train, y_test = y.iloc[train_index], y.iloc[test_index]

# Fit the model and make predictions
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# Calculate accuracy for each fold
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy for Fold {fold_counter}: {accuracy}")
fold_scores.append(accuracy)
fold_counter += 1

# Calculate the mean accuracy across folds
mean_accuracy = sum(fold_scores) / len(fold_scores)
print(f"Average Cross-Validation Accuracy: {mean_accuracy}")