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# File path
file_path = '/mnt/data/heart.csv'
# Import necessary libraries
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, roc_curve, auc
from sklearn.preprocessing import StandardScaler, LabelEncoder
import xgboost as xgb
import joblib
# Step 1: Data Cleaning and Encoding
# Load data
data = pd.read_csv(file_path)
# Handle missing values (example: filling with median)
data = data.fillna(data.median())
# Encode categorical variables
label_encoders = {}
for column in data.select_dtypes(include=['object']).columns:
le = LabelEncoder()
data[column] = le.fit_transform(data[column])
label_encoders[column] = le
# Step 2: Plotting the Dependency Matrix
plt.figure(figsize=(12, 8))
correlation_matrix = data.corr()
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')
plt.title('Correlation Matrix')
plt.show()
# Step 3: Supervised Learning Model for Prediction using XGBoost
# Define features and target
X = data.drop('target', axis=1) # Assuming 'target' is the target variable
y = data['target']
# Split the data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Standardize the data
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
# Train the model
model = xgb.XGBClassifier()
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
y_pred_prob = model.predict_proba(X_test)[:, 1]
# Step 4: Evaluation Using Confusion Matrix and Plotting ROC Curve
# Confusion Matrix
conf_matrix = confusion_matrix(y_test, y_pred)
sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues')
plt.title('Confusion Matrix')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.show()
# ROC Curve
fpr, tpr, _ = roc_curve(y_test, y_pred_prob)
roc_auc = auc(fpr, tpr)
plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:0.2f})')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend(loc='lower right')
plt.show()
# Save the model using joblib
model_filename = '/mnt/data/xgboost_model.joblib'
joblib.dump(model, model_filename)
print(f"Model saved to {model_filename}")
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