AI&ML Intern | Credit Card Fraud Detection & SMS Spam Detection Worked on two end-to-end machine learning projects focused on fraud detection and text classification using data science techniques. Credit Card Fraud Detection using Random Forest Developed a fraud detection model on highly imbalanced transaction data. Performed data cleaning, preprocessing, feature engineering, and class imbalance handling. Conducted EDA to analyze transaction patterns and applied the Random Forest algorithm for classification. Evaluated performance using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC. SMS Spam Detection using Naive Bayes Built an SMS spam classifier using Natural Language Processing techniques. Applied text preprocessing, TF-IDF vectorization, and implemented the Naive Bayes algorithm. Evaluated the model using accuracy, precision, recall, and F1-score. Used Python, Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn for implementation and visualization.