CREDIT CARD FRAUD DETECTION

About the program Completed a 7week 45day internship focused on developing a Credit Card Fraud Detection system using data analytics and machine learning techniques. The project involved analyzing large volumes of transaction data to identify fraudulent patterns and minimize false positives. Key responsibilities included: Data collection, cleaning, and preprocessing of transaction datasets Exploratory Data Analysis EDA to detect fraud trends and anomalies Handling imbalanced datasets using resampling techniques e.g., SMOTE Building and evaluating machine learning models such as Logistic Regression, Decision Trees, Random Forest, and XGBoost Model performance evaluation using metrics like Accuracy, Precision, Recall, F1Score, and ROCAUC Creating visual dashboards to present fraud insights and risk patterns The final outcome was a predictive model capable of detecting suspicious transactions with improved accuracy and reduced false alarms. Perks Hands-on experience with real-world financial transaction data Practical exposure to fraud detection systems used in fintech and banking industries Skill development in Python, data preprocessing, and machine learning algorithms Experience in model evaluation and performance optimization Improved analytical thinking and problem-solving skills Internship completion certificate Professional mentorship and guidance Who can apply? Only those candidates can apply who: are from Any and specialisation from Any are available for duration of 7 Weeks have relevant skills and interests Terms of Engagement Duration: 7 weeks (45 days) Confidentiality: Required to maintain data privacy and adhere to organizational data security policies Deliverables: Submission of weekly progress reports and final project presentation Evaluation: Performance assessed based on participation, project quality, and final model performance Stipend: [Mention if applicable / Unpaid internship if applicable] Number of openings 8