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Train a classification algorithm, such as logistic regression or random forests, to classify transactions as fraudulent or genuine. Evaluate the model' s performance using metrics like precision, recall, and F1-score, and consider techniques like oversampling or undersampling for improving results.
Train a classification algorithm, such as logistic regression or random forests, to classify transactions as fraudulent or genuine. Evaluate the model' s performance using metrics like precision, recall, and F1-score, and consider techniques like oversampling or undersampling for improving results.
kashafashraf230
Task 02 of Machine Learning Internship at CodSoft. Used different Classifiers such as Decision Tree Classifier, Logistic Regression and Random Forest Classifier to build a Credit Card Fraud Detection model which predicts whether a user is likely to commit fraud based on the transaction history and patterns.
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SiddharthWayne
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Abhishekthakur02
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Credit card fraud detection using Machine learning
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The Credit Card Fraud Detection System is a machine learning-based project designed to identify fraudulent credit card transactions. Using supervised learning techniques, the analyzes transaction patterns and predicts whether a transaction is legitimate or fraudulent.
Dhananjay0719
Repo for task no 2 credit card fraud detection model for ml intern at codsoft
marinaredamekhael
FOUR TASKS WITH CODSOFT INTERN (ML) : MOVIE GENRE CLASSIFICATION , CREDIT CARD FRAUD DETECTION , CUSTOMER CHURN PREDICTION AND SPAM SMS DETECTION
RushiKohak
Worked as a Machine Learning Intern at CodSoft, building real-world ML projects using Python. Completed projects on credit card fraud detection, customer churn prediction, and spam SMS detection. Gained hands-on experience in data preprocessing, model training, evaluation, and debugging.
Mujeeb117
"Excited to showcase my journey as an intern at CodSoft! From tackling Credit Card Fraud Detection to mastering Sales Prediction, Titanic Survival Prediction, Iris Flower Classification, and Movie Rating Prediction, each task has been a rewarding learning experience.
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