NIDHISHARMA21/Credit-Card-Fraud-Detection

CODSOFT | TASK 1 | Credit-Card-Fraud-Detection

This project involves detecting fraudulent credit card transactions using various machine learning models. The dataset used is the Credit Card Fraud Detection dataset, which contains anonymized features of transactions and a target variable indicating fraud.

Key Steps:

Data Preprocessing:

Loaded the dataset and checked for missing values.

Applied Winsorization to handle outliers in the features.

Performed exploratory data analysis (EDA) with visualizations such as box plots, heatmaps, and histograms to understand the data distribution and correlations.

Model Training:

Split the data into training and testing sets.

Trained and evaluated three models: Logistic Regression, XGBoost Classifier, and Support Vector Classifier (SVC).

Evaluated models using metrics like AUC, accuracy, confusion matrix, and classification report.

Model Saving:

Saved the trained models using Pickle for future use.

Tools & Libraries:

Python pandas numpy matplotlib seaborn scikit-learn xgboost pickle

This repository includes the code for data preprocessing, model training, evaluation, and saving the models. The goal is to provide a robust solution for detecting fraudulent transactions effectively.