Add Fraud Detection with Machine Learning Project

[sanchitc05]

Description:

This issue is for adding a new project titled "Fraud Detection with Machine Learning" to the repository. The goal of this project is to help users understand how machine learning can be applied to detect fraudulent financial transactions.

Tech Stack:

• Programming Language: Python
• Libraries/Tools:
  • pandas for data manipulation
  • NumPy for numerical computing
  • scikit-learn for implementing machine learning algorithms (Logistic Regression, Decision Trees, Random Forest)
  • Matplotlib for data visualization
  • imbalanced-learn to handle class imbalance (e.g., SMOTE)

Approach:

1. Data Preprocessing:

   • Load a publicly available dataset (e.g., the Credit Card Fraud Detection dataset from Kaggle).
   • Perform exploratory data analysis (EDA) to understand the features and label distribution (identify class imbalance).
   • Handle missing values and outliers, if any.
   • Normalize/scale the data as necessary.

2. Model Selection:

   • Train different classification algorithms such as Logistic Regression, Decision Trees, and Random Forests.
   • Use techniques like cross-validation to evaluate model performance.

3. Handling Class Imbalance:

   • Since fraud detection is a highly imbalanced problem, employ techniques like oversampling (SMOTE) or undersampling to address the imbalance between fraudulent and non-fraudulent cases.

4. Model Evaluation:

   • Evaluate models using performance metrics like confusion matrix, precision, recall, and F1-score, focusing on fraud detection accuracy.
   • Visualize model results using ROC-AUC curves and precision-recall curves.

5. Outcome:

   • The project will allow users to predict whether a financial transaction is fraudulent or not based on historical data.
   • Provide a well-documented notebook explaining each step.

If that sounds tempting feel free to assign this issue to me, I will be more than happy to work on it!

[github-actions[bot]]

Thanks for creating the issue,Please read the Pinned issued first and Readme.md in each Pull Request you made. Keep learning...

[coderhetal]

I am a gssoc 2024 extended contributor . Please assign this to me. Also please label it

[Niketkumardheeryan]

@coderhetal this kind of projects already there pls try to add something new