Name: PRIYA KOTAGIRIWAR
Company: CODTECH IT SOLUTIONS
ID: CT4ML3288
Domain: MACHINE LEARNING
Duration: June to July 2024
Mentor: Neela Santosh Kumar
The objective of this project is to develop a predictive model using logistic regression to detect fraudulent credit card transactions. This model aims to assist financial institutions and credit card companies in identifying potentially fraudulent activities in real-time, thereby reducing financial losses and enhancing security measures.
Data Collection and Preparation: Gather a comprehensive dataset containing information about credit card transactions, including features such as transaction amount, time, location, merchant details, and other relevant attributes. Preprocess the data to handle missing values, outliers, and categorical variables. Balance the dataset if necessary, to address the class imbalance typically present in fraud detection problems.
Feature Selection and Engineering: Identify the most significant features that influence the likelihood of a transaction being fraudulent and create new features if necessary. This step involves exploratory data analysis (EDA) to understand the relationships between different features and the target variable (fraudulent or not).
Model Development: Implement a logistic regression model to predict the probability of a transaction being fraudulent based on the selected features. Split the dataset into training and testing sets to evaluate the model's performance.
Model Evaluation and Validation: Assess the accuracy and performance of the logistic regression model using appropriate metrics such as Accuracy, F1 Score. Validate the model by comparing its predictions with actual outcomes on the test dataset.
Optimization and Tuning: Fine-tune the model by adjusting hyperparameters, adding or removing features, and experimenting with different regularization techniques to improve its predictive accuracy and handling of imbalanced data.
Programming Languages:
Python: The primary language for data analysis, model development, and implementation due to its extensive libraries and ease of use.
Data Handling and Analysis:
Pandas: For data manipulation and analysis, including data cleaning, transformation, and exploration.
NumPy: For numerical computations and handling arrays.
Data Visualization:
Matplotlib: For creating static, interactive, and animated visualizations.
Seaborn: For statistical data visualization, making it easier to plot complex graphs.
Machine Learning Libraries:
Scikit-learn: For implementing logistic regression models, feature selection, model evaluation, and validation.
Data Preprocessing:
Scikit-learn Preprocessing: For scaling, encoding categorical variables, handling missing values, and techniques for handling class imbalance.
Model Evaluation:
Scikit-learn Metrics: For calculating evaluation metrics such as Accuracy, F1 Score.
Integrated Development Environment (IDE):
Colab Notebook: For interactive development, data exploration, and visualization.