Codsoft Intern

Abstract

1. Introduction

This repository contains the projects I worked on during my Codsoft internship. Each project focuses on a different machine learning problem, including credit card fraud detection, customer churn prediction, movie genre classification, and SMS spam detection.

2. Objective

This repository contains the projects I worked on during my Codsoft internship. Each project focuses on a different machine learning problem, including credit card fraud detection, customer churn prediction, movie genre classification, and SMS spam detection.

This project demonstrates how Jupyter Notebook can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/abhiverse01/Codsoft-Intern.git
cd Codsoft-Intern

1. To install the required packages, run:
2. To run any of the projects, navigate to the respective project directory and execute the Jupyter notebook.

pip install -r requirements.txt

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by abhiverse01 and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.