CodSoft

Data Science Task

Data Science Projects Repository

Welcome to my Data Science Projects Repository! This repository contains a collection of data science projects I've worked on during my journey as a Data Science enthusiast. Each project focuses on different aspects of data analysis, machine learning, and predictive modeling.

Projects Included

1. Credit Card Fraud Detection

• File: Credit Card Fraud Detection.py
• Description: This project aims to detect fraudulent credit card transactions using machine learning techniques. The goal is to identify patterns that distinguish fraudulent transactions from legitimate ones.

2. Iris Flower Classification

• File: IRIS FLOWER CLASSIFICATION.py
• Description: In this project, I've implemented a machine learning model to classify different species of iris flowers based on their features. The project showcases fundamental data preprocessing, model selection, and evaluation.

3. Sales Prediction using Python

• File: SALES PREDICTION USING PYTHON.py
• Description: This project revolves around predicting sales trends using Python and various predictive modeling techniques. The focus is on forecasting future sales to aid business decisions and strategies.

4. Titanic Survival Prediction

• File: Titanic Survival Prediction.py
• Description: Using the Titanic dataset, this project involves predicting survival outcomes of passengers on the Titanic. It showcases data preprocessing, feature engineering, and machine learning model application.

Usage

Each project file is named after the project's title. You can explore the individual project files to understand the implementation details, data preprocessing steps, model selection, and evaluation techniques used.

Feel free to use these projects as a learning resource, and don't hesitate to reach out if you have any questions or suggestions for improvement.

License

This repository is open-source and can be used for educational purposes. If you plan to use or share any of the code or ideas presented here, please provide appropriate attribution.

Contact

You can connect with me on LinkedIn (https://www.linkedin.com/in/pradeep-soni-649858208/) for discussions, collaborations, and further insights into these projects.

Happy exploring and learning!