ML & Data Science Projects

Welcome to the ML & Data Science Projects repository! This repository contains a series of data science and machine learning projects aimed at better understanding their potential applications in work-related environments as well as real life.

Table of Contents

• Introduction
• Projects
• Technologies Used
• How to Use
• Contributing
• License

Introduction

Data science and machine learning are rapidly evolving fields with applications across various industries. This repository serves as a collection of projects that explore different aspects of data science and machine learning, ranging from data analysis and visualization to predictive modeling and deep learning.

Projects

Here's a brief overview of the projects included in this repository:

1. Water Strider Classification and Behavioral Pattern Analysis

   • Description: This project focuses on classifying water striders into different species and analyzing their behavioral patterns using machine learning algorithms.
   • Technologies: Python, scikit-learn, pandas, matplotlib

2. Predicting Cancer Likelihood in Patients

   • Description: In this project, predictive modeling techniques are applied to medical data to determine the likelihood of cancer in patients based on various factors.
   • Technologies: Python, scikit-learn, pandas, numpy, matplotlib

Technologies Used

The projects in this repository primarily utilize the following technologies:

• Python 3.x
• Jupyter Notebook
• Various Python libraries for data manipulation, analysis, and machine learning (e.g., scikit-learn, pandas, numpy, matplotlib, seaborn)

How to Use

To explore any of the projects in this repository, follow these steps:

1. Clone the repository to your local machine using git clone.
2. Navigate to the project directory.
3. Install the necessary dependencies using pip install -r requirements.txt.
4. Open the project notebooks using Jupyter Notebook or any compatible IDE.
5. Follow the instructions provided within each project notebook to run and experiment with the code.

Contributing

Contributions to this repository are welcome! If you have ideas for additional projects or improvements to existing ones, feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.