Data_Science_with_ML

Welcome to the Data Science with Machine Learning repository! This repository is a curated collection that showcases my learning journey in data science and machine learning. It includes Exploratory Data Analysis (EDA), machine learning models (regression, classification, clustering), visualization examples, and end-to-end workflows. Additionally, this repository contains my class assignments and tests.

Table of Contents

• Introduction
• Getting Started
• Repository Structure
• Technologies Used
• Contributing
• License
• Contact

Introduction

This repository is designed to provide a comprehensive overview of my progress in the field of data science and machine learning. The goal is to document the learning process, share knowledge, and collaborate with others in the community.

Getting Started

To get started with this repository, follow these steps:

1. Clone the repository:

   git clone https://github.com/Virendra108/Data_Science_with_ML.git

2. Navigate to the project directory:

   cd Data_Science_with_ML

3. Install necessary dependencies:

   • Ensure you have Python and Jupyter Notebook installed.
   • Install Python packages:

   pip install -r requirements.txt

4. Launch Jupyter Notebook:

   jupyter notebook

Repository Structure

The repository is organized as follows:

• 00-Assignments
• 00-TEST
• 1-Python
• 10-Interview Prepration
• 11-Recomendation Engine
• 12-Confusion Matrix
• 13-Naive_byes_classification
• 14-Ensamble Learning
• 15-Linear Regression
• 16-Hypothesis
• 2-SQL
• 3-Stastics
• 4-Ai
• 5-Data_prep
• 6-Computer_Vision
• 7-Clustering
• 8-IBM-study material
• 9-PCA & SVD

Technologies Used

• Jupyter Notebook: For interactive data analysis and visualization.
• PLpgSQL: For database manipulation and querying.
• Python: For data processing and machine learning.

Contributing

Contributions are welcome! If you have suggestions, bug reports, or improvements, please open an issue or submit a pull request.

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes and commit them (git commit -m 'Add new feature').
4. Push to the branch (git push origin feature-branch).
5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contact

For any questions or inquiries, feel free to reach out:

• GitHub: Virendra108

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Happy Learning and Coding!