Python and Data Science Basics Repository

Welcome to my repository! This repository contains resources and projects from my summer bootcamp, where I have learned and applied various concepts in Python, SQL, and data visualization libraries like Seaborn and Matplotlib. I will continue to update this repository with more projects in the future.

Table of Contents

• Introduction
• Technologies Used
• Contents
• Projects
  • Zomato Application
  • Customer Churn Analysis
• Future Projects
• How to Use
• Contributing
• Contact

Introduction

This repository serves as a collection of my work and learning progress in Python and data science. It includes tutorials and examples on:

• Python programming
• SQL database management
• Data visualization using Seaborn and Matplotlib
• Data manipulation using NumPy and pandas

Technologies Used

• Python: General-purpose programming language
• SQL: Database management
• Seaborn: Data visualization library based on Matplotlib
• Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations
• NumPy: Library for numerical computations
• pandas: Data manipulation and analysis library
• Tkinter: Standard GUI library for Python

Contents

• python_basics/: Basics of Python programming
• sql_basics/: Basics of SQL
• seaborn_tutorials/: Examples and tutorials for Seaborn
• matplotlib_tutorials/: Examples and tutorials for Matplotlib
• numpy_tutorials/: Examples and tutorials for NumPy
• pandas_tutorials/: Examples and tutorials for pandas
• Tkinter/: Examples and tutorials for Tkinter

Projects

Zomato Application

This project is a simulation of the Zomato food delivery app, which was originally developed as a group project with my teammates. It was built using Python, SQL, Tkinter, and Python-SQL connectivity.

Key features:

• User Authentication: Login and registration system
• Profile Management: View and edit user profiles
• Restaurant Listings: Display and search for restaurants
• Order Management: Place and manage orders

Technologies Used:

• Python
• SQL
• Tkinter
• Python-SQL connectivity

Directory Structure:

• zomato_app/ (forked from original group project repository)
  • Database/: Contains all SQL files for the database
  • Database_Scheme_and_Data/
    • ER_diagram.jpg: Entity-Relationship diagram
    • zomato_er_diagram.pbix: Power BI file of the ER diagram
    • zomatosqlexcelsheet.xlsx: Excel sheet with SQL data
  • GUI/
    • __pycache__/
      • Main_window.cpython-311.pyc
      • Main_window.cpython-312.pyc
    • main_window.py: Main GUI window implementation
    • user_zomato_authentication.py: User authentication module
  • Images/
    • zomato.jpg: Zomato logo or application image

For the complete project history and contributions, please refer to the original repository (https://github.com/ManoharKonala/Zomato-Clone-with-Python-Tkinter-and-MySQL).

Customer Churn Analysis

This project analyzes customer churn patterns in a telecommunications company, focusing on various factors that influence customer retention.

Table of Contents:

1. Introduction
2. Data Loading and Preprocessing
3. Exploratory Data Analysis (EDA)
4. Univariate Analysis
5. Bivariate Analysis
6. Multivariate Analysis
7. Conclusions

Key Features:

• Comprehensive analysis of customer churn factors
• Visualization of churn patterns using various plots (histograms, bar plots, violin plots, etc.)
• In-depth examination of relationships between different variables and churn

Technologies Used:

• Python
• pandas
• NumPy
• Matplotlib
• Seaborn

Key Findings:

• Higher churn rates among customers with Fiber optic internet service and higher monthly charges
• Senior citizens with lower tenure are more likely to churn
• Customers on month-to-month contracts have higher churn rates compared to those on longer-term contracts
• Factors such as online security and backup services influence churn rates

Directory Structure:

• customer_churn_analysis/
  • churn_analysis.ipynb: Jupyter notebook containing the full analysis
  • WA_Fn-UseC_-Telco-Customer-Churn.csv: Dataset used for the analysis

This project provides valuable insights into customer behavior and factors affecting churn in the telecommunications industry. The findings can be used to develop targeted retention strategies and improve customer satisfaction.

Future Projects

I plan to add more projects in the future, including but not limited to:

• Data analysis projects using real-world datasets
• Machine learning projects
• Advanced data visualization projects

Stay tuned for updates!

How to Use

To use the contents of this repository:

1. Clone the repository:

   git clone https://github.com/ManoharKonala/BootCamp_2K24.git
   
   

Contributing to Python and Data Science Basics Repository

Thank you for your interest in contributing to this repository! Your contributions are highly appreciated.

How to Contribute

Reporting Issues

If you find any bugs or have suggestions for improvements, please open an issue on GitHub.

Submitting Pull Requests

1. Fork the repository.
2. Create a new branch: git checkout -b my-feature-branch
3. Make your changes.
4. Commit your changes: git commit -m 'Add some feature'
5. Push to the branch: git push origin my-feature-branch
6. Open a pull request.

Coding Standards

Please ensure your code adheres to the following standards:

• Follow the PEP 8 style guide for Python code.
• Write clear and concise commit messages.
• Include comments and docstrings where necessary.

Contact Information

If you have any questions, feedback, or need further assistance, feel free to reach out to me through the following channels:

• Email: konalavrmanohar@gmail.com
• LinkedIn: (https://www.linkedin.com/in/konala-venkata-rama-manohar-56487a288/)
• GitHub: (https://github.com/ManoharKonala)

I am always open to collaboration and discussion. Thank you for your interest in my repository!