Data Analytics Python Project

A collection of Python-based data analytics projects showcasing exploratory data analysis, visualizations, and actionable insights.

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Table of Contents

1. Overview
2. Projects
   • ChatGPT Reviews Analysis
   • Elections Ad Spending Analysis
   • IPL 2024 RCB vs DC Analysis
   • Netflix Content Strategy Analysis
   • Rainfall Trends in India Analysis
3. Technologies Used
4. Setup Instructions
5. Contributing
6. License
7. Contact

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Overview

The Data Analytics Python Project repository features multiple projects focused on real-world data problems. Each project:

• Leverages Python for data manipulation, visualization, and analysis.
• Explores datasets to uncover patterns and provide actionable insights.
• Utilizes visual storytelling to communicate results effectively.

These projects are suitable for data analysts, data scientists, and Python enthusiasts seeking to learn or implement analytics workflows.

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Projects

1. ChatGPT Reviews Analysis

• Description: Analyze user feedback and reviews of ChatGPT to uncover sentiment trends, popular features, and common issues.
• Key Highlights:
  • Sentiment classification (positive, neutral, negative)
  • Word cloud visualization of frequently used terms
  • Recommendations for improvement based on user feedback

2. Elections Ad Spending Analysis

• Description: An exploration of advertising spending during elections, focusing on key parties, platforms, and spending trends.
• Key Highlights:
  • Comparison of ad spending across major political parties
  • Time-series analysis of ad spending patterns
  • Correlation between ad spending and public sentiment

3. IPL 2024 RCB vs DC Analysis

• Description: A detailed analysis of the IPL 2024 match between RCB and DC, covering player performance and team strategies.
• Key Highlights:
  • Player performance metrics (e.g., strike rates, economy rates)
  • Team comparisons across batting and bowling metrics
  • Predictive insights for future matches

4. Netflix Content Strategy Analysis

• Description: Evaluate Netflix’s content strategy by analyzing trends in genres, audience preferences, and production patterns.
• Key Highlights:
  • Genre trends over time
  • Regional content preferences
  • Recommendations for improving viewer retention

5. Rainfall Trends in India Analysis

• Description: A study of rainfall patterns in India, identifying seasonal trends and state-wise anomalies.
• Key Highlights:
  • State-wise rainfall distribution
  • Seasonal deviations and anomalies
  • Predictive modeling for rainfall trends

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Technologies Used

The projects in this repository leverage the following technologies and libraries:

• Programming Language: Python
• Advanced Excel Functions: Demonstrates use of Excel functions like VLOOKUP, HLOOKUP, IF, INDEX-MATCH, and more.
• Libraries:
  • Data Analysis: Pandas, NumPy
  • Visualization: Matplotlib, Seaborn
  • NLP: NLTK
  • Predictive Modeling: Scikit-learn
• Tools:
  • Jupyter Notebook

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Setup Instructions

1. Clone the repository:

      git clone https://github.com/kunalkumar2001/Data-Analytics-Python-Project.git
      cd Data-Analytics-Python-Project
   
   

Contributing

Contributions are welcome!

If you’d like to improve the projects, fix bugs, or add new analyses, follow these steps:

1. Fork the repository.
2. Create a feature branch:

      git checkout -b feature/your-feature-name

3. Commit your changes

    git commit -m "Add your feature description"

4. Push the branch to your fork:

      git push origin feature/your-feature-name

5. Open a pull request

License

This project is licensed under the MIT License.

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This README.md provides a clear overview of each project, its purpose, and your analytical techniques. Let me know if you'd like to add more details to specific projects or sections!