🦠 COVID-19 India Data Analysis & Visualization

This project demonstrates a data engineering and visualization pipeline that scrapes raw COVID-19 data from an API, processes it, and generates a dynamic "racing" bar chart visualization. This project showcases skills in data extraction, cleaning, and creating animated data visualizations.

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📊 Project Goal

The primary goal of this project is to provide a comprehensive workflow for analyzing COVID-19 case trends in India. It focuses on:

• API Data Extraction: Fetching raw, unstructured data from https://api.covid19india.org/raw_data.json.
• Data Processing: Cleaning, structuring, and aggregating the raw data into a usable format (e.g., daily confirmed cases per state).
• Dynamic Visualization: Creating a running bar chart to visually represent the evolution of total COVID-19 cases by state over time. This type of visualization effectively highlights trends and changes in rankings.

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🛠️ Technologies Used

• Python: The core programming language.
• Requests: A library for making API calls to fetch the raw JSON data.
• Pandas: For data loading, cleaning, aggregation, and manipulation.
• Matplotlib & Pandas Plotting: For creating static plots.
• matplotlib.animation or bar_chart_race: For generating the animated running bar chart.

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📁 Project Structure

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├── COVID-19-India.ipynb    
├── requirements.txt          # List of required Python libraries
└── README.md                 # This file

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📝 How to Run the Project

1. Clone the repository:

   git clone https://github.com/satishf889/COVID19-india-analysis.git
   cd COVID19-india-analysis

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required libraries:

   pip install -r requirements.txt

4. Run the notebook:

   Upon execution, the script will:

   • Fetch the raw data from the API.
   • Process the data to aggregate total cases per state over time.

Snapshot from notebook

Note: Data in this api is getting updated slowly so you might see differences in count.