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📊 Data Science Project Portfolio

A comprehensive collection of data science projects showcasing exploratory data analysis, predictive modeling, and machine learning techniques. This portfolio includes three distinct projects with end-to-end analysis from data preprocessing to model evaluation.

🎯 Projects Overview

1. 🦠 COVID-19 Global Analysis

Project File: Project_Covid_19_Analysis.ipynb

An exploratory data analysis of COVID-19 confirmed cases and deaths using the John Hopkins dataset. This project involves:

  • Data aggregation and preprocessing
  • Time-series visualization of confirmed cases by country
  • Statistical analysis and trend identification
  • Global and country-specific insights

Dataset:

  • covid19_Confirmed_dataset.csv - Confirmed cases data
  • covid19_deaths_dataset.csv - Deaths data

Key Techniques: EDA, Data Aggregation, Time-Series Analysis, Data Visualization

2. 🌧️ Rainfall Prediction Using Linear Regression

Project File: Project_Rainfall_Prediction.ipynb

A predictive modeling project using linear regression to forecast rainfall in Austin, Texas. This project demonstrates:

  • Weather data preprocessing and feature engineering
  • Linear regression model development
  • Model performance evaluation
  • Weather pattern analysis

Dataset: austin_weather.csv - Austin weather data with historical measurements

Key Techniques: Linear Regression, Feature Engineering, Model Evaluation, Data Cleaning

3. 🔬 Tumor Detection

Project File: Project_Tumor_Detection (1).ipynb

A machine learning classification project for tumor detection with comprehensive data preprocessing and exploratory analysis. This project includes:

  • Data preprocessing and feature engineering
  • Exploratory data analysis
  • Classification model development
  • Model evaluation and performance metrics

Dataset: Tumor_Detection.csv - Tumor detection dataset with medical indicators

Key Techniques: Classification, Data Preprocessing, EDA, Feature Selection, Model Evaluation

📁 Project Structure

DataScienceProject/
├── README.md                                    # Project documentation
├── app1.py                                      # Streamlit web application
├── requirements.txt                             # Python dependencies
│
├── Project_Covid_19_Analysis.ipynb             # COVID-19 analysis notebook
├── Project_Rainfall_Prediction.ipynb           # Rainfall prediction notebook
├── Project_Tumor_Detection (1).ipynb           # Tumor detection notebook
│
├── covid19_Confirmed_dataset.csv               # COVID-19 confirmed cases data
├── covid19_deaths_dataset.csv                  # COVID-19 deaths data
├── austin_weather.csv                          # Austin weather data
├── Tumor_Detection.csv                         # Tumor detection data
└── worldwide_happiness_report.csv              # Additional dataset

🛠️ Technologies & Libraries

  • Python 3.x
  • Data Processing: pandas, NumPy
  • Visualization: Matplotlib, Seaborn
  • Machine Learning: scikit-learn
  • Web Framework: Streamlit
  • Jupyter Notebook

📦 Installation & Setup

Prerequisites

  • Python 3.7 or higher
  • pip or conda package manager

1. Clone or Download Repository

cd DataScienceProject

2. Install Dependencies

pip install -r requirements.txt

3. Run the Projects

Option A: Jupyter Notebooks (Individual Projects)

# Run individual notebooks
jupyter notebook Project_Covid_19_Analysis.ipynb
jupyter notebook Project_Rainfall_Prediction.ipynb
jupyter notebook "Project_Tumor_Detection (1).ipynb"

Option B: Streamlit Web Application (All Projects)

streamlit run app1.py

This launches an interactive web application showcasing all three projects with:

  • Project selection via sidebar navigation
  • Interactive data exploration
  • Real-time visualizations
  • Multi-select country comparison (COVID-19)

🚀 Live Demonstrations

📊 Interactive Dashboard - All Projects in One Place

Access all three projects through our unified web application:

🔗 View Live Dashboard

This single application provides:

  • ✅ Interactive navigation between all 3 projects
  • ✅ Real-time data visualizations
  • ✅ Multi-select country comparison (COVID-19)
  • ✅ Rainfall predictions and weather analysis
  • ✅ Tumor detection analysis and insights
  • ✅ Full exploratory data analysis

📊 Key Features

COVID-19 Analysis

  • ✅ Global case tracking
  • ✅ Country-wise comparison
  • ✅ Time-series trends
  • ✅ Death statistics
  • ✅ Interactive filtering

Rainfall Prediction

  • ✅ Weather data preprocessing
  • ✅ Linear regression modeling
  • ✅ Performance metrics visualization
  • ✅ Weather pattern analysis
  • ✅ Prediction accuracy evaluation

Tumor Detection

  • ✅ Medical data preprocessing
  • ✅ Feature correlation analysis
  • ✅ Classification model development
  • ✅ Performance evaluation
  • ✅ Data insights visualization

💡 How to Use

For Learning:

  1. Start with individual Jupyter notebooks to understand the analysis step-by-step
  2. Review comments and documentation in each cell
  3. Experiment with parameters and visualizations

For Deployment:

  1. Set up a Streamlit Cloud account
  2. Connect your GitHub repository
  3. Deploy app1.py for an interactive dashboard
  4. Share the deployed link in the Demo section above

For Modification:

  1. Update datasets as needed
  2. Modify parameters in notebooks or app1.py
  3. Add new visualizations or analysis
  4. Create additional project notebooks following the same structure

📈 Expected Outputs

  • COVID-19: Line charts showing case progression, statistical summaries, data tables
  • Rainfall: Regression plots, performance metrics, weather insights
  • Tumor Detection: Classification reports, feature importance, confusion matrices

🤝 Contributing

Feel free to:

  • Fork and enhance these projects
  • Add new analysis or models
  • Improve visualizations
  • Expand datasets
  • Create additional notebooks

📝 Notes

  • Ensure all CSV files are in the same directory as the Jupyter notebooks and app1.py
  • For Streamlit app, run from the project root directory
  • Update file paths if reorganizing project structure
  • Check that all dependencies are installed before running

📞 Support & Contact

For questions or issues:

  • Review notebook comments and documentation
  • Check dataset descriptions in respective notebooks
  • Refer to library documentation (Pandas, Scikit-learn, Streamlit)

📄 License

This project is open for educational and personal use. Feel free to use and modify these projects for learning purposes.

Last Updated: June 2024

Happy Analyzing! 🎉