This repository contains three comprehensive data science projects completed during my internship at Code Alpha. Each project demonstrates different aspects of machine learning, data analysis, and visualization using Python.
| Task | Project Name | Domain | Key Technologies |
|---|---|---|---|
| 01 | Iris Flower Classification | Machine Learning | Scikit-learn, Random Forest |
| 02 | Unemployment Analysis in India | Data Analysis | Pandas, Matplotlib, Seaborn |
| 03 | Car Price Prediction | Regression Analysis | Linear Regression, Feature Engineering |
Classify iris flowers into three species (Setosa, Versicolor, Virginica) using machine learning algorithms based on sepal and petal measurements.
- Random Forest Classifier implementation
- Confusion Matrix visualization with heatmap
- Model Performance evaluation
- Clean Data Pipeline with train-test split
- High accuracy classification model
- Visual confusion matrix showing model performance
- Proper handling of categorical target variables
python Task_01.py- pandas
- scikit-learn
- seaborn
- matplotlib
Analyze unemployment trends in India with special focus on COVID-19 impact, regional disparities, and seasonal patterns.
- Time Series Analysis of national unemployment trends
- COVID-19 Impact Visualization with lockdown markers
- State-wise Comparison during peak crisis period
- Seasonal Pattern Analysis including monsoon effects
- Interactive Insights Dashboard with professional styling
- National Trend: Line plot with data point labels and COVID markers
- Regional Impact: Horizontal bar chart with color gradient
- Seasonal Patterns: Monthly averages with monsoon highlighting
- Insights Summary: Professional text visualization with key findings
- Unemployment peaked at ~24% in April 2020
- Significant regional disparities during crisis
- Seasonal patterns linked to monsoon periods
- Gradual recovery post-lockdown
python Task_02.py- pandas
- matplotlib
- seaborn
- numpy
Predict used car prices using machine learning regression techniques with comprehensive feature analysis and data visualization.
- Linear Regression Model with preprocessing pipeline
- Correlation Analysis with masked heatmap
- Price Category Analysis with custom legends
- Feature Relationship Visualization with trend lines
- Model Performance Evaluation with prediction plots
- Correlation Matrix: Numerical features relationships
- Price Distribution: Categorical analysis with value labels
- Price vs Present Price: Scatter plot with trend analysis
- Age vs Price: Relationship analysis with correlation metrics
- Model Evaluation: Actual vs Predicted comparison
- Feature Engineering: Car age calculation from manufacturing year
- Categorical Encoding: One-hot encoding for fuel type, transmission, etc.
- Performance Metrics: MAE, RMSE, R² score evaluation
- Professional Visualizations: Enhanced legends and statistical annotations
python Task_03.py- pandas
- numpy
- scikit-learn
- matplotlib
- seaborn
Code Alpha Internship/
├── Task_01.py # Iris Classification
├── Task_02.py # Unemployment Analysis
├── Task_03.py # Car Price Prediction
├── README.md # This file
├── car_data.csv # Car dataset
├── Unemployment in India.csv # Unemployment dataset
└── Iris.csv # Iris dataset (if using custom data)
- Python 3.7+
- pip package manager
-
Clone the repository
git clone https://github.com/Hackbits/Code-Alpha-Internship.git cd code-alpha-internship -
Install dependencies
pip install pandas numpy scikit-learn matplotlib seaborn
-
Run any project
python Task_01.py # For Iris Classification python Task_02.py # For Unemployment Analysis python Task_03.py # For Car Price Prediction
- Confusion matrix heatmap showing classification accuracy
- Model performance metrics
- Time series plots with COVID-19 impact markers
- State-wise unemployment comparison charts
- Seasonal trend analysis with insights
- Correlation heatmaps and feature analysis
- Price prediction scatter plots
- Model evaluation metrics and visualizations
- Machine Learning: Classification and regression algorithms
- Data Visualization: Professional plots with matplotlib/seaborn
- Data Preprocessing: Feature engineering and encoding
- Model Evaluation: Performance metrics and validation
- Classification Problems: Multi-class species identification
- Time Series Analysis: Trend analysis and seasonal patterns
- Regression Analysis: Price prediction and feature importance
- Real-world Applications: COVID impact analysis, market prediction
- Advanced Models: Try XGBoost, Neural Networks
- Cross-validation: Implement k-fold validation
- Hyperparameter Tuning: Grid search optimization
- Interactive Dashboards: Streamlit/Plotly integration
- API Development: Flask/FastAPI for model serving
- Feature Selection: Automated feature importance analysis
- Model Comparison: Multiple algorithm performance comparison
- Data Pipeline: Automated data preprocessing
- Deployment: Docker containerization
S SRIDHAR RAO
- 🎓 Data Science Intern at Code Alpha
- 📧 Email: sridharrao764@gmail.com
- 💼 LinkedIn: S Sridhar Rao
- 🐙 GitHub: Hackbits
- Code Alpha for providing the internship opportunity
- Scikit-learn community for excellent ML libraries
- Matplotlib/Seaborn for powerful visualization tools
- Pandas for efficient data manipulation
This project is licensed under the MIT License - see the LICENSE file for details.
- Fork the repository
- Create your feature branch (
git checkout -b feature/AmazingFeature) - Commit your changes (
git commit -m 'Add some AmazingFeature') - Push to the branch (
git push origin feature/AmazingFeature) - Open a Pull Request
If you have any questions or suggestions, feel free to:
- Open an issue in this repository
- Contact me directly via email
- Connect with me on LinkedIn
⭐ If you found this project helpful, please give it a star!