Successfully completed a comprehensive Data Science learning program focused on data analysis, machine learning, feature engineering, and model deployment. This repository showcases practical implementations, reproducible notebooks, and data-driven insights using real-world datasets.
- Exploratory Data Analysis (EDA)
- Data Cleaning & Preprocessing
- Feature Engineering
- Data Visualization
- Statistical Analysis
- Supervised Machine Learning
- Model Training & Evaluation
- Performance Metrics & Validation
- Deep Learning Fundamentals
- Model Deployment Basics
- Reproducible Notebooks
- Technical Documentation & Reporting
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn
- TensorFlow
- Jupyter Notebook
- Git
- GitHub
- Performed comprehensive Exploratory Data Analysis (EDA) on real-world datasets.
- Built and evaluated supervised Machine Learning models.
- Applied feature engineering techniques to improve model performance.
- Learned the fundamentals of deep learning and deployment workflows.
- Created reproducible notebooks with well-documented analyses and reports.
- Developed data-driven solutions following industry best practices.
├── datasets/
├── notebooks/
├── models/
├── visualizations/
├── reports/
├── requirements.txt
└── README.md
Yathish Gowda C Computer Science Engineering Student | Data Science & Machine Learning Enthusiast
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Made with ❤️ by Yathish Gowda C