This repository contains my work on various business case studies completed during my Data Science and Machine Learning course at Scaler Academy. Through these projects, I've applied different analytical techniques to solve real-world business problems.
Focus: Logistic Regression
- Developed a credit risk assessment model
- Predicted loan default probabilities
- Implemented feature selection techniques
- Evaluated model performance using ROC-AUC and precision-recall metrics
Focus: Linear Regression
- Built a model to predict student exam scores
- Performed feature engineering and selection
- Analyzed factors affecting academic performance
- Implemented various regression techniques
Focus: Feature Engineering
- Created meaningful features from raw delivery data
- Applied advanced feature engineering techniques
- Handled temporal and geographical data
- Improved delivery time predictions
Focus: Confidence Interval and Central Limit Theorem
- Analyzed sales patterns and seasonality
- Applied statistical inference techniques
- Constructed confidence intervals
- Made data-driven business recommendations
Focus: Descriptive Statistics & Probability
- Performed customer segmentation analysis
- Created detailed customer profiles
- Applied probability concepts
- Generated actionable marketing insights
Focus: Data Exploration and Visualization
- Analyzed content distribution patterns
- Created interactive visualizations
- Identified viewing trends
- Made content strategy recommendations
Focus: SQL
- Wrote complex SQL queries for data analysis
- Performed data aggregation and transformation
- Generated business insights from transaction data
- Created performance dashboards
- Programming: Python, SQL
- Data Analysis: Pandas, NumPy, SciPy
- Machine Learning: Scikit-learn
- Visualization: Matplotlib, Seaborn, Plotly
- Development: Jupyter Notebook
- Version Control: Git, GitHub
Each case study includes:
- 📓 Jupyter notebook with detailed analysis
- 📑 PDF describing the business problem
- 🔍 Methodology documentation
- 📊 Visualizations and insights
- 💡 Business recommendations
Through these projects, I've developed:
- Strong foundation in statistical analysis
- Proficiency in machine learning algorithms
- Data preprocessing and feature engineering skills
- Business problem-solving capabilities
- Data visualization expertise
- SQL query optimization skills
Due to confidentiality agreements, the actual datasets used in these analyses are not included in this repository. However, the notebooks and documentation provide comprehensive information about:
- Data structure and characteristics
- Analysis methodology
- Implementation details
- Results and insights
- LinkedIn: https://www.linkedin.com/in/rengarajan-g/
- Email: [[email protected]]
- Portfolio: [In progress]
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