This repository showcases various projects I have worked on, developed as part of my learning journey with Edvancer Eduventures. These projects cover a range of supervised and unsupervised learning techniques, using real-world datasets and practical problem-solving approaches.
- Goal: Predict the sales of counterfeit medicines.
- Model Used: Regression Techniques.
- Special Focus: Handling missing data and feature importance analysis.
- Goal: Predict customer interest in purchasing a caravan insurance policy.
- Model Used: Logistic Regression.
- Dataset: Socio-demographic and product usage data with over 5000 customer profiles.
- Data Preparation & Preprocessing: Handling missing values, scaling, encoding categorical variables, and dealing with multicollinearity.
- Machine Learning Techniques: Regression (Linear, Logistic), Tree-based models (Random Forest), Regularization (Ridge, Lasso).
- Evaluation Metrics: RMSE, Accuracy, AUC-ROC, Confusion Matrix.
- Tools & Platforms: Python, R, Tableau, SQL, Jupyter Notebook, RStudio.
Edvancer Eduventures provided me with comprehensive training in Artificial Intelligence and Machine Learning, covering topics such as Python, Regression and Classification techniques. These projects reflect the practical, hands-on approach emphasized during the learning experience.