This repository contains a collection of hands-on projects and assignments completed as part of an Applied Machine Learning (AML) course. The projects demonstrate practical implementation of machine learning concepts, end-to-end ML workflows, and model evaluation using Python and Jupyter Notebooks.
- Data preprocessing and exploratory data analysis (EDA)
- Feature engineering and data transformation
- Supervised learning (regression and classification)
- Unsupervised learning (clustering)
- Model evaluation and performance metrics
- Visualization and result interpretation
- Programming Language: Python
- Libraries: scikit-learn, pandas, NumPy, matplotlib
- Environment: Jupyter Notebook
Each notebook in this repository corresponds to an individual AML assignment or mini-project.
The notebooks follow a structured approach:
- Problem understanding
- Data exploration and preprocessing
- Model selection and training
- Evaluation and analysis of results
Through these projects, I gained hands-on experience in:
- Implementing machine learning algorithms on real-world datasets
- Designing reproducible ML pipelines
- Evaluating and comparing models using appropriate metrics
- Translating problem statements into data-driven solutions