This project showcases practical applications of machine learning by building models that can analyze, predict, and extract insights from data. It includes the full workflow from data preprocessing and exploration to model training, evaluation, and visualization of results. The project demonstrates both supervised and unsupervised learning techniques using popular Python libraries.
- Data preprocessing and cleaning
- Exploratory data analysis (EDA)
- Implementation of supervised learning models:
- [Example: Linear Regression, Decision Trees, Random Forests, etc.]
- Implementation of unsupervised learning models:
- [Example: K-Means Clustering, PCA, etc.]
- Model evaluation and performance metrics
- Visualization of results
- Python 3.10+
- Libraries:
pandas– Data manipulationnumpy– Numerical computationsscikit-learn– Machine learning algorithmsmatplotlib/seaborn– Data visualization- Jupyter Notebook