Machine Learning Modeling

Supervised and unsupervised machine-learning notebooks from TC2034 - Modelación del Aprendizaje con Inteligencia Artificial (Tecnológico de Monterrey). Each notebook takes a problem end to end: data understanding, preprocessing, modeling, tuning and evaluation.

Notebooks

#	Notebook	What it covers
1	1_linear-regression.ipynb	Linear Regression - exploratory analysis, train/test split, fitting LinearRegression, error metrics and result visualization.
2	2_supervised-learning-depression-prediction.ipynb	Supervised classification predicting student depression - preprocessing & scaling, Logistic Regression, Random Forest, SVM, hyperparameter tuning with GridSearchCV / RandomizedSearchCV, cross-validation, ROC curves and confusion matrices.
3	3_unsupervised-learning-clustering.ipynb	Unsupervised clustering - Hierarchical/Agglomerative clustering and KMeans, feature scaling, silhouette score, elbow method (KneeLocator), dendrograms and interactive Plotly visualizations.

Tech stack

Python · pandas · NumPy · scikit-learn · SciPy · Matplotlib · seaborn · Plotly · kneed

Setup

pip install -r requirements.txt

Datasets are not included in this repository.

Author

Steffany Lara - Data Science & Mathematics Engineering, Tecnológico de Monterrey.