This repository is related to the NBA topic and Machine Learning.
The repository includes the following .ipynb files:
This notebook outlines the primary goals and objectives of the analysis.
This file is dedicated to the data collection process. It includes steps to gather, clean, and prepare NBA data from various sources for analysis.
The first part of data exploration is dedicated to the exploration of the NBA SQLite database.
The second part of data exploration is dedicated to the exploration of the NBA PostgreSQL database, collected over API.
The third part of data exploration is dedicated to the identification of patterns, trends, and statistical analysis.
This notebook focuses on implementing and evaluating linear and polynomial regression models, exploring relationships between various features in the NBA data.
This notebook focuses on implementing and evaluating models like the decision tree, random forest, gradient boosted trees, and XGBoost.
This notebook focuses on neural network models, applying techniques like various architectures, dropout, regularization, learning rate reduction, and early stopping.
This notebook focuses on clustering with two or more features, employing the elbow method and K-means.
This notebook is dedicated to anomaly detection with two or more features, using the Elliptic Envelope and Gaussian Mixture Model.
Additionally, there are a few folders:
- The
figuresfolder contains images for some.ipynbfiles. - The
reportsfolder holds data profiling reports that can be opened in any browser. - The
sql_scriptsfolder includes several useful SQL queries related to the PostgreSQL Database. - The
utilsfolder contains several Python files that are excluded from.ipynbfiles to avoid overloading them with code. Links to these files are included in the.ipynbfiles.