INDE_577-Data-Science-and-Machine-Learning-Hanxuan-Lin

The lectures of INDE 577 are given by R. Davila by Rice University in the Fall semester of 2022. This repository includes all codes, readme files, and relevant instruction for implementing a particular algorithm onto a dataset. The algorithms will include supervised/unsupervised ML and a bit RL.

Content (include both Supervised learning and Unsupervised learning)

1. K-Nearest Neighbors
2. Gradient Descent
3. Linear Regression
4. Logistic Regression
5. Single Perceptron
6. Multi Layer Perceptron
7. Decision Trees
8. Random Forests
9. Ensemble Learning
10. K-Means Clustering
11. Principal Component Analysis(PCA)

Data sources

All the data used in these algorithms are from public database, such as LendingClub, UCI Machine Learning Repository[ https://archive.ics.uci.edu/ml/index.php], etc. And some random numerical data are generated as needed in the code.

Python packages

Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, Keras, TensorFlow, etc. (Programmed in Visual Studio)