Statistical Learning Project Report

This report is regarding to the project for Statistical Learning Course in Shahid Beheshti University, Faculty of Mathematics and Computer Science. This report covers the following contents:

Data

To learn about the data set please follow these links.

• Dataset Description
• Dataset Web site

Dataset for clustering is:

• Clustering Dataset Web site

Results

To view result please follow these links:

• Regression Results (project #1)
• Classification Results (project #2)
• SVM Results (project #3)
• Clustering Results (project #4)
• Neural Network Results (project #5)

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Requirements

This project is developed on Python. In order it you will need the following python-packages installed. You can download and install them via pip tool.

• numpy
• sklearn
• scipy
• metrics
• pandas
• keras (for running NN)
• tensorflow (for running NN)

Runing Project

In order to run the project, inside the project root folder call the run.py with a project name argument. By running each project it will run and illustrate its results.

python run.py PROJECT_NAME

Where PROJECT_NAME could be one of these:

• classification or cl for running Classification
• bench-mark or bm for running Bench-Mark
• feature-selection or fs for running Feature Selection on regression data
• linear-regression or lr for running Linear Regression
• reg-all for running all Regression based modules
• kmeans or km for running k-means clustering
• hierarchical-clustering or hc for running Hierarchical Clustering (Agglomerative-Clustering)
• neural-networks or nn for running Neural Network Classification

You can also run these commands for running projects based on the part they’re described in:

• p1 for running part #1 (Regression)
• p2 for running part #2 (Classification)
• p3 for running part #3 (SVM)
• p4 for running part #4 (Clustering)
• p5 for running part #5 (Neural Network)

Report

In order to read more about this project please follow this link: https:/ArefMq/ML-Project/blob/master/report.pdf