Data Cleaning, Feature Engineering, and Dimensionality Reduction in Machine Learning

This is a project to showcase different data cleaning, feature preprocessing, and feature selection in machine learning. Each jupyter notebook itself is a standalone illustration of the technique covered in that notebook.

Dependencies

This project requires python and the following python libraries.

1. pandas
2. numpy
3. seaborn
4. matplotlib
5. scikit-learn

It also requires a software that can open and execute a Jupyter Notebook.

Installation

1. Clone the repo.
2. Download the necessary data from the Data section below for the required technoque.
3. Navigate to the top-level project directory that contains this readme file.
4. Go to Source_Codes directory.
5. Run the following command:

       jupyter notebook
    
   

6. This will open a tab on a web browser.
7. Click on the file for the dimensionality reduction technque that you are interested in.

Methods

1. Missing Values Imputation Techniques
2. Handling Categorical Data
3. Zero-Variance Feature Removal
4. Multicollinearity Removal
5. Tokenization, Stemming, and Lemmatization
6. Forward Elimination/ Bakward Elimination/ Stepwise Elimination