# Data_Science_and_Machine_learning_Elements

**Repository Path**: Arhatking/Data_Science_and_Machine_learning_Elements

## Basic Information

- **Project Name**: Data_Science_and_Machine_learning_Elements
- **Description**: Conduct model evaluation, model selection and feature selection,PCA and K-Means clustering. I also generate recommendations systems based on Latent Dirichlet Approximation
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-12-05
- **Last Updated**: 2020-12-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Data_Science_and_Machine_learning_Elements

Project 1: Data transforming and calculating summary statistics and a few visualizations

Project 2: A hypothesis test and cleaning some data before training a regression model.

Project 3: Performing model evaluation, model selection and feature selection in both a regression and classification setting

Project 4: There two parts here

In Part 1, I use PCA to plot a high dimensional dataset in 2D. I then perform K-Means clustering on the dataset and plot using the same PCA representation to see how clustered our data is in the original 64D space.

In Part 2, I generate recommendations on products from a department store based on product descriptions.
I first transform the data into topics using Latent Dirichlet Approximation, and then generate recommendations based on this new representation.