DLBDSMLUSL01 - Unsupervised Machine Learning and Feature Engineering

Task 1: Mental Health in Technology-related jobs

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Goal:

The goal of this project is to analyze a large dataset containing information about mental health in technology-related companies. The evaluation requires using Unsupervised Machine learning techniques to form clusters of similar participants. Once the clusters are formed, visualizations of the clusters and their profiles are used to provide deeper understanding of the main principals of the data set.

Project Format

3 notebooks were used in order to improve readability and separate stages of analysis.

1. Data Cleaning

data_cleaning = 'Task1DataCleaning.ipynb'

2. Encoding, Scaling, PCA, and K-Means

data_analysis = 'Task1DataAnalysis.ipynb'

3. Feature Importance

data_feature_importance = 'Task1FeatureImportance.ipynb'

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For optimum results if downloading the entire folder please use 'run_notebooks.py'.

In your terminal enter:

python run_notebooks.py

This will ensure all notebooks are run in sequential order.

For required packages please see 'requirements.txt'