This repository contains a series of data science projects focusing on Dimensionality Reduction and Clustering techniques, along with an implementation of Dimensionality Reduction using Databricks. Each project is designed to demonstrate key data science skills and methodologies.
This project demonstrates the application of Dimensionality Reduction techniques on a complex dataset. The main goal is to reduce the number of features while retaining the essential information, thereby simplifying the model without significant loss of information.
- PCA (Principal Component Analysis)
- t-SNE (t-Distributed Stochastic Neighbor Embedding)
This project extends the dimensionality reduction approach by integrating clustering algorithms. Post-dimensionality reduction, the dataset is subjected to clustering to identify inherent groupings within the data.
- PCA for Dimensionality Reduction
- K-Means Clustering
A specialized project that leverages the power of Databricks for Dimensionality Reduction. This project showcases how Databricks can be effectively utilized for handling large-scale data and complex computations.
- Integration with Databricks
- Advanced Dimensionality Reduction
To run these notebooks, click on the 'Open In Colab' badges. This will take you to Google Colab, where you can run the notebooks in an interactive environment.
Ensure you have the necessary dependencies installed: