Udacity - Machine learning Nano Degree Program : Project-3
This is fourth project in the series of the projects listed in Udacity- Machine Learning Nano Degree Program.
A wholesale distributor in Lisbon, Portugal recently tested a change to their delivery method for some customers, by moving from a morning delivery service five days a week to a cheaper evening delivery service three days a week.Initial testing did not discover any significant unsatisfactory results, so they implemented the cheaper option for all customers. Almost immediately, the distributor began getting complaints about the delivery service change and customers were canceling deliveries — losing the distributor more money than what was being saved.
My goal was to to find what types of customers they have to help them make better, more informed business decisions in the future. I have used unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.
This project is designed to give me a hands-on experience with unsupervised learning and work towards developing conclusions for a potential client on a real-world dataset. Many companies today collect vast amounts of data on customers and clients, and have a strong desire to understand the meaningful relationships hidden in their customer base. Being equipped with this information can assist a company engineer future products and services that best satisfy the demands or needs of their customers.
Achievements:
- Preprocessed the data using PCA
- Clustering algorithm used : K-mean and Gaussian mixture model
- Achieved silhouette score of 0.4223 using Gaussian mixture model
Things i have learnt by completing this project:
- How to apply preprocessing techniques such as feature scaling and outlier detection.
- How to interpret data points that have been scaled, transformed, or reduced from PCA.
- How to analyze PCA dimensions and construct a new feature space.
- How to optimally cluster a set of data to find hidden patterns in a dataset.
- How to assess information given by cluster data and use it in a meaningful way.
- Project 0 : Titanic Survivals Prediction
- Project 1 : Boston's Houses Prediction
- Project 2 : Charity Donors Prediction
- Project 4 : Smart Cab
- Project 5 : ImageNetBot
- Project 6 : Stock Price Predictor
This project uses the following software and Python libraries: