Supervised Machine Learning - Fibre Content Regression
Project Overview
- In this project, a training dataset and a testing dataset are given. The data contains a 2-dimensional label which denotes the cotton and the polyester content in the fabric respectively.
- We will build and train 5 different regression models to determine which models gives the best performance in terms of prediction error and efficiency.
- The regression models that are used in this project include: Elastic Net, Lasso (L1), Ridge (L2), Multi-Layer Perceptron Neural Network(MLPNN), Support Vector Regression (SVR)
- Read the full analysis here
Python - Personal Loan Analysis
Project Overview
- This case is about a bank (Thera Bank) whose management wants to explore ways of converting its liability customers to personal loan customers.
- We found that an individual’s annual income and the number of family members are the most important factors in determining whether an individual accepts a personal loan from the bank.
- The libraries involved in the project include: Pandas, Matplotlib, Seaborn, and Plotly
- Read the full analysis here
