This is a repository that contains project's files, where I gain hands-on practice through Machine Learning Projects.
Many of the code used in these files are adapted from the following source:
- machinelearningmastery.com
- scikit-learn.org
- Aurélien Géron,
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, Oreilly, 2019. - Ankur A. Patel,
Hands-On Unsupervised Learning Using Python, Oreilly, 2019.
Concrete is one of the most important material in civil engineering. For example, it is used for the development of foundations, columns, beams, slabs, and different load-bearing components. The concrete compressive strength (CCS) is a highly nonlinear function of age and ingredients. In this project, our goal is to predict the CCS.
A personal loan allows you to borrow money from a lender for almost any purpose, typically with a fixed-term, a fixed rate, and a regular monthly schedule. In this project, we use data from Lending Club. Investors earn money through interest paid on the loans, and Lending Club makes money from loan origination fees and service charges. Our goal is to perform group segmentation on the loan dataset, i.e., identify the underlying structure and group clients based on similarity. This work is adapted from "Ankur A. Patel, Hands-On Unsupervised Learning Using Python, Oreilly, 2019".
Central banks incorporate various security features in their banknotes to enable the general public, retailers, professional cash handlers and central banks to detect counterfeits. Authenticating whether a banknote is real or not is one of the most common tasks in the banking industry. In this project, we use classification methods to model the probability that a banknote is genuine or forged, as a function of its features.