A repository for beginners to come and get accustomed to classical machine learning methods. This repository can act as a guide for beginners on how to structure their data science projects.
We will start with basic classification methods. For absolute beginners, Classification is a technique wherein the data (containing one or multiple features) is used to classify the entries into one of the different classes present. For eg, in our case we are working with the very popular (and equivalent to 'Hello World' in classification techniques) dataset called the Titanic Dataset (https://www.kaggle.com/competitions/titanic/data) from Kaggle.
In this repository, we will build from the very basic Logistic Regression to Support Vector Machines and all the way up to Deep Learning methods. Each classification method will have its own notebook for us to be able to observe the nuances in each method. Additionally, there will a separate notebook called 'comparison' that will compare the results of different methods and obtain inferences from it.
Please feel free to suggest any changes and Hopefully we all get to learn or takeaway something useful from this journey.