Hands-on Machine Learning

This repository contains my Machine Learning projects.

1. Performance measure: different metrics to measure model performance and compare different classification models.
2. DecisionTree Classification : its a project on the popular kaggle dataset https://www.kaggle.com/datasets/pablomgomez21/drugs-a-b-c-x-y-for-decision-trees

Problem statement: You have collected data about a set of patients, all of whom suffered from the same illness. During their course of treatment, each patient responded to one of 5 medications, Drug A, Drug B, Drug c, Drug x and y.Build a model to find out which drug might be appropriate for a future patient with the same illness. The features of this dataset are Age, Sex, Blood Pressure, and the Cholesterol of the patients, and the target is the drug that each patient responded to.

3. Random Forest : a project that touches upon Random Forest, and GridSearchCV for hyperparameter tuning.
4. Classification : a basic project on Iris classification that touches seaborn, logisticregression, matplotlib, etc for representation and accuracy testing.

Problem statement : An individual’s annual income results from various factors. Intuitively, it is influenced by the individual’s education level, age, gender, occupation, and etc. We can explore the possibility in predicting income level based on the individual’s personal information. https://www.kaggle.com/datasets/wenruliu/adult-income-dataset