Artificial Intelligence: Machine Learning A Comprehensive Study

Machine learning is one of the core fields of Artificial Intelligence (AI) that focuses on

enabling computers to learn patterns from data and make decisions based on those

patterns. In this project, we studied several supervised learning models, including KNearest Neighbors (KNN), Support Vector Machines (SVM), Decision Trees (DT),

and Logistic Regression (LR). All models were trained and tested using the same

dataset, allowing for a fair comparison of their performance. The models were evaluated

and compared primarily based on their accuracy on the training data. Before training the

models, data pre-processing steps were applied to ensure quality and consistency of

the dataset.

These steps included Cleaning the data, handling missing values, inconsistent

values, encoding categorical features, and scaling numerical features where

necessary. These steps helped light the way to improved model performance and

ensured that the comparison between the different models were reliable and

meaningful.