This repository is a collection of Jupyter notebooks that showcase various models, their exploratory data analysis, and implementation. Each notebook delves into a specific machine learning algorithm, presenting a step-by-step explanation of the entire process.
In the exploratory data analysis section, I explore the dataset's characteristics, including data types, missing values, and distributions. Visualizations such as histograms, scatter plots, and heatmaps provide valuable insights into the data's underlying patterns and relationships.
Following the exploratory phase, I proceed to implement the chosen machine learning algorithm. This involves data preprocessing, feature engineering, model training, hyperparameter tuning, and performance evaluation. The implementation is presented in a clear and concise manner