Step-by-Step Machine Learning Project Implementation: EDA, Feature Engineering, Selection, and Model Training

Dive into a comprehensive video tutorial on implementing a complete machine learning project, covering essential steps from exploratory data analysis to model hyperparameter tuning. Learn how to clean datasets, perform EDA and feature engineering, select relevant features, train models, and optimize their performance. Access the accompanying GitHub repository for hands-on practice and follow along with timestamped sections for each project phase, including data cleaning, feature selection, and model training.

Intorduction
Cleaning the dataset
EDA And Feature Engineering
Fetaure Selection
Model Training
Model Hyperparameter Tuning