A Look at the Project Ahead

• Problem formulation: defining the problem and understanding the business context.
• Data collection: gathering and acquiring data from various sources.
• Data preprocessing: cleaning, transforming, and preparing data for analysis.
• Exploratory data analysis: understanding the structure, contents, and patterns in the data through visualizations and summary statistics.
• Feature engineering: creating new features from the existing data.
• Model selection: choosing an appropriate model or algorithm for the problem.
• Model training: fitting the model to the data and tuning its hyperparameters.
• Model evaluation: assessing the performance of the model using appropriate metrics and techniques.

Learning Objectives

• Learn the full machine learning project cycle, from data exploration to model evaluation.
• Gain hands-on experience with data cleaning and preparation techniques.
• Understand the importance of feature engineering and how to apply it to real-world datasets.
• Learn how to select and train machine learning models for different problems.
• Learn how to make predictions and draw insights from data using machine learning algorithms.
• Understand the working mechanisms of three famous boosting algorithms: CatBoost, LightGBM, and XGBoost.
• Discover how to use ensemble learning techniques to combine model predictions and achieve even better results.