0. Machine Learning Preparation Projects

This course is a curated collection of both Supervised and Unsupervised Machine Learning content.
IMPORTANT: This course reuses material from two previous courses available on my Udemy profile. If you have already taken them, note that this is a combined and reorganized version, designed to offer a unified learning experience without switching between separate courses.
5. Practical Projects and Real Applications
Project 1: A/B Testing on Web Traffic-
In this project, you’ll work with real web traffic data from a company to evaluate the impact of two page versions on user conversion rates. You will learn to:

• Clean and explore traffic data using Pandas.
• Apply statistical tests like the t-test and hypothesis testing to compare conversion rates.
• Visualize results and make data-driven decisions.
• Create a report with actionable insights to optimize website performance.

Project 2: Bank Data Exploration for Machine Learning-
In this practical case, you’ll analyze a financial dataset containing bank client information to detect patterns and prepare data for future Machine Learning models. You’ll focus on:

• Data cleaning and transformation using Pandas and NumPy.
• Visual exploration with Seaborn to identify trends and correlations.
• Using descriptive statistics to understand client behavior.
• Preparing the dataset for predictive modeling.

Project 3: Insurance Data Analysis-
You will work with insurance company data to identify key factors affecting policy costs and client risk. You will learn to:

• Clean and transform customer and claim data.
• Apply EDA techniques to find trends and anomalies.
• Use descriptive stats and visualizations to support decisions.
• Prepare data for predictive risk models.

Project 4: Malware Data Analysis-
In this project, you will analyze a dataset containing malware information to detect cyberattack patterns and help prevent vulnerabilities. You will focus on:

• Cleaning and structuring malware data.
• Visual exploration to identify relevant features.
• Applying preprocessing techniques to improve data quality.