0. 機械学習準備プロジェクト

Real-World Data Science Projects for Clients & Products

This course offers a carefully curated collection of content covering both supervised and unsupervised learning techniques, grounded in practical application. Important: This course leverages materials from my previous two Udemy courses. If you've already taken those, this is a consolidated and reorganized version designed to provide a seamless and consistent learning experience – no need to jump between separate courses!

Dive into hands-on projects that tackle real-world data challenges:

Project 1: A/B Testing for Web Traffic

Apply data science to optimize website performance using real-world web traffic data from actual companies. You'll learn how to:

• Clean and Explore Traffic Data using Pandas.
• Apply statistical methods like t-tests and hypothesis testing to analyze results.
• Visualize Data effectively and make data-driven decisions.
• Create actionable insights and reports to optimize website performance.

Project 2: Exploring Banking Data for Machine Learning

Analyze a financial dataset containing bank client information to uncover patterns and prepare for future machine learning models. Focus areas include:

• Data Cleaning and Transformation using Pandas and NumPy.
• Identifying trends and correlations through visual exploration with Seaborn.
• Understanding customer behavior using descriptive statistics.
• Preparing datasets for predictive models.

Project 3: Insurance Data Analysis

Leverage insurance company data to identify key factors influencing policy costs and client risk. You will:

• Clean and Transform Client & Claim Data.
• Apply exploratory data analysis (EDA) techniques to find trends and outliers.
• Utilize descriptive statistics and visualizations to support decision-making.
• Prepare data for risk prediction models.

Project 4: Malware Data Analysis

Analyze a malware dataset to detect cyberattack patterns and contribute to vulnerability prevention. Key skills you'll develop:

• Clean and Structure Malware Data.
• Identify relevant features through visual exploration.
• Apply preprocessing techniques to improve data quality.