Chapter 0: Experience practical techniques on Kaggle!

0.1 Let's dive into the world of Kaggle!
__0.1.1 What can we learn from data and data analysis?
__0.1.2 Kaggle: A data analysis competition platform used worldwide.
__0.1.3 How to use this book

Chapter 1: What is Kaggle?

1.1 A platform where data scientists around the world compete
1.2 Kaggle Medals and Ranks
1.3 Kaggle Competition Participation Flow
1.4 Types of Contests
1.5 Kaggle Community

Chapter 2: Data Analysis Procedures and Building a Data Analysis Environment

2.1 Data Analysis Sequence and Overview
2.2 Data Analysis Environment
__2.2.1 Data analysis environment using Python
__2.2.2 Data analysis environment on local or cloud
2.3 Interactive data analysis environment using Jupyter Notebook
__2.3.1 Building a data analysis environment on a local computer
2.4 Using Anaconda's Virtual Environment (Windows)
2.5 Using the pyenv environment (Mac)
2.6 Using Kaggle Analysis Tools

Chapter 3 Kaggle Competition Challenge ①: Titanic Survivor Prediction

3.1 Analyzing Real Data Using Kaggle
3.2 What is the Titanic Survivor Prediction Contest?
3.3 Downloading Data
3.4 Preparing for Data Analysis
__3.4.1 [Step 1] Preparing the data analysis environment
__3.4.2 [Step 2] Creating a new file
__3.4.3 [Step 3] Check the directory structure
__3.4.4 [Step 4] Installing and Importing Libraries
__3.4.5 [Step 5] Reading Data
__3.4.6 [Step 6] Setting the random seed
3.5 Understanding the Data Overview
3.6 Data Visualization
__3.6.1 Visualization of data related to the target variable
3.7 Data preprocessing and feature value generation
3.8 Machine Learning Modeling
__3.8.1 Verifying prediction accuracy with validation data
__3.8.2 Learning using cross-validation
3.9 Submitting your results to Kaggle
3.10 Various analyses other than accuracy
__3.10.1 Additional Analysis ①: Who was on board the Titanic?
__3.10.2 Additional Analysis ②: Focus on Specific Clusters

Chapter 4 Kaggle Competition Challenge ②: House Price Prediction

4.1 Data Analysis in More Detail
4.2 What is the House Price Prediction Contest?
4.3 Downloading Data
4.4 Creating a Baseline for Benchmarks
__4.4.1 Prediction with LightGBM
__4.4.2 Model training and prediction using cross-validation
__4.4.3 Check the importance of each variable
4.5 Preprocessing of target variables: Checking the distribution of target variables
__4.5.1 Checking the distribution of SalePrice data
4.6 Preprocessing explanatory variables: Checking for missing values
__4.6.1 Checking missing values ​​for each explanatory variable
4.7 Excluding outliers
__4.7.1 What is an outlier?
__4.7.2 Check the data distribution of each explanatory variable
4.8 Identifying explanatory variables: Generating feature values
4.9 Hyperparameter Optimization
__4.9.1 Optuna implementation
__4.9.2 Submitting results to Kaggle
4.10 Ensemble using various machine learning methods
__4.10.1 Training with Random Forest
__4.10.2 Deleting missing values ​​in LotFrontage
__4.10.3 Training with XGBoost
__4.10.4 Combining XGBoost and LightGBM Results
4.11 Additional Analysis ①: Cluster Analysis Using Statistical Techniques
__4.11.1 Classifying houses using statistical techniques
__4.11.2 Principal Component Analysis
4.12 Additional Analysis ②: Analyzing and Visualizing the Conditions of Luxury Housing
__4.12.1 Visualizing with a Decision Tree

Chapter 5: Tips for Improving New Data Science Skills

5.1 Special Interview with a Kaggle Master
5.2 Kaggle Recommended Starter Notebooks
__5.2.1 Sales Prediction Contest
__5.2.2 PUBG Final Ranking Prediction (Kernel Only) Contest
__5.2.3 IEEE-CIS Fraud Detection Competition
5.3 GCP's AI Platform Analysis Procedure
__5.3.1 About GCP's AI Platform
__5.3.2 Using GCP
__5.3.3 Uploading Data to GCP
__5.3.4 Using GCP's AI Platform
__5.3.5 Using Storage Data on GCP's AI Platform
__5.3.6 Added new library
__5.3.7 Stop using the instance

Appendix A: Becoming a Kaggle Master: Life as a Full-Time Kaggler and the Top 3 Earthquake Prediction Solutions
A.1 Self-introduction
A.2 One and a half years as a full-time Kaggler
__A.2.1 Why I Became a Full-Time Kaggler
__A.2.2 One year after becoming a full-time Kaggler
__A.2.3 Six months to aim for Kaggle Grandmaster
__A.2.4 Reflecting on a year and a half as a full-time Kaggler
A.3 LANL Earthquake Prediction 3rd Place Solution
__A.3.1 Overview of the Contest
__A.3.2 Training data
__A.3.3 Test Data
__A.3.4 Learning Methods
__A.3.5 Top Prize Key
__A.3.6 Leaderboard Probing
__A.3.7 Score Changes
__A.3.8 Best Model on the Public Leaderboard
__A.3.9 Host Paper
__A.3.10 Are the data used in the paper and the competition data the same?
__A.3.11 Combine the organizer's paper information with leaderboard probing information

Appendix B: Interview with a Korean Kaggle Grandmaster: Lee Yu-han
B.1 Interview Introduction
B.2 Interview Contents

How to successfully enter Kaggle, the world's leading data analysis competition, using Python!

Covers everything you need to get started with Kaggle!

Kaggle is a global data analysis competition platform that provides a space for users to measure and improve their data analysis skills, as well as a community for discussion and code sharing.
The book explains what Kaggle is, the types of Kaggle competitions, medals and levels, and how to enter competitions.
Additionally, before participating in the competition, learn the order in which data analysis will be conducted and create an environment suitable for data analysis.
We explain the basic procedures of Kaggle competitions and the knowledge required for data analysis so that even beginners can participate in the competition.

Learn the data analysis process by solving competition challenges!

Participate in Kaggle's flagship beginner tutorial competitions to build highly accurate models step by step, and even apply different methods to the same data to approach problems.
You can learn data analysis and machine learning firsthand by solving actual competition challenges.
Additionally, through this process, you can acquire data analysis skills that can be applied to your work or build a foundation for aiming for the top ranks of Kaggle.

Let's learn about the things you've always wondered about through the experiences of Kaggle Masters and Grandmasters!

We included the experiences of two Japanese Kaggle Masters and one domestic Kaggle Grandmaster.
This article will walk you through your journey to becoming a Kaggle Master/Grandmaster, including how you got started on Kaggle, how you studied to become a Kaggle Master/Grandmaster, and how you can connect Kaggle with your work.
Through their vivid experiences, let's learn about things you've always wondered about Kaggle and various tips.