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Setting Up a Local Environment for the Kaggle Titanic Competition

Kaggle Practice 1: Setting Up a Local Environment for the Kaggle Titanic Competition
Kaggle Practice 2: First Submission
Kaggle Practice 3: Feature Engineering for Cabin
Kaggle Practice 4: Feature Engineering (Imputing Age with Random Forest)

https://www.kaggle.com/c/titanic

Available on GitHub

Abstract

  • Set up a local development environment for the Kaggle Titanic competition.

Introduction

Kaggle Notebooks are convenient, but local development offers faster iteration, better IDE support, and easier version control with Git.
I've been participating in the Kaggle Titanic competition and trying to improve my score. However, logging into Kaggle every time I wanted to submit an experiment quickly became tedious.

Environment Summary

Surprisingly, all I really needed was Python.

Item Value
Python 3.14.6
Virtual Env .venv (venv)
Project Path 51_googleantigravity\1st_
pip 26.1.x

Pitfalls/Things to Keep in Mind

I ran into a small issue during setup, so I'll document it here.

Crucial Point:

  • Avoid using non-ASCII characters (such as Japanese characters) in the virtual environment path. C extension libraries (DLLs) such as scipy and scikit-learn failed to load properly when the path contained non-ASCII characters.

1. Step-by-Step Guide

1.1. Installing Python

Download and install from the Official Python Release Page.

Scroll down to the bottom of the page to find the installer.

Also, add Python to your PATH:

setx PATH "%PATH%;<Python_Installation_Folder>"
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1.2. Creating the Project Folder

Create it anywhere you like on your PC.

mkdir 51_googleantigravity\1st_
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1.3. Creating the Virtual Environment

Create a Python virtual environment:

# Move to the project folder
cd 51_googleantigravity\1st_
python.exe -m venv .venv
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Structure after creation:

1st_/
└── .venv/
    ├── Scripts/
    │   ├── activate
    │   ├── python.exe
    │   └── pip.exe
    └── Lib/
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1.4. Installing Packages

Install various packages required for the Python code:

python.exe -m pip install --upgrade pip pandas numpy scikit-learn matplotlib seaborn jupyter kaggle xgboost lightgbm
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Major packages installed:

Package Version Purpose
pandas 3.0.3 Data manipulation
numpy 2.5.0 Numerical calculations
scikit-learn 1.9.0 Machine learning
matplotlib 3.11.0 Data visualization
seaborn 0.13.2 Statistical data visualization
xgboost 3.3.0 Gradient boosting
lightgbm 4.6.0 Gradient boosting
jupyter / notebook 7.6.0 Jupyter Notebook environment
kaggle 2.2.3 Kaggle CLI

1.5. Directory Structure

Create directories for datasets, notebooks, and outputs:

mkdir data\raw
mkdir data\processed
mkdir notebooks
mkdir submissions
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Organized folder structure:

1st_/
├── .venv/
├── data/
│   ├── raw/          ← Kaggle raw data
│   └── processed/    ← Visualizations and preprocessed data
├── notebooks/        ← Notebook files
└── submissions/      ← Submission CSV files
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1.6. Creating requirements.txt 

python.exe -m pip freeze > requirements.txt
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1.7. Creating the Notebook

Create notebooks/titanic_eda.ipynb with the following structure:

Cell Content
1 Import libraries
2 Load data (train.csv / test.csv)
3 Exploratory Data Analysis (missing values, survival rate, age distribution, correlation heatmap)
4 Feature Engineering (extracting titles, imputing age, family size, etc.)
5 Model training
6 Generate submission file (output to submissions/submission.csv)

1.8. Launching Jupyter Notebook

Start the notebook server:

# Move to project directory
cd 51_googleantigravity\1st_
# Activate virtual environment
.venv\Scripts\activate
# Start notebook
jupyter notebook notebooks\titanic_eda.ipynb
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Your browser should open automatically. If not, copy the URL displayed in the terminal and paste it into your browser.

1.9. Preparing Kaggle CLI

  1. Go to Kaggle Settings -> "API Tokens" tab.
  2. Click "Create Legacy API Key" under the Legacy API Credentials section.
  3. Place the downloaded kaggle.json file at C:\Users\<your_username>\.kaggle\kaggle.json.
  4. Run the following: 
# Activate virtual environment
.venv\Scripts\activate
# Download Titanic data using Kaggle API
kaggle competitions download -c titanic -p data\raw
# Extract ZIP (using standard Windows tar command)
tar -xf data\raw\titanic.zip -C data\raw
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Alternatively, you can download files manually:

Download the following 3 files from https://www.kaggle.com/c/titanic/data and place them in data\raw\:

  • train.csv
  • test.csv
  • gender_submission.csv

1.10. Submitting to Kaggle 

kaggle competitions submit -c titanic -f submissions\submission.csv -m "Your comment"
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That's it! You now have a fully functional local environment for experimenting with Kaggle Titanic and submitting results directly from your machine.

Japanese version:
Kaggle Practice 1: Setting up Kaggle Titanic Environment on a Local PC
Kaggle Practice 2: First Submission
Kaggle Practice 3: Feature Engineering for Cabin
Kaggle Practice 4: Feature Engineering (Imputing Age with Random Forest)

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