AutoML for a 3-Year-Old: Beat 13000+ Data Scientists with 10 Lines of Code in 5 minutes

Short Description

A no-code AutoML tool with a simple GUI—train a model in 10 minutes, beat 13,000+ Kaggle competitors with just 10 lines of code!

Overview

This tool makes machine learning so simple that even a 3-year-old could do it! With a beginner-friendly interface, you can train powerful models on Kaggle competitions like Titanic without writing any code. Just input a few details, and the tool will download data, train the model, and generate a ready-to-submit file.

Key Features

No Coding Needed: Simply use the GUI to select options—AutoGluon handles the rest.

• Fast & Powerful: Train models in as little as 5 minutes and get results that can compete with thousands of data scientists.
• Real-Time Feedback: Progress bar keeps you updated as your model trains.
• Auto Submission File: Automatically generates a submission.csv file ready for Kaggle.
• Multiple Evaluation Metrics: Choose from metrics like accuracy, AUC, F1, RMSE, and more to match your competition’s requirements.
• Automatic Dataset Handling: Automatically downloads and unzips Kaggle datasets—no manual steps required.
• Preset Flexibility: Easily choose between good_quality for fast training or best_quality for maximum performance.
• Dynamic Problem Type Detection: The tool automatically detects whether your problem is binary classification, multiclass classification, or regression—no need to manually configure.
• Time Limit Options: Set time limits from 5 minutes to 10 hours for optimal training duration based on your resources and needs.
• User-Friendly Customization: Input fields for competition name, label column, and ID column make it easy to adapt to any Kaggle competition.
• Versatile Problem Solving: Whether you’re tackling a simple binary classification like Titanic or a complex regression task, this tool adapts to various types of machine learning challenges.

Example: Beat 13,000 Data Scientists on Titanic in 5 Minutes

The Titanic - Machine Learning from Disaster competition on Kaggle is a classic binary classification problem. The goal is to predict whether a passenger survived or not based on various features such as their age, ticket class, gender, and more. With over 13,000 participants, it's a highly competitive challenge.

Using this tool, you can train a machine learning model on the Titanic dataset in just 10 minutes and achieve a top 4% ranking, beating thousands of data scientists—all with just 10 lines of code and no prior machine learning experience.

Here’s the code to get started:

run_kaggle_automl(
    competition_name='titanic',
    label_column='Survived',
    id_column='PassengerId',
    time_limit=600,  # 10 minutes
    presets='best_quality'
)