What is Kaggle?

While Kaggle started with supervised learning competitions (like predicting house prices or Titanic survival), it now supports the entire range of data science, machine learning, and AI workflows. Here's the full scope of what Kaggle is used for:

✅ 1. Supervised Learning

Most common, yes — but just one part.

🏠 Regression (e.g., house prices)
🧍 Classification (e.g., Titanic survival, spam detection)

❓ 2. Unsupervised Learning

You’ll find notebooks and datasets for:

📦 Clustering (e.g., customer segmentation)
🌐 Dimensionality reduction (e.g., PCA for image compression)

🤖 3. Deep Learning Tasks

With TensorFlow, PyTorch, Keras — you’ll see:

🖼️ Image classification (e.g., cats vs. dogs)
🗣️ NLP (sentiment analysis, summarization, text generation)
🎵 Audio/speech recognition
🧠 LLMs and transformers (fine-tuning BERT, GPT, etc.)

🕹️ 4. Reinforcement Learning

While rarer than other categories, there are:

🐍 Notebooks using OpenAI Gym environments
🏁 Path-planning, game AI, and Q-learning projects

📈 5. Time Series & Forecasting

You’ll find:

📅 Stock price prediction
🦠 COVID-19 case forecasting
⛅ Weather prediction

Often includes tools like:

statsmodels
prophet
LSTM/RNN models

🔬 6. Exploratory Data Analysis (EDA) Projects

No modeling — just visual exploration:

Seaborn/Matplotlib visual storytelling
Finding insights in sports, economy, or demographic data

🏗️ 7. Data Engineering + Preprocessing

Examples:

Data cleaning pipelines
Missing value treatment
Feature engineering recipes
Efficient I/O (e.g., feather, parquet formats)

🧪 8. Real-World Applications

Kaggle now has "Code Competitions" and "Notebooks" on:

🔍 Document search (IR, vector DBs)
🧬 Biology (protein folding, cancer detection)
🛒 Recommender systems
🧾 PDF parsing, OCR, and web scraping

📚 9. Learning + Community

Not just competitions:

Kaggle Learn: mini-courses (Python, ML, SQL, etc.)
Public notebooks: like StackOverflow, but for data workflows
Discussions: Q&A, guides, updates

Domain	Examples
Supervised ML	Titanic, Housing, Spam
Unsupervised ML	Clustering, PCA
Deep Learning	CNNs, NLP, LLMs
Reinforcement Learning	Q-Learning, OpenAI Gym
Time Series Forecasting	Prophet, ARIMA, LSTM
EDA & Data Cleaning	Visual stories, missing data hacks
Data Engineering	Joins, transforms, pipelines
Real-world AI Apps	Recommenders, OCR, Chatbots