Kaggle Competitions Intermediate

Competitions are at the heart of Kaggle. Companies and researchers post real-world ML problems, and the community competes to build the best models. Learn about competition types, how they work, and strategies to succeed.

Competition Types

Type	Difficulty	Prizes	Description
Getting Started	Beginner	Knowledge only	Permanent competitions for learning (Titanic, Housing Prices, Digit Recognizer)
Playground	Beginner-Intermediate	Swag / Small prizes	Fun, lower-stakes competitions for practice and skill building
Featured	Intermediate-Advanced	$10K - $1M+	Real business problems from companies with significant cash prizes
Research	Advanced	Varies	Academic and scientific problems pushing the boundaries of ML
Community	Varies	Medals / Swag	User-organized competitions hosted on the platform

How Competitions Work

1. Join and Download Data

   Accept the competition rules, then download the training data (train.csv), test data (test.csv), and sample submission file.

2. Explore and Build

   Perform EDA on the training data, engineer features, and train your ML model.

3. Generate Predictions

   Use your model to predict on the test set and create a submission file matching the required format.

4. Submit and Score

   Upload your submission CSV. Kaggle scores it against a hidden portion of the test data and shows your public leaderboard score.

5. Iterate

   Improve your model, try new features, tune hyperparameters, and submit again (limited submissions per day).

Evaluation Metrics

Each competition specifies its evaluation metric. Common ones include:

Metric	Task Type	Description
Accuracy	Classification	Percentage of correct predictions
AUC-ROC	Binary Classification	Area under the receiver operating characteristic curve
Log Loss	Classification	Negative log-likelihood of correct class probabilities
RMSE	Regression	Root mean squared error between predicted and actual values
MAE	Regression	Mean absolute error between predicted and actual values
F1 Score	Classification	Harmonic mean of precision and recall
MAP@K	Ranking/Retrieval	Mean average precision at K for recommendation tasks

Submission Format

Submissions are typically CSV files with an ID column and prediction column(s):

# Example: Titanic competition submission
import pandas as pd

# Generate predictions
test = pd.read_csv('test.csv')
predictions = model.predict(test_features)

# Create submission file
submission = pd.DataFrame({
    'PassengerId': test['PassengerId'],
    'Survived': predictions
})
submission.to_csv('submission.csv', index=False)

# Submit via API
# kaggle competitions submit -c titanic -f submission.csv -m "Random Forest v2"

Team Formation

• Most competitions allow teams of 2-5 members
• Teams can merge during the competition (before the merger deadline)
• Team members share submissions and leaderboard position
• Prizes are split among team members
• Find teammates through the competition's Discussion tab

Notable Competitions

Competition	Type	Significance
Titanic	Getting Started	The most popular beginner competition; binary classification on passenger survival
Housing Prices	Getting Started	Classic regression problem predicting house prices in Ames, Iowa
Digit Recognizer	Getting Started	MNIST handwritten digit classification; great for learning neural networks
ImageNet	Research	Landmark competition that drove breakthroughs in computer vision and deep learning
Google QUEST Q&A	Featured	NLP competition for understanding question quality

Winning Strategies