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README.md
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num_examples: 454
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- name: sample
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num_bytes: 126488.84698275862
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num_examples: 50
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download_size: 5161799
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dataset_size: 9415829.769396551
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---
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# Project Euler Problems Dataset
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A comprehensive collection of mathematical and programming challenges from Project Euler (projecteuler.net), organized for machine learning and educational purposes.
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## Dataset Description
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This dataset contains 918 problems from Project Euler, a series of challenging mathematical/computer programming problems that require creative problem-solving approaches.
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### Features
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- **id**: Problem number (integer)
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- **title**: Problem title
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- **problem**: Plain text version of the problem statement
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- **question_latex**: LaTeX formatted problem statement
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- **question_html**: HTML formatted problem statement
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- **numerical_answer**: The correct numerical answer
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- **pub_date**: Publication date
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- **solved_by**: Number of people who have solved the problem
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- **diff_rate**: Difficulty rating (percentage of users who solved it)
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## Splits
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The dataset provides several splits for different use cases:
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- **train/test**: Standard 80/10/10 split for machine learning
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- train: 734 problems
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- test: 184 problems
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- **easy/medium/hard**: Problems grouped by difficulty level
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- easy: 277 problems (>25% solve rate)
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- medium: 336 problems (5-25% solve rate)
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- hard: 305 problems (≤5% solve rate)
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- **early_problems/later_problems**: First half vs. second half of problems by ID
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- early_problems: 464 problems
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- later_problems: 454 problems
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- **sample**: A random selection of 50 problems for quick experimentation
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## Usage
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```python
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from datasets import load_dataset
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# Load the entire dataset with all splits
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dataset = load_dataset("your-username/project-euler-problems")
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# Work with specific splits
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train_problems = dataset["train"]
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hard_problems = dataset["hard"]
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sample_problems = dataset["sample"]
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# Example: Get a problem
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problem = train_problems[0]
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print(f"Problem #{problem["id"]}: {problem["title"]}")
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print(problem["problem"])
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print(f"Answer: {problem["numerical_answer"]}")
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```
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## Potential Applications
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- Training math problem-solving models
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- Generating solutions or solution approaches
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- Testing reasoning capabilities of language models
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- Educational tools for learning algorithmic thinking
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## Citation & License
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This dataset is provided for research and educational purposes.
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Project Euler problems are from projecteuler.net.
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