license:mit size_categories:n<1K format:csv modality:tabular library:datasets library:pandas library:mlcroissant library:polars region:us infinite-dataset-hub synthetic
---
license: mit
tags:
- infinite-dataset-hub
- synthetic
---

# ChessMasterTraining

tags: game analysis, ML, deep learning

_Note: This is an AI-generated dataset so its content may be inaccurate or false_

**Dataset Description:**

The 'ChessMasterTraining' dataset is designed for the purpose of training machine learning models to analyze chess games and predict game outcomes. Each row represents a unique game with associated features such as the opening moves, mid-game strategy, endgame scenarios, and final game results. The dataset aims to assist in developing algorithms that can understand the complexities of chess, learn from past games, and potentially contribute to AI development in the realm of deep learning.

**CSV Content Preview:**

```csv
game_id,opening_moves,mid_game_moves,endgame_moves,final_result,player_rating,game_length,strategy_type,win_probability
GM1001,e4 e5 Nf3 Nc6 Bb5,Nf6 d3 a6 Ba4 Nf3 Bb7,O-O Be7,Ruy Lopez,Nimzo-Indian,2100,1500,1.05,0.67
GM1002,d4 Nf6 c4 e6 Nc3 Bb4,Bd3 O-O,d5,Sicilian Defense,Najdorf,2300,1550,0.90,0.52
GM1003,e4 e5 Nf3 Nc6 Bc4 Bc5,b4,Ruy Lopez,Cambridge Springs,2250,1600,0.93,0.58
GM1004,d4 Nf6 d5 e3 c6 Bg5 Be7 Nbd7 f4,dxc6 bxc6 bxc6,Ruy Lopez,Open Variation,1.40,1575,0.87,0.60
GM1005,e4 c5 Sicilian Defense,Nc3 d6 Nf3 Nc6,Bd3 Be7 Bc4 Bc5,e5,Open Sicilian,2400,1700,0.98,0.75
```

In this preview, the CSV contains the following columns:

- `game_id`: A unique identifier for each chess game.
- `opening_moves`: The initial sequence of moves played by both players.
- `mid_game_moves`: A sequence of moves that represent the middle game phase.
- `endgame_moves`: The sequence of moves made during the endgame.
- `final_result`: The result of the game, e.g., win for white, win for black, or draw.
- `player_rating`: The ELO rating of the white player.
- `game_length`: The number of moves played in the game.
- `strategy_type`: The classification of the game strategy, e.g., defensive, aggressive, etc.
- `win_probability`: A predicted probability of winning for the white player, based on historical data and analysis.

**Source of the data:**

The dataset was generated using the [Infinite Dataset Hub](https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub) and microsoft/Phi-3-mini-4k-instruct using the query 'Chess':

- **Dataset Generation Page**: https://huggingface.co/spaces/infinite-dataset-hub/infinite-dataset-hub?q=Chess&dataset=ChessMasterTraining&tags=game+analysis,+ML,+deep+learning
- **Model**: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct
- **More Datasets**: https://huggingface.co/datasets?other=infinite-dataset-hub
