https://github.com/msthamizh/horse-race-prediction

This project aims to predict the outcomes of horse races (e.g., "win" or "place") using machine learning techniques. The goal is to provide actionable insights into horse race performance through advanced data processing, machine learning models, and visualization tools.
https://github.com/msthamizh/horse-race-prediction

data-preprocessing exploratory-data-analysis machine-learning python

Last synced: 10 days ago
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This project aims to predict the outcomes of horse races (e.g., "win" or "place") using machine learning techniques. The goal is to provide actionable insights into horse race performance through advanced data processing, machine learning models, and visualization tools.

• Host: GitHub
• URL: https://github.com/msthamizh/horse-race-prediction
• Owner: MSThamizh
• Created: 2024-12-17T06:00:52.000Z (20 days ago)
• Default Branch: main
• Last Pushed: 2024-12-24T17:26:41.000Z (13 days ago)
• Last Synced: 2024-12-24T18:27:47.947Z (13 days ago)
• Topics: data-preprocessing, exploratory-data-analysis, machine-learning, python
• Language: Jupyter Notebook
• Homepage:
• Size: 2.84 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Horse Race Outcome Prediction

## Problem Statement

The primary objective of this project is to predict horse race outcomes and identify significant features contributing to these outcomes. The key components include:

- **Data Retrieval**: Collect and preprocess historical horse race data.

- **Feature Engineering**: Create and transform input features to enhance model predictions.

- **Imbalanced Data Management**: Effectively address the imbalanced nature of race outcomes.

- **Predictive Modeling**: Develop classification models to predict outcomes such as "win" or "place."

---

## Workflow

### 1. Data Retrieval

- Collect historical horse race data, including:

  - Race features (track, weather, race distance, class, date).

  - Horse features (age, weight, past performance).

  - Jockey and trainer statistics.

  - Results (win, place).

  

### 2. Data Cleaning and Preparation

- Remove duplicates.

- Handle missing values using statistical methods like mean/median for numerical variables.

- Convert data types as required for machine learning models.

### 3. Feature Engineering

- Create new features such as:

  - `win_rate`, `avg_finishing_time`, `recent_form`.

  - Encode categorical variables (e.g., horse names, jockeys, race tracks) using one-hot encoding, label encoding, or ordinal encoding.

  

### 4. Imbalanced Data Management

- Apply techniques like SMOTE (Synthetic Minority Over-sampling Technique), under-sampling, or class weights to handle class imbalance effectively.

### 5. Model Building

- Train multiple classification models:

  - Random Forest Classifier.

- Evaluate performance using:

  - Accuracy, Precision, Recall, F1-score.

  - ROC-AUC.

- Use stratified k-fold validation and hyperparameter tuning (Grid Search, Random Search) to optimize model performance.

---

## Technologies Used

- **Python**: Main language for data processing and modeling.

- **Scikit-learn**: Library for machine learning model development.

- **Pandas/Numpy**: Data manipulation and preparation.

- **Seaborn/Matplotlib**: Data visualization for EDA (Exploratory Data Analysis).

---

## References

- **Python**: [https://docs.python.org/3/](https://docs.python.org/3/)

- **Scikit-learn Documentation**: [https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)