Merge pull request #851 from Varunshiyam/fixes-848

World Cup Prediction with Deep Learning

Prediction Models/World_cup_Prediction/Readme.md

@@ -0,0 +1,51 @@
+# World Cup Prediction with Deep Learning
+
+This project uses a Character Embedding model to predict FIFA World Cup match outcomes with an 80% accuracy. The prediction model is developed using TensorFlow, with a focus on leveraging player-specific data along with team information to enhance prediction accuracy.
+
+## Project Overview
+
+In this project, we integrate individual player data with match history to construct a deep learning model that predicts match results. This model demonstrates the importance of feature selection, showing how player data and match records contribute to accurate predictions.
+
+### Key Features
+
+- **Data Ingestion:** Load and preprocess historical World Cup match data and player rosters.
+- **Data Transformation:** Create character embeddings for player names to use in deep learning model inputs.
+- **Model Building:** Use TensorFlow to construct a Character Embedding model tailored for predictive analysis.
+- **Evaluation:** Achieve 80% prediction accuracy on test data, using World Cup 2018 results as a validation set.
+
+## Project Structure
+
+- `data/`: Contains historical World Cup data and player rosters.
+- `notebooks/`: Jupyter notebooks with data preprocessing, model building, and evaluation code.
+- `scripts/`: Python scripts for data loading, model training, and evaluation.
+- `models/`: Saved models for reproducibility.
+
+## Getting Started
+
+1. **Install dependencies**:
+    ```bash
+    pip install -r requirements.txt
+    ```
+
+2. **Run the model**:
+    - Load the data in the `data/` directory.
+    - Execute the Jupyter notebooks in `notebooks/` to preprocess data, build, and evaluate the model.
+
+3. **Model Evaluation**:
+    - The model's performance metrics are logged, including accuracy, precision, and recall.
+
+## Dependencies
+
+- TensorFlow
+- Pandas
+- NumPy
+- Scikit-learn
+
+## Results
+
+The model achieves approximately 80% accuracy in predicting the outcome of FIFA World Cup matches. This result highlights the effectiveness of character embeddings for player names and team rosters in sports outcome prediction.
+
+## Conclusion
+
+The project emphasizes that feature selection and data quality are as crucial as model architecture in predictive modeling. By focusing on player-specific data, this project offers insights into the predictive power of individual player contributions to team performance.
+