Overview / Usage

Predict the white winner in a chess game on the basis of first move of white player and response of black player. In the dataset all the set of moves are given but We choose to predict the white winner the first move.Also it predicts the next move of the player using deep learning techniques

Methodology / Approach

Chess prediction is a complex task that requires advanced machine learning techniques and a well-designed methodology. The following steps can be taken as a general methodology for chess prediction:

1. Data Collection: Collect data from various sources such as chess databases, game logs, and historical records. The data should include information about the players, their ratings, the opening moves, and the outcome of the games.
2. Data Preprocessing: Clean and preprocess the collected data. This involves removing duplicates, missing data, and irrelevant data. The data should be formatted in a way that is compatible with the machine learning algorithms.
3. Feature Engineering: Extract relevant features from the preprocessed data. This involves transforming the data into a format that can be used by the machine learning algorithms. For example, converting the opening moves into numerical values, and calculating the difference in player ratings.
4. Model Selection: Select a suitable machine learning model that can handle the prediction task. The model can be chosen based on factors such as accuracy, training time, and interpretability.
5. Model Training: Train the selected machine learning model using the preprocessed and engineered data. This involves splitting the data into training and testing sets, and using the training set to teach the model to make predictions.
6. Model Evaluation: Evaluate the performance of the trained machine learning model. This involves testing the model on the testing set and comparing its predictions with the actual outcomes.
7. Model Optimization: Optimize the machine learning model to improve its accuracy and performance. This involves tweaking the model parameters and retraining the model on the optimized parameters.
8. Deployment: Deploy the optimized machine learning model to a production environment where it can be used to make predictions on new data.

In summary, the methodology of chess prediction involves collecting and preprocessing data, engineering relevant features, selecting and training a suitable machine learning model, evaluating its performance, optimizing the model, and finally deploying it to a production environment.

Technologies Used

jupyter notebook

visual studio code

Tensorflow

Repository

https://github.com/Godxlove/Chess-Oracle.git

Other links

• video

Collaborators