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BDG Game Predictor

Upload Historical Data (CSV): Number of Games to Analyze:

B+g

BDG Game Predictor

BDG

PredictorPro CodeGen

Historical Game Data (CSV or JSON):

Select Prediction Model: Random Forest Neural Network Markov Chain Custom Model

Generated code will appear here...

`; document.getElementById('output').textContent = generatedCode; } function trainModel() { const data = document.getElementById('data-input').value; const model = document.getElementById('model-selection').value; alert(`Training ${model} model with provided data...`); }

Now 200%

PredictorPro CodeGen

Historical Game Data (CSV or JSON):

Select Prediction Model: Random Forest Neural Network Markov Chain Custom Model

Generated code will appear here...

`; document.getElementById('output').textContent = generatedCode; } function trainModel() { const data = document.getElementById('data-input').value; const model = document.getElementById('model-selection').value; // Placeholder for actual model training logic alert(`Training ${model} model with provided data...`); }

1100%

BDG Game Predictor

Waiting for next outcome...

Games played in the last minute: 0

BARASAT _C

                        

        

BDG Game Predictor

        

Waiting for next outcome...

        

Games played in the last minute: 0

        

Game Price: Not Available

                          Visit BDG Game     

    

BARASAT _B

                        

        

BDG Game Predictor

        

Waiting for next outcome...

        

Games played in the last minute: 0

        

Game Price: Not Available

                          Visit BDG Game     

    

BARASAT _A

                        

        

BDG Game Predictor

        

Waiting for next outcome...

        

Games played in the last minute: 0

                      

    

500%

BDG Game Predictor

Waiting for next outcome...

Games played in the last minute: 0

from flask import Flask, request, jsonify from flask_cors import CORS app = Flask(__name__) CORS(app) # This will enable CORS for all routes @app.route('/predict', methods=['POST']) def predict(): data = request.json input_data = data.get('data') # Perform your prediction logic here # For demonstration, let's return a mock prediction prediction = 'Small' if int(input_data) % 2 == 0 else 'Big' return jsonify({'prediction': prediction}) if __name__ == '__main__': app.run(debug=True)