m33tbhorania/purchase_prediction_model

Purchase Prediction Model

Overview

This repository contains a neural network model trained to predict user purchases based on their interactions. The model was trained using a synthetic dataset to simulate user behavior and classify whether a user will make a purchase.

Model Details

• Model Type: Dense Neural Network
• Training Data: Synthetic dataset generated for simulation purposes
• Training Accuracy: 74.2%
• Evaluation Metrics:
  • Accuracy: 73.1%
  • Precision (Class 0): 0.00
  • Precision (Class 1): 0.73
  • Recall (Class 0): 0.00
  • Recall (Class 1): 1.00
  • F1-Score (Class 0): 0.00
  • F1-Score (Class 1): 0.85

Files

• purchase_prediction_model.h5: The trained model file.
• example_usage.py: A script demonstrating how to load the model and make predictions.

How to Use the Model

To use this model, follow these steps:

1. Install Dependencies

   Ensure you have TensorFlow installed. You can install it using pip:

   pip install tensorflow
   

2 Load the Model and Make Predictions

Use the following code to load the model and make predictions:

from tensorflow.keras.models import load_model import numpy as np

(Load the trained model) model = load_model('path/to/purchase_prediction_model.h5')

(Example data (replace with your test data)) X_test = np.array([[0.5, 1.0, 0.2, ...]])

(Make predictions) predictions = model.predict(X_test)

print(predictions)

Replace 'path/to/purchase_prediction_model.h5' with the actual path to your model file and adjust X_test to match the format of your test data.