https://github.com/faisal-fida/payment-prediction-ml
This project is a Flask-based web application designed to train a machine learning model using TensorFlow, make predictions on payment data, and display the results using a web interface.
https://github.com/faisal-fida/payment-prediction-ml
deep-learning flask machine-learning mnist-handwriting-recognition payments tensorflow
Last synced: 2 months ago
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This project is a Flask-based web application designed to train a machine learning model using TensorFlow, make predictions on payment data, and display the results using a web interface.
- Host: GitHub
- URL: https://github.com/faisal-fida/payment-prediction-ml
- Owner: faisal-fida
- Created: 2023-10-04T07:35:31.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-06T08:07:12.000Z (over 1 year ago)
- Last Synced: 2024-11-10T21:16:29.364Z (over 1 year ago)
- Topics: deep-learning, flask, machine-learning, mnist-handwriting-recognition, payments, tensorflow
- Language: PureBasic
- Homepage:
- Size: 1.23 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Payment Prediction ML
This project is a Flask-based web application designed to train a machine learning model using TensorFlow, make predictions on payment data, and display the results using a web interface.
## Table of Contents
- [Installation](#installation)
- [Usage](#usage)
- [Complexities](#complexities)
- [Solutions](#solutions)
- [Challenges](#challenges)
- [License](#license)
## Installation
1. Clone the repository:
```sh
git clone https://github.com/faisal-fida/payment_prediction_ml.git
cd payment_prediction_ml
```
2. Install the dependencies:
```sh
pip install -r requirements.txt
```
3. Run the application:
```sh
python app.py
```
## Usage
The application provides the following functionalities:
1. **Train the Model**: Run `prediction.py` to train the model on MNIST data and save the trained model.
2. **Web Interface**: Use Flask to provide a web interface for viewing predictions.
## Features
- **Machine Learning Model**: The project uses TensorFlow to create, train, and evaluate a neural network model for payment prediction.
- **Data Handling**: The project involves normalizing and splitting data into training and testing sets.
- **Web Integration**: Flask is used to create a web interface for displaying predictions and allowing users to upload images.
- **Efficient Model Training**: Implemented a sequential neural network model using TensorFlow, including layers for data flattening and dense layers with ReLU and softmax activations.
- **Data Normalization**: Utilized TensorFlow utilities to normalize the dataset, ensuring the model trains effectively.
- **User-Friendly Interface**: Developed a responsive web interface with Tailwind CSS for users to upload images and view predictions.
- **Model Accuracy**: Ensuring the model achieves a high accuracy required fine-tuning the architecture and parameters.
- **Data Normalization**: Properly normalizing the data was crucial for the model's performance.
- **File Handling**: Managing file uploads and predictions through a web interface required careful handling of HTTP requests and response rendering.