https://github.com/praveendecode/docker-credit-card-prediction

Docker Images Creation for Machine learning project for predicting credit card approvals, aiding financial decision-making with data analysis and predictive modeling. Access insights and predictions here.
https://github.com/praveendecode/docker-credit-card-prediction

docker docker-container docker-image dockerfile machine-learning python

Last synced: 29 days ago
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Docker Images Creation for Machine learning project for predicting credit card approvals, aiding financial decision-making with data analysis and predictive modeling. Access insights and predictions here.

• Host: GitHub
• URL: https://github.com/praveendecode/docker-credit-card-prediction
• Owner: praveendecode
• Created: 2023-10-08T06:44:56.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-12T07:18:18.000Z (over 1 year ago)
• Last Synced: 2025-02-09T13:35:10.319Z (3 months ago)
• Topics: docker, docker-container, docker-image, dockerfile, machine-learning, python
• Language: Python
• Homepage:
• Size: 43 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Project Title

### Credit Card Approval Prediction  [(Visit Image Here)](https://hub.docker.com/repository/docker/praveendecode/credict-card-approval-prediction/general)

![image](https://github.com/praveendecode/docker-credit-card-prediction/assets/95226524/bc96e0f1-d523-4758-89cb-e960d8ffd25b)

## Overview

    The Credit Card Approval Prediction project is aimed at utilizing machine learning models and data analysis to assess creditworthiness. It aids financial institutions and individuals in making informed decisions regarding credit card applications. The project provides an easy, efficient, and reliable way to predict whether a credit card application is likely to be approved or rejected.

## Features

    - Predicts credit card approval or rejection based on user input.

    

    - Utilizes a Random Forest Classifier machine learning model.

    

    - Provides a streamlined and user-friendly interface for credit prediction.

## Getting Started

 To get started with this project, you can deploy the provided Docker image available at [Docker Hub](https://hub.docker.com/repository/docker/praveendecode/credict-card-approval-prediction/general). Follow the instructions on Docker Hub for deployment.

## Technical Steps

    1. Data Preparation: The project uses a dataset (cdata.csv) containing relevant features for credit prediction.

    

    2. Model Training: It employs a Random Forest Classifier to train the credit approval prediction model.

    

    3. Model Testing:  The model is tested for accuracy using a test dataset.

    

    4. User Input Prediction: Users can input their values to test whether a loan application would be approved or rejected.

## Methods

    - Data Preprocessing

    

    - Random Forest Classifier for Credit Prediction

    

    - Data Splitting for Training and Testing

## Skills Covered

    - Data Analysis

    

    - Machine Learning

    

    - Model Deployment

    

    - Docker

    

    - Data Preprocessing

    

    - Python Programming

## Results

      

      The project achieves credit approval prediction with the help of a Random Forest Classifier. Users can input their information, and the system will provide a quick response, indicating whether the loan is approved or rejected. The accuracy of the model can be measured using the test dataset.