ML Project: End-to-End Deployment

Screenshots

Home Page

Prediction Page

This repository contains an end-to-end machine learning project, from data preprocessing to model deployment. The project demonstrates the complete lifecycle of a machine learning application.

Repository Link

GitHub Repository

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Table of Contents

1. Project Overview
2. Features
3. Technologies Used
4. Setup Instructions
5. Project Workflow
6. Deployment
7. Contributing
8. License

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Project Overview

This project involves building a machine learning model to solve a specific problem. The workflow includes:

• Data collection and preprocessing
• Exploratory data analysis (EDA)
• Model training and evaluation
• Hyperparameter tuning
• Deployment of the model as a web application

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Features

• Data preprocessing pipeline
• Model training and evaluation
• Interactive web application for predictions
• Scalable deployment using cloud services

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Technologies Used

• Programming Language: Python
• Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn, TensorFlow/PyTorch
• Web Framework: Flask/Django/FastAPI
• Deployment: Docker, AWS/GCP/Azure, Heroku

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Setup Instructions

1. Clone the repository:

git clone https://github.com/IbrahimBagwan1/ml_project.git
cd ml_project

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the application:

python app.py

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Project Workflow

1. Data Collection: Gather data from reliable sources.
2. Data Preprocessing:

• Handle missing values
• Encode categorical variables
• Normalize/scale numerical features

3. Exploratory Data Analysis (EDA):

• Visualize data distributions
• Identify correlations

4. Model Training:

• Train multiple models
• Evaluate performance using metrics like accuracy, precision, recall, etc.

5. Hyperparameter Tuning:

• Optimize model parameters using GridSearchCV/RandomizedSearchCV

6. Model Deployment:

• Build a REST API using Flask/Django/FastAPI
• Containerize the application using Docker
• Deploy to cloud platforms like AWS, GCP, or Heroku

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Deployment

The model is deployed as a web application. Follow these steps for deployment:

1. Build a Docker image:

docker build -t ml_project .

2. Run the Docker container:

docker run -p 5000:5000 ml_project

3. Deploy to a cloud platform (e.g., AWS, GCP, Heroku).

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Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch:

git checkout -b feature-name

3. Commit your changes:

git commit -m "Add feature-name"

4. Push to the branch:

git push origin feature-name

5. Open a pull request.

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License

This project is licensed under the MIT License. See the LICENSE file for details.