ML Projects Collection

Welcome to the ML Projects Collection repository! This repository showcases a diverse range of machine learning projects that address real-world problems across various industries. Whether you're interested in image processing, big data analytics, predictive maintenance, or demand forecasting, you'll find an exciting project to explore.

Table of Contents

• Overview
• Projects
  • 1. Image Compression
  • 2. Financial Services ML Project
  • 3. Manufacturing ML Project
  • 4. Transportation & Logistics ML Project
• Getting Started
• Contributing
• License
• Contact

Overview

This repository includes four main projects:

1. Image Compression
   Leverages FFT and SVD techniques in Python to compress images while retaining quality.

2. Financial Services ML Project
   Uses big data approaches to develop models for fraud detection or credit risk prediction.

3. Manufacturing ML Project
   Focuses on building predictive maintenance models to forecast equipment failure.

4. Transportation & Logistics ML Project
   Develops demand forecasting models to optimize shipment and delivery planning.

Each project is accompanied by detailed documentation, source code, and instructions on how to run and extend the project.

Projects

1. Image Compression ✅

Project Idea:
Reduce image file sizes without significant quality loss using advanced compression techniques.

Techniques and Libraries:

• FFT-based Compression:

  • Libraries Used:
    • from matplotlib.image import imread
    • import numpy as np
    • import matplotlib.pyplot as plt
    • from PIL import Image

• SVD-based Compression:

  • Libraries Used:
    • import numpy as np
    • import matplotlib.pyplot as plt
    • from numpy.linalg import svd
    • from PIL import Image

Description:

• FFT-based Compression:
  Converts images into the frequency domain using the Fast Fourier Transform (FFT) and compresses them by retaining significant frequency components.
• SVD-based Compression:
  Applies Singular Value Decomposition (SVD) to decompose the image matrix, allowing reconstruction with fewer singular values for compression.

2. Financial Services ML Project ✅

Project Idea:
Develop a fraud detection or credit risk model using a big data approach.

Objective:
Utilize (or simulate) transaction data to classify whether a transaction is fraudulent or predict a credit score/loan default.

Steps:

• Data Acquisition:
  Use publicly available datasets (e.g., Kaggle’s "Credit Card Fraud Detection")
• Preprocessing & Feature Engineering:
  Cleaning wasn't needed the data was already clean
• Modeling:
  Trained a classifier (Random Forest, tested on XGBoost but Random forest was better performing)
• Evaluation:
  Use appropriate metrics such as ROC-AUC, precision/recall for this use-case (classification)
• Deployment:
  Packaged the model in a REST API using Flask locally and deployed it to a cloud service Render.
• MLOps Touch:
  Integrate logging for predictions and set up basic monitoring with a dashboard or alert system.

3. Manufacturing ML Project

Project Idea:
Build a predictive maintenance model to forecast equipment failure.

Objective:
Predict when equipment might fail based on sensor or operational data, thereby enabling proactive maintenance and minimizing downtime.

Steps:

• Data Acquisition:
  Use a public dataset such as the NASA Turbofan Engine Degradation Simulation or simulate time series sensor data.
• Preprocessing:
  Clean the time series data, handle missing values, and engineer features like rolling averages or rate of change.
• Modeling:
  Develop a forecasting or classification model to predict failure events (using techniques like LSTM networks for time series or gradient boosting for simpler setups).
• Evaluation:
  Evaluate model performance using metrics appropriate for time series analysis or classification (e.g., accuracy, F1 score, or mean time to failure).
• Deployment:
  Create an end-to-end pipeline that serves the model via an API and set up a dashboard for real-time predictions.
• MLOps Touch:
  Integrate versioning (with tools like MLflow) and containerize the solution using Docker for consistent deployment.

4. Transportation & Logistics ML Project

Project Idea:
Develop a demand forecasting model for a logistics network.

Objective:
Predict shipment demand or delivery volume using historical data to optimize route planning and resource allocation.

Steps:

• Data Acquisition:
  Use public datasets (e.g., historical shipment volumes) or simulate data reflecting typical logistics patterns.
• Preprocessing:
  Clean and aggregate data by time (daily/weekly), and engineer features to capture seasonality, trends, and external factors (such as weather or holidays).
• Modeling:
  Implement forecasting models such as ARIMA, Prophet, or deep learning approaches (e.g., RNN/LSTM).
• Evaluation:
  Assess performance using forecasting metrics like MAPE (Mean Absolute Percentage Error) or RMSE.
• Deployment:
  Deploy the model as a microservice using Flask or FastAPI, and optionally integrate it with a dashboard for visualization (using tools like Plotly Dash or Streamlit).
• MLOps Touch:
  Set up automated retraining (e.g., via cron jobs) and log forecast errors for continuous model improvement.

Getting Started

To get started with any of these projects:

1. Clone the Repository:

   git clone https://github.com/dr4gon01/ML-Pojects.git
   cd ML-Projects-Collection