Welcome to my machine learning projects repository! This portfolio contains a series of hands-on projects designed to demonstrate various machine learning techniques, algorithms, and workflows. Each project explores different aspects of data science, from data cleaning and preprocessing to model training and evaluation.
Aircraft Defect Detection and Automated Image Captioning
Objective:
- Use the VGG16 pretrained model for image classification.
- Prepare and preprocess image data.
- Evaluate the model's performance and visualize predictions on test data.
- Use BLIP pretrained model for image captioning and summarization.
- Generate caption and summary for an aircraft image.
This repository will continue to grow as I add more machine learning projects. Here are the general areas that will be covered:
- Supervised Learning: Classification and Regression problems
- Unsupervised Learning: Clustering and Dimensionality Reduction
- Deep Learning: Neural Networks and Computer Vision
| Sr. No. | Project Name | Category | Short Description |
|---|---|---|---|
| 1 | Housing Price Prediction | Regression | Developed a regression model to predict house prices based on various features such as amenities, area, and location. |
| 2 | FMNIST Image Classification | Classification | Implemented a neural network to classify Fashion-MNIST images into categories like shirts, shoes, and bags. |
| 3 | Transaction Anomaly Detection | Classification | Detected anomalies in financial transactions using Isolation Forest. |
| 4 | User Segmentation and App Uninstall Prediction | Clustering | Analyzed user behaviour to segment users and predict app uninstallation using K-means, Random Forest, and XGBoost. |
| 5 | Aircraft Defect Detection and Automated Image Captioning | Classification & Computer Vision | Developed a deep learning pipeline for aircraft defect detection via image classification and automated image captioning/summarization using pretrained models. |
To run the code in this repository, perform the following steps:
- Clone the repository
git clone https://github.com/asitdave/Machine-Learning-Projects.git- Create a virtual environment to install required dependencies.
pip install -r requirements.txtor (for Anaconda)
conda env create -f ML-proj-venv.yml
conda activate ML-proj-venv.ymlFeel free to fork this repository, explore the code, and contribute by submitting issues or pull requests. Suggestions are always welcome!