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.
User Segmentation and App Uninstall Prediction
Objective:
- Identify key factors influencing user retention and churn.
- Segment users based on behavior using clustering techniques.
- Train machine learning models to predict app uninstallation.
- Implement an interactive system for real-time user predictions.
Techniques Used:
- User Segmentation: Clustering with K-Means to categorize user behaviors.
- Churn Prediction: Classification using Random Forest and XGBoost.
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 | Develops a regression model to predict house prices based on various features such as amenities, area, etc. |
| 2 | FMNIST Image Classification | Classification | Implements a neural network to classify Fashion-MNIST images into categories like shirts, shoes, and bags. |
| 3 | Transaction Anomaly Detection | Classification | Detecting anomalies in financial transactions using Isolation Forest. |
| 4 | User Segmentation and App Uninstall Prediction | Clustering | Analyze user behaviour to segment users and predict app uninstallation using K-means, Random Forest and XGBoost. |
Here's the organization of the repository:
/Machine-Learning-Projects
│
├── /Regression
│ └── /Housing Price Prediction
│
├── /Classification
│ ├── /FMNIST-Image-Classification
│ └── /Transaction-Anomaly-Detection
│
├── /Clustering
│ └── /App-Users-Segmentation
│
└── README.md
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!