This repository is a collection of various machine learning projects I have worked on. Each project focuses on a unique problem and demonstrates my ability to apply machine learning concepts, data analysis techniques, and advanced tools to real-world datasets.
| Project Name | Description |
|---|---|
| Classification of Customer Purchases Using Multiple Methods | Predicting whether a customer will buy a car using various classification techniques. |
| Coloring Black & White Images | Developing deep learning models to add color to grayscale images, enhancing their visual appeal. |
| Customer Segmentation Using Clustering Methods | Applying clustering techniques to segment mall customers based on their purchasing behaviors and demographics. |
| Fraud Detection using Artificial Neural Networks | Analyzing bank customer data using ANN for pattern detection and SOM to identify potential fraud. |
| Image Classification using Convolutional Neural Network | Training a CNN to classify images as either a cat or a dog. |
| Movie Rating Prediction using Unsupervised Learning | Predicting if a customer will like a movie using Autoencoders and Boltzmann Machines. |
| Natural-Language | Using Random Forest and Maximum Entropy models to classify reviews as positive or negative. |
| Salary Prediction Using Regression Models | Building regression models to predict employee salaries based on key features like experience, education, and role. |
| Stock Prediction using Recurrent Neural Network | Applying Recurrent Neural Networks (RNNs) to forecast stock prices based on historical data. |
Here is an overview of the technologies and tools I utilized across the projects:
- Programming Languages: Python
- Data Analysis and Manipulation: Pandas, NumPy
- Data Visualization: Matplotlib, Pylab
- Machine Learning: Scikit-learn, XGBoost, Minisom
- Deep Learning: TensorFlow, Keras, PyTorch
- Natural Language Processing: NLTK
- Image Processing and Computer Vision: OpenCV, PIL
- Scientific Computing: Scipy
- GUI Development: Tkinter