ML-TensorFlow-Projects/ ├── Classification_on_imbalanced_data.ipynb ├── Credit_Card_Fraud_Detection.ipynb ├── Computer_vision_with_TensorFlow.ipynb ├── Machine_Learning_Statistics.ipynb ├── Time_series_forecasting.ipynb
This repository contains hands-on ML projects implemented using TensorFlow, Scikit-learn, and other Python libraries. Each notebook addresses a real-world ML challenge from classification to forecasting.
Project Summaries
Notebook: Machine_Learning_Statistics.ipynb
A foundational notebook exploring statistical techniques used in machine learning — including distributions, hypothesis testing, and data visualization.
Notebook: Classification_on_imbalanced_data.ipynb
A classification project focusing on handling imbalanced datasets using techniques
Notebook: Credit_Card_Fraud_Detection.ipynb
A supervised ML project using TensorFlow and Scikit-learn to detect fraudulent credit card transactions using imbalanced binary classification.
Notebook: Computer_vision_with_TensorFlow.ipynb
An image classification model built using Convolutional Neural Networks (CNNs) in TensorFlow/Keras.
- Data Augmentation
- CNN Architecture
- Model Evaluation
Notebook: Time_series_forecasting.ipynb
Applied deep learning models and statistical methods to forecast future trends in time-dependent data.
- Languages: Python
- Libraries: TensorFlow, Keras, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
- Tools: Jupyter Notebook, Google Colab, GitHub