ML Project Ideas

Project ideas for machine learning that can be done with free resources, including datasets and platforms for testing:

1. Image Classification with TensorFlow Playground:
   • Dataset: MNIST (Handwritten digits)
   • Platform: TensorFlow Playground (https://playground.tensorflow.org/)
2. Sentiment Analysis on Twitter Data:
   • Dataset: Twitter API or Kaggle datasets
   • Platform: Google Colab or Jupyter Notebooks (https://colab.research.google.com/)
3. Predictive Text Generation with OpenAI’s GPT-3:
   • API: OpenAI GPT-3
   • Platform: OpenAI Playground or use the API (https://beta.openai.com/signup/)
4. Spam Email Detection:
   • Dataset: UCI Machine Learning Repository – Spambase Dataset
   • Platform: Google Colab or Jupyter Notebooks
5. House Price Prediction with Kaggle:
   • Dataset: Kaggle House Prices dataset
   • Platform: Kaggle Kernels (https://www.kaggle.com/kernels)
6. Image Recognition with Google AutoML Vision:
   • Dataset: Use Google AutoML Vision’s sample dataset or upload your own images
   • Platform: Google AutoML Vision (https://cloud.google.com/automl)
7. Stock Price Prediction with Alpha Vantage API:
   • API: Alpha Vantage API for stock market data
   • Platform: Google Colab or Jupyter Notebooks
8. Chatbot Development with Rasa NLU:
   • Dataset: Create a dataset for training a chatbot
   • Platform: Rasa NLU (https://rasa.com/)
9. Gender and Age Prediction from Images:
   • Dataset: IMDB-WIKI dataset
   • Platform: Google Colab or Jupyter Notebooks
10. Natural Language Processing with NLTK:
    • Dataset: NLTK (Natural Language Toolkit) datasets
    • Platform: Google Colab or Jupyter Notebooks
11. Object Detection with YOLO (You Only Look Once):
    • Dataset: COCO dataset
    • Platform: Google Colab or Jupyter Notebooks
12. Handwritten Digit Recognition with scikit-learn:
    • Dataset: MNIST dataset
    • Platform: Google Colab or Jupyter Notebooks

Remember to check the terms of use for the platforms and datasets to ensure compliance with any usage restrictions. Additionally, always respect privacy and ethical considerations when working with data, especially if it involves personal information.