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google-colab

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This project employs machine learning to forecast housing prices in California. By scrutinizing location, housing details, and demographics, it constructs various regression models like Linear Regression, KNN, Random Forest, Gradient Boosting, and Neural Networks. These models offer invaluable insights to optimize predictive real estate investment

  • Updated Jun 7, 2024
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This project is a web application for predicting loan approval status based on various financial and personal attributes. It uses a machine learning model that I trained on historical loan data to make predictions. I built the web application using Flask for the web framework, SQLite for the database, and the pre-trained model saved with joblib.

  • Updated Jun 6, 2024
  • Jupyter Notebook

Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.

  • Updated Jun 4, 2024
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