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k-nearest-neighbours

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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
  • Jupyter Notebook

This project provides a comprehensive framework for evaluating classification models and selecting the best algorithm based on performance metrics. It demonstrates the importance of hyperparameter tuning and model comparison in machine learning workflows.

  • Updated Jun 6, 2024
  • Python

Project aims to forecast potato prices in India using LSTM, KNN, and Random Forest Regression, integrating historical data on prices, regional stats, and rainfall patterns. Targeting agricultural stakeholders for informed decision-making.

  • Updated Jun 1, 2024
  • Python

This project provides the classification of DNA sequences for Breast cancer prediction which into promoter regions associated. Using machine learning and deep learning techniques, I analyze and try to predict sequence data for negative and positive answers in cancer prediction.

  • Updated May 17, 2024
  • Jupyter Notebook
QSAR-activity-cliff-experiments

This project Integrated machine learning models including Support Vector Machine (SVM), Random Forest, k-Nearest Neighbors, and Neural Networks into a stacked ensemble for predicting potential COVID-19 infections based on the collected data, facilitating proactive healthcare interventions and management.

  • Updated May 11, 2024
  • Jupyter Notebook

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