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datascience-machinelearning

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A-TALE-OF-THREE-CITIES

Analyzing the safety (311) dataset published by Azure Open Datasets for Chicago, Boston and New York City using SparkR, SParkSQL, Azure Databricks, visualization using ggplot2 and leaflet. Focus is on descriptive analytics, visualization, clustering, time series forecasting and anomaly detection.

  • Updated May 3, 2021
  • R

This Repo contains tools that allow us to import, clean, manipulate, and visualize data —Includes Python libraries, like pandas, NumPy, Matplotlib, and many more to work with real-world datasets to learn the statistical and machine learning techniques.

  • Updated Jul 7, 2024
  • Jupyter Notebook

This repository contains the implementation of the research paper tVelloso, E., Bulling, A., Gellersen, H., Ugulino, W. and Fuks, H., 2013, March. Qualitative activity recognition of weight lifting exercises. In Proceedings of the 4th Augmented Human International Conference (pp. 116-123).

  • Updated Oct 9, 2020
  • R

The dataset having Pneumonia and Normal chest X-Ray images were trained on different numbers of epochs to check the variability in the training and validation accuracies. The ResNet50 model with the highest and closest Training and Validation accuracies was then used for the prediction.

  • Updated Sep 6, 2022
  • Jupyter Notebook

This project uses supervised machine learning techniques with multiple regression models to predict CO2 emissions in Canada, it includes data cleaning, encoding, analyzing and visualization to identify patterns, resulting in a model that can make accurate predictions.

  • Updated Mar 13, 2023
  • Jupyter Notebook

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