BellaBeat

Is this study I analysed the public data of the FitBit trackers and suggested a feature improvement on automatic heart rate anomaly detection based on the existing data.

The following tools were used:

• Python
  • Pandas
  • Numpy
  • Matplotlib
  • SKLearn
  • Seaborn
  • TensorFlow

This study consists of 3 notebooks for convenience, the links below are on Kaggle and have the same code as on this repo:

• Part 1 - Data exploration and inspiration to the question
  https://www.kaggle.com/code/kriggs/bellabeat-case-study-1-3-data-exploration

• Part 2 - Feature selection
  https://www.kaggle.com/code/kriggs/bellabeat-case-study-2-3-feature-selection

• Part 3 - ML Modelling
  https://www.kaggle.com/code/kriggs/bellabeat-case-study-3-3-ml-modelling

In these notebooks I go through:

• Statistical visualization of the data

• In depth look of the data

• Explanations

• Feature selection

• ML Modelling

• Model analysis

For the conclusions please check the 3rd notebook:

• https://www.kaggle.com/code/kriggs/bellabeat-case-study-3-3-ml-modelling