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Cannabis-Analysis

Subject

Cannabis strains exist for a long time. The goal is to develop a Machine Learning model that can predict whether a strain is of Hybrid, Indica or Sativa type.

Data Acquisition

Crawling is used to get the data.

Data Arrangement

Once the data was acquired, we coverted nested dictionaries to columns.

Data Cleaning

After reviewing the data, it was found some of the data was faulty

  • Missing data
  • Data type was wrong (float instead of integer)

To complete missing values for ['Feelings', 'Flavors'] - For each column of 'Feeling'/'Flavor' - found the most frequent feeling for each strain that is a "sibling" or "parent".

To complete missing values for ['Cannbinoids'] - For each column of 'Cannbinoids' - got the mean value for each strain that is a "sibling" or "parent".

Data Visualisation

Visualisation technics such as scatter plot, bar plot and pie plot were used

Machine Learning

  • Predict strain type
  • Predict strain type without data of "Hybrid" strains

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