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This is a Premiere Project done by Team Gitlab in Hamoye Data Science Program Dec'22. Out of 5 models used on the data, Random Forest Classifier was used to further improve the prediction of characters death. With parameter tuning and few cross validation, we were able to reduce the base error by 5.42% and increase accuracy by 2,42%.

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classification crossvalidation gridsearchcv hamoye hamoyedatascienceinternship machine-learning modelselection parameter-tuning prediction premiere-project random-forest-classifier randomsearch-cv teamwork

got_character_death_prediction's Introduction


Hi there ๐Ÿ‘‹, My name is Goodrich Okoro

  • ๐Ÿ”ญ Iโ€™m currently working on Automating Data Analytics Reports
  • ๐ŸŒฑ Iโ€™m currently learning how to build simple a automated system where reports and visualization are updated as data is entered into the database
  • ๐Ÿ‘ฏ Iโ€™m looking to collaborate in solving industry/business problems
  • ๐Ÿ’ฌ Ask me about Data Analytics
  • โšก Fun fact: I love music, coding and seeing movies.

Skills and Tools:

Python PostgreSQL MySQL Git Jupyter vscode Pandas SQLite VisuaStudio Numpy SQLAlchemy Azure Github Slack


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