Closing Issue#8-Better Models

[RakshithRAcharya]

Closing Issue #8 Tried predicting wine quality with Logistic Regression, KNN, Random Forest, Support Vector Machines(SVM), Gradient Boosting with parameter tuning to obtain higher accuracy 1) Logistic Regression - 87.5%(without tuning)

• 88.25%(with parameter tuning) 2)KNN - 86.25%(without tuning)
  • 89%(with parameter tuning) 3)RF - 90.5%
    • 90.75% 4)SVM - 85.75%
  • 91.75% 5)Gradient Boosting - 87.5%
    • 88.75%

[acm-bot[bot]]

Hey there @RakshithRAcharya, Thanks for contributing! A maintainer will review this shortly.

[RakshithRAcharya]

This also solves Issue #7 and Issue #6

[pk-95]

Hey @RakshithRAcharya , thanks for your contribution. Gimme a while, will go through it :)

[pk-95]

Hey @RakshithRAcharya , this notebook is super great :) You've done a great job. Would just like to point out though, this ipynb wouldn't cover issue #6 and #7 as those require you to use the 4 given feature columns as is, and work only with model parameters. You can attempt 6 and 7 w separate PR's with these conditions if you wish :)

[pk-95]

But yeas loved the notebook, you will be getting 50 brownie points as well!

[pk-95]

!bounty 400

[acm-bot[bot]]

Congrats @RakshithRAcharya, you got 400 points!

[RakshithRAcharya]

Thank You @pk-95

[techverve]

!bounty 300

[acm-bot[bot]]

Congrats @RakshithRAcharya, you got 300 points!