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Project: Predicting the Perfect Ratio of Red Wine Ingredients with Regression Machine Learning Algorithms

Predict the perfect ratio of ingredients for red wine. This is a numerical discrete outcome. Explore with various Regression Models & see which yields greatest accuracy. Examine trends & correlations within our data. Determine which features are important in determing the quality of red wine. Note: Due to the fact that we are predicting a numerical discreet value, we will be training various Regression Models.

Author: Jarar Zaidi

Date: 6/14/2020

Medium Link to project: https://medium.com/@jararzaidi/project-maximizing-red-wine-profits-with-regression-machine-learning-algorithms-8caad2a10a08

This project consists of these files:

Predicting Red Wine Quality with Regression Machine Learning Algorithms.ipynb - the jupyter Notebook (.ipynb) version of this project

Predicting Red Wine Quality with Regression Machine Learning Algorithms.py - the Python (.py) version of this project

wineQuality.csv - Original dataset used from Kaggle.com in CSV (.csv) format

Table of Contents:

  1. Introduction: Scenario Goal Features & Predictor

  2. Data Wrangling: Missing Values, Detecting/Handling Outliers with a Z-Score

  3. Exploratory Data Analysis: Correlations, Pairplots, Feature Engineering, Kernel Density Estimation (KDE), Regression Joint Plot, Bar Plots, Violin & Box Plots

  4. Machine Learning + Predictive Analytics: Prepare Data for Modeling Modeling/Training R² Values Predictions K-Fold Cross Validation

  5. Conclusions