A Machine Learning Project with Python Code

Red Wine

Table of Content:

  1. Dataset
  2. Data Wrangling
  3. Data Exploration
  4. Guiding Question
  5. Prepare the Data for Classification Model
  6. Modeling: Baseline Classification, SVC, Decision Tree, and Random Forest
  7. Feature Importance
  8. Conclusion


This dataset is originally made available by UCI Machine Learning Repository (links: https://archive.ics.uci.edu/ml/datasets/wine+quality). I import the dataset and name it “df”. It has 1599 entries…


The dataset was originally collected and made available by Dr. Kristen Gorman and the Palmer Station, Antarctica LTER. The set contains 344 rows and 7 columns. The 7 attributes are species, culmen length in mm, culmen depth in mm, flipper length in mm, body mass in g, island, and sex.

Jingyi Fang

USC Sophomore Majoring in CSBA

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