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

Dataset:

This dataset is originally made available by UCI Machine Learning Repository (links: https://archive.ics.uci.edu/ml/datasets/wine+quality).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…

Dataset:

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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