Skip to content
Supervised Learning with scikit-learn
  • AI Chat
  • Code
  • Report

    • Machine Learning with scikit-learn

    Run the hidden code cell below to import the data used in this course.

    # Import the course packages
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sklearn
import scipy.stats

# Import the course datasets
auto = pd.read_csv("datasets/auto.csv")
boston = pd.read_csv("datasets/boston.csv")
diabetes = pd.read_csv("datasets/diabetes.csv")
gapminder = pd.read_csv("datasets/gm_2008_region.csv")
votes = pd.read_csv("datasets/votes.csv")
whitewine = pd.read_csv("datasets/white-wine.csv")

    Take Notes

    Add notes about the concepts you've learned and code cells with code you want to keep.

    Add your notes here

    # Add your code snippets here

    Explore Datasets

    Use the DataFrames imported in the first cell to explore the data and practice your skills! Below are the DataFrames and the target variables you can pratice making predictions on.

    • auto: mpg, the miles per gallon of used cars.
    • boston: MEDV, the median value of owner-occupied homes in thousands of dollars.
    • diabetes: diabetes, 0 indicates that the patient does not have diabetes, while a value of 1 indicates that the patient does have diabetes.
    • gapminder: life, life expectancy
    • party: party affiliation (democrat or republican)
    • whitewine: quality