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The Supervised Learning Workshop

You're reading from   The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781800209046
Length 532 pages
Edition 2nd Edition
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Authors (4):
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Blaine Bateman Blaine Bateman
Author Profile Icon Blaine Bateman
Blaine Bateman
Ashish Ranjan Jha Ashish Ranjan Jha
Author Profile Icon Ashish Ranjan Jha
Ashish Ranjan Jha
Ishita Mathur Ishita Mathur
Author Profile Icon Ishita Mathur
Ishita Mathur
Benjamin Johnston Benjamin Johnston
Author Profile Icon Benjamin Johnston
Benjamin Johnston
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Toc

2. Exploratory Data Analysis and Visualization

Activity 2.01: Summary Statistics and Missing Values

The steps to complete this activity are as follows:

  1. Import the required libraries:
    import json
    import pandas as pd
    import numpy as np
    import missingno as msno
    from sklearn.impute import SimpleImputer
    import matplotlib.pyplot as plt
    import seaborn as sns
  2. Read the data. Use pandas' .read_csv method to read the CSV file into a pandas DataFrame:
    data = pd.read_csv('../Datasets/house_prices.csv')
  3. Use pandas' .info() and .describe() methods to view the summary statistics of the dataset:
    data.info()
    data.describe().T

    The output of info() will be as follows:

    Figure 2.50: The output of the info() method (abbreviated)

    The output of describe() will be as follows:

    Figure 2.51: The output of the describe() method (abbreviated)

  4. Find the total count and total percentage of missing values in each column of the DataFrame and display them for columns having at least...
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