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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

3. Linear Regression

Activity 3.01: Plotting Data with a Moving Average

  1. Load the two required packages:
    import pandas as pd
    import matplotlib.pyplot as plt
  2. Load the dataset into a pandas DataFrame from the CSV file:
    df = pd.read_csv('../Datasets/austin_weather.csv')
    df.head()

    The output will show the initial five rows of the austin_weather.csv file:

    Figure 3.61: The first five rows of the Austin weather data (note that additional columns to the right are not shown)

  3. Since we only need the Date and TempAvgF columns, we'll remove all the other columns from the dataset:
    df = df.loc[:, ['Date', 'TempAvgF']]
    df.head()

    The output will be as follows:

    Figure 3.62: Date and TempAvgF columns of the Austin weather data

  4. Initially, we are only interested in the first year's data, so we need to extract that information only. Create a column in the DataFrame for the year value, extract the year value as an integer from the strings in the Date column...
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