Book Image

The Supervised Learning Workshop - Second Edition

By : Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur
Book Image

The Supervised Learning Workshop - Second Edition

By: Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston, Ishita Mathur

Overview of this book

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society. Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models. Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks. By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.
Table of Contents (9 chapters)

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')

    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']]

    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...