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.

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Table of Contents (9 chapters)
Preface
About the Book
1
1. Fundamentals
1. Fundamentals
Introduction
Data Quality Considerations
Summary
Free Chapter
2
2. Exploratory Data Analysis and Visualization
2. Exploratory Data Analysis and Visualization
Introduction
Exploratory Data Analysis (EDA)
Summary Statistics and Central Values
Missing Values
Distribution of Values
Relationships within the Data
Summary
3
3. Linear Regression
3. Linear Regression
Introduction
Regression and Classification Problems
Linear Regression
Multiple Linear Regression
Summary
4
4. Autoregression
4. Autoregression
Introduction
Autoregression Models
Summary
5
5. Classification Techniques
5. Classification Techniques
Introduction
Ordinary Least Squares as a Classifier
Logistic Regression
Classification Using K-Nearest Neighbors
Classification Using Decision Trees
Artificial Neural Networks
Summary
6
6. Ensemble Modeling
6. Ensemble Modeling
Introduction
One-Hot Encoding
Overfitting and Underfitting
Bagging
Bootstrapping
Boosting
Stacking
Summary
7
7. Model Evaluation
7. Model Evaluation
Introduction
Importing the Modules and Preparing Our Dataset
Evaluation Metrics
Splitting a Dataset
Performance Improvement Tactics
Summary
Appendix
1. Fundamentals
2. Exploratory Data Analysis and Visualization
3. Linear Regression
4. Autoregression
5. Classification Techniques
6. Ensemble Modeling
7. Model Evaluation

6. Ensemble Modeling

Activity 6.01: Stacking with Standalone and Ensemble Algorithms

Solution

  1. Import the relevant libraries:
    import pandas as pd
import numpy as np
import seaborn as sns
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import KFold
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import GradientBoostingRegressor, \
RandomForestRegressor
  2. Read the data:
    data = pd.read_csv('boston_house_prices.csv')
data.head()

    Note

    The preceding code snippet assumes that the dataset is presented in the same folder as that of the exercise notebook. However, if your dataset is present in the Datasets folder, you need to use the following code: data = pd.read_csv('../Datasets/boston_house_prices.csv')

    You will...

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