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Data Science for Marketing Analytics

Data Science for Marketing Analytics - Second Edition

By : Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali
4.3 (203)
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Data Science for Marketing Analytics

Data Science for Marketing Analytics

4.3 (203)
By: Mirza Rahim Baig , Gururajan Govindan , Vishwesh Ravi Shrimali

Overview of this book

Unleash the power of data to reach your marketing goals with this practical guide to data science for business. This book will help you get started on your journey to becoming a master of marketing analytics with Python. You'll work with relevant datasets and build your practical skills by tackling engaging exercises and activities that simulate real-world market analysis projects. You'll learn to think like a data scientist, build your problem-solving skills, and discover how to look at data in new ways to deliver business insights and make intelligent data-driven decisions. As well as learning how to clean, explore, and visualize data, you'll implement machine learning algorithms and build models to make predictions. As you work through the book, you'll use Python tools to analyze sales, visualize advertising data, predict revenue, address customer churn, and implement customer segmentation to understand behavior. By the end of this book, you'll have the knowledge, skills, and confidence to implement data science and machine learning techniques to better understand your marketing data and improve your decision-making.
Table of Contents (11 chapters)
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Preface

9. Multiclass Classification Algorithms

Activity 9.01: Performing Multiclass Classification and Evaluating Performance

Solution:

  1. Import the required libraries:

    import pandas as pd

    import numpy as np

    from sklearn.ensemble import RandomForestClassifier

    from sklearn.model_selection import train_test_split

    from sklearn.metrics import classification_report,\

                                confusion_matrix,\

                                accuracy_score

    from sklearn import metrics

    from sklearn.metrics import precision_recall_fscore_support

    import matplotlib.pyplot as plt

    import seaborn as sns

  2. Load the marketing data into a DataFrame named data and look at the first five rows of the DataFrame using the following code:

    data...

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Data Science for Marketing Analytics
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