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Data Science for Marketing Analytics

You're reading from   Data Science for Marketing Analytics A practical guide to forming a killer marketing strategy through data analysis with Python

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Product type Paperback
Published in Sep 2021
Publisher Packt
ISBN-13 9781800560475
Length 636 pages
Edition 2nd Edition
Languages
Tools
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Authors (3):
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Vishwesh Ravi Shrimali Vishwesh Ravi Shrimali
Author Profile Icon Vishwesh Ravi Shrimali
Vishwesh Ravi Shrimali
Mirza Rahim Baig Mirza Rahim Baig
Author Profile Icon Mirza Rahim Baig
Mirza Rahim Baig
Gururajan Govindan Gururajan Govindan
Author Profile Icon Gururajan Govindan
Gururajan Govindan
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Toc

Table of Contents (11) Chapters Close

Preface
1. Data Preparation and Cleaning 2. Data Exploration and Visualization FREE CHAPTER 3. Unsupervised Learning and Customer Segmentation 4. Evaluating and Choosing the Best Segmentation Approach 5. Predicting Customer Revenue Using Linear Regression 6. More Tools and Techniques for Evaluating Regression Models 7. Supervised Learning: Predicting Customer Churn 8. Fine-Tuning Classification Algorithms 9. Multiclass Classification Algorithms Appendix

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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