This book is designed for those who already have a basic understanding of programming and want to dive into Python-based machine learning through hands-on practice.
With 100 targeted exercises, it provides a structured approach to developing and refining your skills. Each exercise includes clear source code and visual output, making it easier to grasp complex concepts.
Confusion Matrix Visualization in Model Evaluation.
A client working for an e-commerce company has developed a machine learning model to predict whether customers will buy a product based on their online behavior. The goal is to evaluate the model’s performance by analyzing the predictions. The client has requested that you visualize the confusion matrix to better understand the number of correct and incorrect predictions (true positives, false positives, true negatives, and false negatives). Using a simple classification model, such as a decision tree, train a model with generated data, predict the outcomes, and plot the confusion matrix. Your task is:Create synthetic data for this classification problem, using features like ’time spent on the website’ and 'number of items viewed’ as inputs and a binary target variable ’purchase’ (o for no purchase, 1 for purchase).Train a model on this data. Predict using the model. Plot and display the confusion matrix.
CONTENTS.
Chapter 1 Introduction.
Chapter 2 For beginners.
Chapter 3 For advanced.
Chapter 4 Request for review evaluation.
Appendix: Execution Environment.
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