Machine Learning Basics | Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance by Prakhar Chauhan | Learn Smarter
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Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance

The chapter focuses on the construction of a machine learning model aimed at predicting student performance based on various parameters. Key components include data loading, exploration, preprocessing, model building using logistic regression, and model evaluation with appropriate metrics. It culminates in visualizing results and even predicting outcomes for new data.

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Sections

  • 9

    End-To-End Machine Learning

    This section outlines the components of building an end-to-end machine learning model for predicting student exam performance.

    Learning Practice

  • 9.1

    Dataset Overview

    This section provides an overview of a mock dataset used for predicting student exam performance based on factors such as study hours and attendance.

    Learning Practice

  • 9.2

    Step 1: Data Exploration

    This section covers the initial data exploration phase in machine learning, where we examine our dataset's structure and contents.

    Learning Practice

  • 9.3

    Step 2: Data Preprocessing

    This section explains how to convert categorical features into numerical values using one-hot encoding and mapping techniques.

    Learning Practice

  • 9.4

    Step 3: Feature Selection And Splitting

    In this section, we discuss the process of selecting features and splitting the dataset into training and testing sets for machine learning.

    Learning Practice

  • 9.5

    Step 4: Build The Model – Logistic Regression

    This section focuses on building a logistic regression model to predict student exam performance based on various factors.

    Learning Practice

  • 9.6

    Step 5: Make Predictions

    In this section, we learn how to make predictions using a trained Logistic Regression model for predicting student exam performance.

    Learning Practice

  • 9.7

    Step 6: Evaluate The Model

    This section outlines how to evaluate a machine learning model using various performance metrics.

    Learning Practice

  • 9.8

    Step 7: Visualize The Results

    This section highlights the importance of visualizing the results from a machine learning model using a confusion matrix.

    Learning Practice

  • 9.9

    Step 8: Predict For New Student

    In this section, we learn how to make predictions using a trained machine learning model to assess whether a new student will pass an exam based on their study habits and attendance.

    Learning Practice

  • 9.10

    Summary

    This section summarizes the essential steps learned in building a machine learning model to predict student exam performance.

    Learning Practice

References

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Class Notes

Memorization

What we have learnt

  • The importance of clean and...
  • How to use logistic regress...
  • The role of various evaluat...

Final Test

Revision Tests