Student Academic Performance Analysis and Prediction Using Machine Learning

This book provides a comprehensive overview of machine learning techniques for analyzing and predicting student academic performance. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. The book also includes several case studies that demonstrate how machine learning can be used to improve student outcomes.

STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

by Vivian Siahaan

4.7 out of 5

Language	:	English
File size	:	5013 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	312 pages
Lending	:	Enabled
Screen Reader	:	Supported

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Table of Contents

2. Data Preprocessing
3. Feature Engineering
4. Model Selection
5. Model Evaluation
6. Case Studies

Machine learning is a rapidly growing field that has the potential to revolutionize many industries, including education. Educational data mining (EDM) is a subfield of machine learning that focuses on the analysis of educational data to improve student outcomes.

This book provides a comprehensive overview of machine learning techniques for analyzing and predicting student academic performance. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. The book also includes several case studies that demonstrate how machine learning can be used to improve student outcomes.

Data Preprocessing

Data preprocessing is an important step in any machine learning project. It involves cleaning the data, removing outliers, and normalizing the data. Data preprocessing can help to improve the accuracy and performance of machine learning models.

There are a variety of data preprocessing techniques that can be used, depending on the specific dataset. Some common data preprocessing techniques include:

• Missing value imputation
• Outlier removal
• Data normalization
• Feature scaling

Feature Engineering

Feature engineering is the process of creating new features from the original data. Feature engineering can help to improve the performance of machine learning models by making the data more informative and relevant.

There are a variety of feature engineering techniques that can be used, depending on the specific dataset. Some common feature engineering techniques include:

• One-hot encoding
• Label encoding
• Binning
• Feature selection

Model Selection

Model selection is the process of choosing the best machine learning model for a given dataset. There are a variety of factors that need to be considered when selecting a machine learning model, including the size of the dataset, the type of data, and the desired accuracy.

There are a variety of machine learning models that can be used for analyzing and predicting student academic performance. Some common machine learning models include:

• Linear regression
• Logistic regression
• Decision trees
• Random forests
• Neural networks

Model Evaluation

Model evaluation is the process of assessing the performance of a machine learning model. There are a variety of metrics that can be used to evaluate the performance of a machine learning model, including accuracy, precision, recall, and F1-score.

It is important to evaluate the performance of a machine learning model on a separate dataset that was not used to train the model. This is called cross-validation. Cross-validation helps to ensure that the model is not overfitting the training data.

Case Studies

This book includes several case studies that demonstrate how machine learning can be used to improve student outcomes. These case studies cover a variety of topics, including:

• Predicting student dropout
• Improving student engagement
• Personalizing learning
• Automating educational tasks

This book provides a comprehensive overview of machine learning techniques for analyzing and predicting student academic performance. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and evaluation. The book also includes several case studies that demonstrate how machine learning can be used to improve student outcomes.

Machine learning is a powerful tool that can be used to improve student outcomes. By applying machine learning techniques to educational data, we can gain a better understanding of student learning and create more effective and personalized learning experiences.

STUDENT ACADEMIC PERFORMANCE ANALYSIS AND PREDICTION USING MACHINE LEARNING WITH PYTHON GUI

by Vivian Siahaan

4.7 out of 5

Language	:	English
File size	:	5013 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Print length	:	312 pages
Lending	:	Enabled
Screen Reader	:	Supported

FREE