Title: Machine Learning Analysis for Predicting Student Attrition in Gujarat's Education System
Description: This project involves applying advanced machine learning techniques to analyze and predict student dropout rates within Gujarat's education system. Leveraging a comprehensive dataset encompassing student demographics, economic background, academic performance, and social factors, we aim to uncover intricate patterns and determinants of student attrition. Through rigorous data preprocessing, feature engineering, and model training, we seek to develop robust predictive algorithms capable of accurately forecasting dropout probabilities for individual students. Additionally, we will employ techniques such as feature importance analysis and model interpretability to gain insights into the underlying factors driving student attrition. The findings from this study have significant implications for educational policymakers and stakeholders, providing actionable insights to design targeted interventions aimed at mitigating dropout rates and fostering student retention.
Objective: The primary technical objectives of this project are as follows:
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Data Exploration and Preprocessing:
- Explore the dataset to understand its structure, features, and distributions.
- Perform data preprocessing tasks such as handling missing values, encoding categorical variables, and standardizing numerical features.
- Conduct exploratory data analysis (EDA) to identify potential correlations and patterns related to student dropout.
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Feature Engineering and Selection:
- Engineer new features or transformations to extract relevant information from the dataset.
- Select informative features using techniques such as correlation analysis, mutual information, or feature importance ranking.
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Model Development:
- Design and train machine learning models to predict student dropout probabilities.
- Experiment with various algorithms including but not limited to logistic regression, decision trees, random forests, support vector machines (SVM), and gradient boosting machines (GBM).
- Fine-tune model hyperparameters using techniques such as grid search or random search to optimize performance metrics.
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Model Evaluation and Interpretation:
- Evaluate model performance using appropriate metrics such as accuracy, precision, recall, F1-score, and area under the ROC curve (AUC-ROC).
- Validate models using techniques such as cross-validation to assess generalization performance.
- Interpret model predictions and feature importance to gain insights into factors contributing to student dropout.