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Pull request overview
This PR significantly improves the README.md by transforming it from a minimal 5-line instruction file into a comprehensive, well-structured documentation with over 120 lines covering project overview, features, installation, workflow, and usage details.
Key Changes:
- Added detailed project description with technical overview and workflow documentation
- Included comprehensive installation instructions and prerequisites
- Added structured sections for dataset description, project structure, results analysis, and contribution guidelines
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| ## 📁 Project Structure | ||
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| ``` | ||
| ├── datasets.csv # Student performance dataset |
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The filename listed here is incorrect. The actual dataset file is named "xAPI-Edu-Data.csv" (as correctly mentioned in line 30 and used in student.py), not "datasets.csv". This inconsistency could confuse users about which file to use.
| ├── datasets.csv # Student performance dataset | |
| ├── xAPI-Edu-Data.csv # Student performance dataset |
| ## 🔍 Results & Analysis | ||
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| The analysis identifies which factors most strongly predict student performance: | ||
| - **Top Predictors**: VisitedResources, RaisedHands, AnnouncementsView, StudentAbsenceDays, Discussion |
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The feature name capitalization is inconsistent with the actual code. In student.py (line 74), the feature is named 'VisITedResources' (with capital I and T), not 'VisitedResources'. Similarly, 'raisedhands' is all lowercase, not 'RaisedHands'. While this is describing the results, using the exact feature names from the code would improve accuracy.
| - **Top Predictors**: VisitedResources, RaisedHands, AnnouncementsView, StudentAbsenceDays, Discussion | |
| - **Top Predictors**: VisITedResources, raisedhands, AnnouncementsView, StudentAbsenceDays, Discussion |
| Kudos. | ||
| # Student Performance Analysis using Big Data | ||
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| A machine learning project that analyzes and predicts student academic performance based on educational behavioral data. This project leverages multiple regression and ensemble algorithms to identify key factors influencing student success. |
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The description is not entirely accurate. The code actually uses both classification and regression algorithms. A RandomForestClassifier is used for feature importance analysis (line 61 in student.py), while regression algorithms are used for the prediction task. The statement "machine learning project that analyzes and predicts student academic performance based on educational behavioral data" could be clarified to reflect that it's primarily a classification problem (predicting student performance class/level).
| - Remove irrelevant features | ||
| - Encode categorical variables | ||
| - Scale features for algorithms | ||
| 3. **Dimensionality Reduction**: Identify and retain top 6 most important features |
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The statement about identifying the "top 6 most important features" is misleading. The code in student.py (line 74) hardcodes a specific list of 6 features to retain, but this is not based on automatically selecting the "top 6" from the feature importance analysis. The features are manually specified in a list, not dynamically selected based on importance rankings.
| 3. **Dimensionality Reduction**: Identify and retain top 6 most important features | |
| 3. **Dimensionality Reduction**: Retain a predefined subset of 6 important features as specified in the script |
1 participant
Hello, I noticed that project is good but the README.md needs some enhancing, so I modified It.