{"id":30668140,"url":"https://github.com/farhad-here/predict_student_performance","last_synced_at":"2026-04-14T19:31:23.131Z","repository":{"id":311000091,"uuid":"1042071876","full_name":"farhad-here/Predict_student_performance","owner":"farhad-here","description":"Predict Student Performance, is a data analysis and machine learning project aimed at predicting students' final performance (g3) based on demographic, family, and academic features. 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Through this project, a full data pipeline has been created, covering everything from exploratory data analysis (EDA) to the construction and evaluation of a machine learning model.\n\nThis project can help educators and institutions proactively identify students at risk of underperformance, allowing for timely and effective interventions.\n\n---\n\n## 📓 Description\nAn end-to-end data analysis and machine learning project for predicting student performance using educational and social data. The project includes data analysis, model building, and an interactive dashboard.\n---\n\n## 🎯 The Problem\n\nStudent academic success is influenced by numerous factors. By analyzing data related to study habits, family status, and other behavioral characteristics, we can build a model that predicts which students might receive a low final grade. This prediction enables a proactive, preventative approach rather than a reactive one.\n\n---\n\n## 🛠️ Project Architecture \u0026 Workflow\n\nThis project follows a structured and modular workflow:\n\n1.  **Data Collection \u0026 Cleaning:**\n    * The dataset is loaded from a CSV file, and cleaning and preprocessing operations (such as handling missing values and correcting data types) are performed.\n\n2.  **Exploratory Data Analysis (EDA):**\n    * A comprehensive statistical and visual analysis is conducted to uncover hidden relationships between features and the final student grade.\n    * Visualizations such as a Correlation Matrix, Histograms, and Scatter Plots are created for a better understanding of the data.\n\n3.  **Feature Engineering \u0026 Preprocessing:**\n    * Categorical features are encoded numerically to make them suitable for machine learning models.\n    * The data is split into training and testing sets.\n\n4.  **Machine Learning Model:**\n    * A classification model is trained to predict the final student grade (e.g., pass or fail).\n    * The model's performance is measured using appropriate evaluation metrics (such as accuracy, F1-Score, and a Confusion Matrix).\n\n5.  **Conclusion \u0026 Insights:**\n    * A summary of key findings and important insights derived from the data analysis is presented.\n\n---\n\n## 💻 Technical Stack \u0026 Libraries\n\n* **Programming Language:** Python\n* **Data Analysis:** `Pandas`, `NumPy`\n* **Data Visualization:** `Matplotlib`, `Seaborn`\n* **Machine Learning:** `Scikit-learn`\n* **Development Environment:** `Jupyter Notebook`\n\n---\n\n## 🏙️ Dataset\nThe dataset includes features like:\n- **Demographic**: `sex`, `age`, `address`, `famsize`\n- **Family and Education**: `medu`, `fedu`, `mjob`, `fjob`\n- **Lifestyle and Social**: `famrel`, `freetime`, `goout`, `dalc`, `walc`, `health`\n- **Academic**: `absences`, `g1`, `g2`, and the target `g3`\n\n\n---\n## ✋ Approach\n\n- **Regression**  \n  - Predict `g3` as a numerical value (range: 0–20).\n- **Classification**  \n  - Convert `g3` into two classes:  \n    - **Fail**: 0–9  \n    - **Pass**: 10–20  \n\n---\n\n## ▶️ How to Run the Project\n\nFollow these steps to run the project on your local machine:\n\n1.  **Clone the Repository:**\n    ```bash\n    git clone [https://github.com/farhad-here/Predict_student_performance.git](https://github.com/farhad-here/Predict_student_performance.git)\n    cd Predict_student_performance\n    ```\n\n2.  **Install Dependencies:**\n    * It is highly recommended to use a virtual environment.\n    ```bash\n    pip install -r requirements.txt\n    ```\n\n3.  **Run the Project:**\n    * Open the `Jupyter Notebook` file and run all the cells in order.\n    ```bash\n    jupyter notebook\n    ```\n\n\n## ✉️ PowerBi Dashboard\n\n\u003cimg width=\"2071\" height=\"1165\" alt=\"powerstudenbi\" src=\"https://github.com/user-attachments/assets/b7e9b132-a3b5-458b-86e6-c925fb3f9965\" /\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarhad-here%2Fpredict_student_performance","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffarhad-here%2Fpredict_student_performance","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarhad-here%2Fpredict_student_performance/lists"}