{"id":26201699,"url":"https://github.com/naveen88112/final_education","last_synced_at":"2026-04-11T12:33:42.433Z","repository":{"id":281818117,"uuid":"946488557","full_name":"Naveen88112/Final_Education","owner":"Naveen88112","description":"Student Performance Prediction This project examines the student performance data, pre-processes the features, and implements machine learning methods (Random Forest) for the forecasting of final grades. 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It involves preprocessing educational data, conducting exploratory data analysis (EDA), and training a classification model to predict final grades.\n\nFeatures\n- Data Preprocessing: Standardization, encoding of categorical features, and handling missing values.\n- Exploratory Data Analysis(EDA): Statistical summaries and visualizations.\n- Machine Learning Model: Random Forest Classifier trained to predict student grades.\n- Performance Evaluation: Accuracy score used to assess model effectiveness.\n\nTechnologies Used\n- Python\n- Pandas \u0026 NumPy\n- Scikit-learn\n- Matplotlib \u0026 Seaborn\n\nHow to Run\n1. Clone the repository:\n   \n   \"git clone https://github.com/yourusername/student-performance-prediction.git\"\n\n2. Open the Jupyter Notebook or Google Colab.\n3. Upload the dataset (if required) and execute the cells step by step.\n\nResults \u0026 Insights\n- Feature preprocessing improved model performance.\n- The Random Forest model was used to classify student performance.\n- EDA provided insights into the factors affecting student grades.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnaveen88112%2Ffinal_education","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnaveen88112%2Ffinal_education","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnaveen88112%2Ffinal_education/lists"}