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🚀 Mission: Decoding Student Success

"Grades are not just numbers; they are the output of a lifestyle algorithm."

Traditional systems wait for failure. CogniPath predicts it. By engineering a custom Cognitive Efficiency Ratio, this engine correlates a student's biological habits (Sleep, Attendance) with their academic trajectory, identifying "Burnout Risk" before exam day.

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⚡ Key Algorithmic Innovations

The engineering logic behind the predictions:

Feature	The Science Behind It
🧠 Cognitive Efficiency	Custom Metric: $\frac{\text{Study Hours}}{(\text{Sleep Hours} + 1)}$ Detects students who study hard but retain less due to sleep deprivation.
🛡️ Risk Segmentation	Dynamic Vectorization: Automatically flags students as High Risk if attendance $< 75%$ using NumPy vectorized operations.
🌲 Ensemble Learning	Gradient Boosting Regressor: Uses a sequence of decision trees where each tree corrects the errors of the previous one.
⚙️ Pipeline Automation	Scikit-Learn Pipelines: Encapsulates Scaling (StandardScaler) and Encoding (OneHotEncoder) to prevent data leakage.

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🛠️ System AND Architecture

Live Data Flow Visualization (Auto-Generated):

graph LR
    subgraph Data Ingestion
        A[📂 train.csv] -->|Load| B(DataEngineer Class)
    end
    
    subgraph Feature Engineering
        B -->|Calc| C{Efficiency Ratio}
        B -->|Segment| D{Attendance Risk}
        C & D -->|Transform| E[Preprocessing Pipeline]
    end
    
    subgraph AI Core
        E -->|Train| F[Gradient Boosting Model]
        F -->|Predict| G[🎯 Exam Score Forecast]
    end
    
    style F fill:#0072ff,stroke:#00c6ff,stroke-width:2px,color:#fff
    style C fill:#00c6ff,stroke:#0072ff,stroke-width:2px,color:#000
    style D fill:#ff5252,stroke:#333,stroke-width:2px,color:#fff

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