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README.md · Python personal · maintained

>_ Student-Performance-Predictor

End-to-end ML project predicting student performance — preprocessing, feature engineering, training, Streamlit web app.

data #ml#data#feature-engineering#streamlit

This is the ML project I finally let become a pipeline instead of a chaotic notebook. It predicts student academic performance from survey-style features, end to end: raw CSV → cleaned DataFrame → engineered features → trained model → interactive Streamlit app.

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor

pipeline = Pipeline([
    ("scale", StandardScaler()),
    ("model", RandomForestRegressor(n_estimators=120, random_state=42)),
])

Structure

  • data/ — raw + processed.
  • notebooks/ — EDA done once, then frozen into modules.
  • src/ — the pipeline as functions, so the app reuses the same code.
  • app.py — Streamlit frontend with a slider board for each feature.

Lessons

  • Version your data, then your model, then your vibes.
  • Feature engineering beats model choice 9 times out of 10 at this scale.
  • Streamlit makes “show the work” trivial — a good demo is a feature.