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End to End Project for Student Performance Prediction

Technologies used - -> scikit-learn -> numpy, pandas, matplotlib, seaborn -> LinearRegression, K-NearestRegressor, DecisionTreeRegressor, AdaBoostRegressor , CatBoostRegressor , GradientBoostRegressor, RandomForestRegressor, XGBoostRegressor ->Flask, html, Python

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The project predict the score a student probably can score in an exam based on historical study patterns such as previous marks, study hours, study source etc.This is an end-to-end project from analysing data to prediction app using Flask.

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