Student Performance Factors

1.importing libraries , exploring data

2.( Nan values (SimpleImputer "most_frequent"

2.outliers (IQR)

3. Encoding (OneHotEncoder - LabelEncoder)

4.(StratifiedKFold)Visualization and Splitting the Data

5.Normalization (PowerTransformer)

6.Classifiers Models : 12 Machine Learning Moddel is Applied to the data set , and finally ( Grid Search & Random Search ) for the best model