funky-ml 0.1.4

Automated ML by GD-Singh

Funky-ml

Funky-ML takes data, hues , features, labels as input and performs everything right from Visualisation to Prediction using 11 Models.

Features:
    Visualisations
        1) Bar plots
        2) Box Plots
        3) Distribution Plots
        4) Correlation and Pairplots
    Preprocessing
        1) SMOTE 
        2) Label Encoding and One-Hot Encoding of categorical data.
        3) Splits data into Test and Train Set
        4) Scaling Data
    Prediction
        1) Logistic Regression
        2) Support Vector Classification
        3) K-Nearest Neighbors
        4) Decision Tree Classifiers
        5) GaussianNB
        6) Stochastic Gradient Descent
        7) Random Forest Classifier
        8) AdaBoost Classifier
        9) Gradient Boosting
        10) Light Gradient Boosting
        11) PassivaAggressive Classifier
        
    CrossValidation
        1) K-Fold CV
        2) GridSearch CV
    
    Parameters:
        data : pd.DataFrame
            Dataset
        hues : str
            Hues for visualisation
        features : pd.DataFrame
            Features for Prediction
        labels : pd.DataFrame
            Labels for prediction
        test_size : int or float
            Percentage for test set split. Default = 0.2
        random_state : int
        tune : str
            Whether to enable hypertuning or not. Default = 'n'
        cv_folds : int
            No. of CV Folds. Default 5
            
     Example:
          from funkyml.Funky import funkify
          dataset = pd.read_csv('XYZ.csv')
          features = dataset.iloc[:, :-1]
          lables = dataset.iloc[:, -1]
          funkify(dataset , 'hue' , features, labels)