diff --git a/anai/__init__.py b/anai/__init__.py
index a9fe722..02ecfd3 100644
--- a/anai/__init__.py
+++ b/anai/__init__.py
@@ -30,6 +30,7 @@ def run(
     df=None,
     target: str = None,
     filepath: str = None,
+    df_kwargs: dict = {},
     config: bool = False,
     except_columns: list = [],
     predictor: list = [],
@@ -64,6 +65,10 @@ def run(
                 DataFrame to be used for modelling.
             target : str
                 Target Column Name 
+            filepath : str
+                Filepath of the dataframe to be loaded.
+            df_kwargs : dict
+                Keyword arguments for the dataframe loading function. Only used if filepath is not None.
             except_columns : list, optional
                 List of Columns to be excluded from the dataset
             predictor : list
@@ -137,6 +142,7 @@ def run(
             
             ai = anai.run(
                         filepath='examples/Folds5x2_pp.xlsx', 
+                        df_kwargs={'sheet_name': 'Sheet1'},
                         target='PE',
                         predictor=['lin'],
             )
@@ -166,7 +172,7 @@ def run(
                 raise FileNotFoundError("ANAI Config File Not Found")
         if df is None:
             if filepath is not None:
-                df = df_loader(filepath, suppress=True)
+                df = df_loader(filepath, suppress=True, **df_kwargs)
             else:
                 raise ValueError("Please provide a dataframe or a filepath")
         if __task(df, target) and not suppress_task_detection or task == "regression":
@@ -182,6 +188,7 @@ def run(
                 df=df,
                 target=target,
                 filepath=filepath,
+                df_kwargs=df_kwargs,
                 config=config,
                 except_columns=except_columns,
                 predictor=predictor,
@@ -206,6 +213,7 @@ def run(
                 metric=metric,
                 ensemble=ensemble,
             )
+            regressor.fit()
             return regressor
         elif (
             not __task(df, target)
@@ -224,6 +232,7 @@ def run(
                 df=df,
                 target=target,
                 filepath=filepath,
+                df_kwargs=df_kwargs,
                 config=config,
                 except_columns=except_columns,
                 predictor=predictor,
@@ -243,6 +252,7 @@ def run(
                 ensemble=ensemble,
                 exclude_models=exclude_models,
             )
+            classifier.fit()
             return classifier
     except KeyboardInterrupt:
         if os.path.exists(os.getcwd() + "/dask-worker-space"):
@@ -283,11 +293,12 @@ def __task(df, target):
         return False
 
 
-def load(df_filepath):
+def load(df_filepath, **df_kwargs):
     """Loads a dataframe from a filepath.
 
     Args:
         df_filepath (str): Filepath of the dataframe to be loaded.
+        df_kwargs (dict): Keyword arguments to be passed to df_loader function.
 
     Returns:
         pd.DataFrame : Loaded dataframe.
@@ -296,12 +307,12 @@ def load(df_filepath):
     
     suppress = False
     if type(df_filepath) is str:
-        df = __df_loader_single(df_filepath, suppress=False)
+        df = __df_loader_single(df_filepath, suppress=False, **df_kwargs)
     elif type(df_filepath) is list:
         print(Fore.YELLOW + "Loading Data [*]\n")
         df = pd.concat(
             [
-                __df_loader_single(df_filepath[i], suppress=True)
+                __df_loader_single(df_filepath[i], suppress=True, **df_kwargs)
                 for i in range(len(df_filepath))
             ]
         )
diff --git a/anai/supervised/__init__.py b/anai/supervised/__init__.py
index 669e151..f56e44f 100644
--- a/anai/supervised/__init__.py
+++ b/anai/supervised/__init__.py
@@ -39,6 +39,7 @@ def __init__(
         df=None,
         target: str = None,
         filepath: str = None,
+        df_kwargs: dict = {},
         config: bool = False,
         except_columns: list = [],
         predictor: list =["lr"],
@@ -68,11 +69,16 @@ def __init__(
 
         Parameters:
 
-            features : array
-                        features array
-            lables : array
-                        labels array
-            except_columns (list): [List of Columns to be excluded from the dataset]
+            df : Pandas DataFrame
+                DataFrame to be used for modelling.
+            target : str
+                Target Column Name 
+            filepath : str
+                Filepath of the dataframe to be loaded.
+            df_kwargs : dict
+                Keyword arguments for the dataframe loading function. Only used if filepath is not None.
+            except_columns : list, optional
+                List of Columns to be excluded from the dataset
             predictor : list
                         Predicting model to be used
                         Default ['lr']  - Logistic Regression\n
@@ -116,7 +122,7 @@ def __init__(
             params : dict
                         contains parameters for model
             tune : boolean
-                    when True Applies GridSearch CrossValidation
+                    when True Applies Optuna to find best parameters for model
                     Default is False
             test_size: float or int, default=.2
                         If float, should be between 0.0 and 1.0 and represent
@@ -125,21 +131,18 @@ def __init__(
                         If int, represents the absolute number of test samples.
             cv_folds : int
                     No. of cross validation folds. Default = 10
-            pca : str
-                if 'y' will apply PCA on Train and Validation set. Default = 'n'
+            pca : bool
+                if True will apply PCA on Train and Validation set. Default = False
             lda : str
-                if 'y' will apply LDA on Train and Validation set. Default = 'n'
+                if True will apply LDA on Train and Validation set. Default = False
             pca_kernel : str
                     Kernel to be use in PCA. Default = 'linear'
             n_components_lda : int
                     No. of components for LDA. Default = 1
             n_components_pca : int
                     No. of components for PCA. Default = 2
-            loss : str
-                    loss method for ann. Default = 'binary_crossentropy'
-                    rate for dropout layer. Default = 0
-            smote : str,
-                Whether to apply SMOTE. Default = 'y'
+            smote : Bool,
+                Whether to apply SMOTE. Default = True
             k_neighbors : int
                 No. of neighbors for SMOTE. Default = 1
             verbose : boolean
@@ -160,12 +163,8 @@ def __init__(
                     minimize : Minimize
             optuna_n_trials : int
                 No. of trials for optuna. Default = 100
-            optuna_metric: str
-                Metric to be used in optuna. Default = 'R^2'
-            lgbm_objective : str
-                Objective for lgbm classifier. Default = 'binary'
             ensemble : boolean
-                Whether to use ensemble. Default = True
+                Whether to use ensemble methods. Default = True
 
         Returns:
 
@@ -178,6 +177,7 @@ def __init__(
             import anai
             ai = anai.run(
                         filepath='examples/test_data.csv', 
+                        df_kwargs={'index_col':'id'},
                         target='PE',
                         predictor=['lr'],
             )
@@ -196,7 +196,7 @@ def __init__(
                 raise FileNotFoundError("ANAI Config File Not Found")
         if df is None:
             if filepath is not None:
-                df = df_loader(filepath)
+                df = df_loader(filepath, **df)
             else:
                 raise ValueError("Please provide a dataframe or a filepath")
         if type(predictor) == list:
@@ -295,9 +295,8 @@ def __init__(
         self.dimension_handler = DimensionHandler()
         self.encoder = None
         self.le_encoder = None
-        self.__fit()
 
-    def __fit(self):
+    def fit(self):
         """[Takes Features and Labels and Encodes Categorical Data then Applies SMOTE , Splits the features and labels in training and validation sets with test_size = .2
         scales X_train, self.X_val using StandardScaler.
         Fits every model on training set and predicts results,
@@ -760,9 +759,21 @@ def __load(self, path=None):
         else:
             raise ValueError("No path specified.Please provide actual path\n")
 
-    def explain(self, method):
+    def explain(self, method, show_graph=True):
         """
-        Returns the importance features of the dataset
+        Explains the model using the specified method.
+        
+        args:
+            method (str): [Method to use for explaining the model]
+                Available methods:
+                    - shap
+                    - perm
+
+            show_graph (bool): [Whether to show the graph or not]
+            
+        returns:
+            [DataFrame] : [Explained DataFrame]
+        
         """
         columns = self.features.columns
         self.explainer.set_params(
@@ -775,6 +786,7 @@ def explain(self, method):
             self.fit_params,
             False,
             columns,
+            show_graph,
         )
         if self.pred_mode == "all":
             classifier = copy.deepcopy(self.best_classifier.model)
@@ -804,6 +816,7 @@ def __init__(
         df=None,
         target: str = None,
         filepath: str = None,
+        df_kwargs: dict = {},
         config: bool = False,
         except_columns: list = [],
         predictor: list = ["lin"],
@@ -833,6 +846,10 @@ def __init__(
         Parameters:
             df (dataframe): [Dataset containing features and target]
             target (str): [Target Column Name]
+            filepath : str
+                Filepath of the dataframe to be loaded.
+            df_kwargs : dict
+                Keyword arguments for the dataframe loading function. Only used if filepath is not None.
             except_columns (list): [List of Columns to be excluded from the dataset]
             predictor : list
                         Predicting models to be used
@@ -931,6 +948,7 @@ def __init__(
             
             ai = anai.run(
                         filepath='examples/Folds5x2_pp.xlsx', 
+                        df_kwargs={'sheet_name': 'Sheet1'},
                         target='PE',
                         predictor=['lin'],
             )
@@ -949,7 +967,7 @@ def __init__(
                 raise FileNotFoundError("ANAI Config File Not Found")
         if df is None:
             if filepath is not None:
-                df = df_loader(filepath)
+                df = df_loader(filepath, **df_kwargs)
             # elif config_filepath is not None:
             #     df, target = load_data_from_config(config_filepath)
             else:
@@ -1048,9 +1066,8 @@ def __init__(
         self.encoder = None
         self.features = None
         self.labels = None
-        self.__fit()
 
-    def __fit(self):
+    def fit(self):
         """[Takes Features and Labels and Encodes Categorical Data then Applies SMOTE , Splits the features and labels in training and validation sets with test_size = .2
         scales X_train, X_val using StandardScaler.
         Fits model on training set and predicts results, Finds R^2 Scoreand mean square error
@@ -1573,9 +1590,21 @@ def __load(self, path=None):
         else:
             raise ValueError("No path specified.Please provide actual path\n")
 
-    def explain(self, method):
+    def explain(self, method, show_graph=True):
         """
-        Returns the importance features of the dataset
+        Explains the model using the specified method.
+        
+        args:
+            method (str): [Method to use for explaining the model]
+                Available methods:
+                    - shap
+                    - perm
+
+            show_graph (bool): [Whether to show the graph or not]
+            
+        returns:
+            [DataFrame] : [Explained DataFrame]
+        
         """
         self.explainer.set_params(
             self.features,
@@ -1585,6 +1614,7 @@ def explain(self, method):
             self.y_val,
             self.cv_folds,
             self.fit_params,
+            self.show_graph,
         )
         if self.pred_mode == "all":
             regressor = copy.deepcopy(self.best_regressor.model)
@@ -1595,14 +1625,12 @@ def explain(self, method):
             )
             print(Fore.YELLOW + "Explaining ANAI [*]\n")
 
-        if self.original_predictor == "all":
-            raise TypeError(
-                "[Error] This method is only applicable on single predictor"
-            )
-        elif method == "perm":
-            self.explainer.permutation(model=regressor)
+        if method == "perm":
+            res = self.explainer.permutation(model=regressor)
+            return res
         elif method == "shap":
-            self.explainer.shap(model=regressor)
+            res = self.explainer.shap(model=regressor)
+            return res
         else:
             raise NotImplementedError(
                 "Technique not implemented. Please choose from perm, shap"
diff --git a/anai/utils/connectors/data_handler.py b/anai/utils/connectors/data_handler.py
index ae366b8..1578dbe 100644
--- a/anai/utils/connectors/data_handler.py
+++ b/anai/utils/connectors/data_handler.py
@@ -8,7 +8,9 @@ def __df_loader_single(
     obj=None,
     objfilepath=None,
     suppress=False,
+    df_kwargs = {}
 ):
+    kwargs = df_kwargs
     df = None
     flag = 0
     if obj is None:
@@ -20,32 +22,32 @@ def __df_loader_single(
         # print(path)
         flag = 1
     if path.endswith(".csv") or path.startswith("http") or path.startswith("https"):
-        df = pd.read_csv(df_filepath)
+        df = pd.read_csv(df_filepath, **df_kwargs)
     elif path.endswith(".xlsx"):
         if flag == 1:
-            df = pd.read_excel(io.BytesIO(df_filepath.read()))
+            df = pd.read_excel(io.BytesIO(df_filepath.read()), **df_kwargs)
         else:
-            df = pd.read_excel(df_filepath)
+            df = pd.read_excel(df_filepath, **df_kwargs)
     elif path.endswith(".pkl"):
-        df = pd.read_pickle(df_filepath)
+        df = pd.read_pickle(df_filepath, **df_kwargs)
     elif path.endswith(".h5"):
-        df = pd.read_hdf(df_filepath)
+        df = pd.read_hdf(df_filepath, **df_kwargs)
     elif path.endswith(".feather"):
-        df = pd.read_feather(df_filepath)
+        df = pd.read_feather(df_filepath, **df_kwargs)
     elif path.endswith(".parquet"):
-        df = pd.read_parquet(df_filepath)
+        df = pd.read_parquet(df_filepath, **df_kwargs)
     elif path.endswith(".json"):
-        df = pd.read_json(df_filepath)
+        df = pd.read_json(df_filepath, **df_kwargs)
     elif path.endswith(".html"):
-        df = pd.read_html(df_filepath)
+        df = pd.read_html(df_filepath, **df_kwargs)
     elif path.endswith(".stata"):
-        df = pd.read_stata(df_filepath)
+        df = pd.read_stata(df_filepath, **df_kwargs)
     elif path.endswith(".sas7bdat"):
-        df = pd.read_sas(df_filepath)
+        df = pd.read_sas(df_filepath, **df_kwargs)
     elif path.endswith(".msgpack"):
-        df = pd.read_msgpack(df_filepath)
+        df = pd.read_msgpack(df_filepath, **df_kwargs)
     elif path.endswith(".jsonl"):
-        df = pd.read_json(df_filepath, lines=True)
+        df = pd.read_json(df_filepath, lines=True, **df_kwargs)
     else:
         raise Exception(
             "File extension not supported. Use .csv, .xlsx, .pkl, .h5, .feather, .parquet, .json, .html, .stata, .sas7bdat, .msgpack , .jsonl OR use df argument"
@@ -63,14 +65,14 @@ def __df_loader_single(
     return df
 
 
-def df_loader(df_filepath, obj=None, objfilepath=None, suppress=False):
+def df_loader(df_filepath, obj=None, objfilepath=None, suppress=False, df_kwargs={}):
     if type(df_filepath) is str:
-        df = __df_loader_single(df_filepath, obj, objfilepath, suppress)
+        df = __df_loader_single(df_filepath, obj, objfilepath, suppress, **df_kwargs)
     elif type(df_filepath) is list:
         print(Fore.YELLOW + "Loading Data [*]\n")
         df = pd.concat(
             [
-                __df_loader_single(df_filepath[i], obj, objfilepath, True)
+                __df_loader_single(df_filepath[i], obj, objfilepath, True, **df_kwargs)
                 for i in range(len(df_filepath))
             ]
         )
diff --git a/anai/utils/explainable_anai/explain_core.py b/anai/utils/explainable_anai/explain_core.py
index 542076c..8a601ae 100644
--- a/anai/utils/explainable_anai/explain_core.py
+++ b/anai/utils/explainable_anai/explain_core.py
@@ -18,6 +18,7 @@ def __init__(self):
         self.isReg = None
         self.columns = None
         self.y_test = None
+        self.show_graph = None
 
     def set_params(
         self,
@@ -30,6 +31,7 @@ def set_params(
         fit_params={},
         isReg=True,
         columns=None,
+        show_graph=True,
     ):
         self.features = features
         self.X_train = X_train
@@ -40,16 +42,18 @@ def set_params(
         self.fit_params = fit_params
         self.isReg = isReg
         self.columns = columns
-
+        self.show_graph = show_graph
+        
     def permutation(self, model):
         try:
-            permutational_feature_importance(
-                self.features.columns, self.X_train, self.y_train, model, self.isReg
+            res = permutational_feature_importance(
+                self.features.columns, self.X_train, self.y_train, model, self.isReg, self.show_graph
             )
+            return res
         except Exception as e:
             print(Fore.YELLOW + "Automatically switching to Surrogate mode\n")
             try:
-                permutational_feature_importance(
+                res = permutational_feature_importance(
                     self.features.columns,
                     self.X_train,
                     self.y_train,
@@ -57,7 +61,9 @@ def permutation(self, model):
                         model, self.X_train, self.y_train, isReg=self.isReg
                     ),
                     self.isReg,
+                    self.show_graph,
                 )
+                return res
             except Exception as e:
                 print(e)
                 print(traceback.format_exc())
@@ -66,15 +72,17 @@ def permutation(self, model):
 
     def shap(self, model):
         try:
-            shap_feature_importance(self.features.columns, self.X_train, model)
+            res = shap_feature_importance(self.features.columns, self.X_train, model, self.isReg, self.show_graph)
+            return res
         except Exception as e:
             print(Fore.YELLOW + "Automatically switching to Surrogate mode\n")
             try:
-                shap_feature_importance(
+                res = shap_feature_importance(
                     self.features.columns,
                     self.X_train,
-                    surrogate_decision_tree(model, self.X_train, isReg=self.isReg),
+                    surrogate_decision_tree(model, self.X_train, isReg=self.isReg, show_graph=self.show_graph),
                 )
+                return res
             except Exception as e:
                 print(e)
                 print(traceback.format_exc())
diff --git a/anai/utils/explainable_anai/permutation.py b/anai/utils/explainable_anai/permutation.py
index ee470d8..08bba83 100644
--- a/anai/utils/explainable_anai/permutation.py
+++ b/anai/utils/explainable_anai/permutation.py
@@ -4,7 +4,7 @@
 import modin.pandas as pd
 
 
-def permutational_feature_importance(columns, X_test, y_test, model, isReg=False):
+def permutational_feature_importance(columns, X_test, y_test, model, isReg=False, show_graph=True):
     perm_importance = permutation_importance(
         model,
         X_test,
@@ -20,20 +20,22 @@ def permutational_feature_importance(columns, X_test, y_test, model, isReg=False
     a = dict(sorted(perm_dict.items(), key=lambda item: item[1], reverse=False))
     df1 = pd.DataFrame(a.items(), columns=["Column Name", "Permutation Value"])
     df1["Color"] = np.where(df1["Permutation Value"] < 0, "red", "green")
-    fig = go.Figure()
-    fig.add_trace(
-        go.Bar(
-            name="Net",
-            x=df1["Permutation Value"],
-            y=df1["Column Name"],
-            marker_color=df1["Color"],
-            orientation="h",
+    if show_graph:
+        fig = go.Figure()
+        fig.add_trace(
+            go.Bar(
+                name="Net",
+                x=df1["Permutation Value"],
+                y=df1["Column Name"],
+                marker_color=df1["Color"],
+                orientation="h",
+            )
         )
-    )
-    fig.update_layout(
-        template="plotly_dark",
-        title_text="Permutation Feature Importance",
-        xaxis_title="Permutation Value",
-        yaxis_title="Feature Name",
-    )
-    fig.show()
+        fig.update_layout(
+            template="plotly_dark",
+            title_text="Permutation Feature Importance",
+            xaxis_title="Permutation Value",
+            yaxis_title="Feature Name",
+        )
+        fig.show()
+    return df1
diff --git a/anai/utils/explainable_anai/shap.py b/anai/utils/explainable_anai/shap.py
index 5562e39..350314c 100644
--- a/anai/utils/explainable_anai/shap.py
+++ b/anai/utils/explainable_anai/shap.py
@@ -2,26 +2,27 @@
 import numpy as np
 
 
-def shap_feature_importance(columns, X_train, model, *args, **kwargs):
+def shap_feature_importance(columns, X_train, model, show_graph, *args, **kwargs):
     explainer = shap.TreeExplainer(model)
     shap_values = np.array(explainer.shap_values(X_train))
     if shap_values.ndim == 3:
         shap_values = np.array(shap_values[1] + shap_values[1][1], )
     columns = columns
-    shap.summary_plot(
-        shap_values,
-        X_train,
-        feature_names=columns,
-        plot_type="bar",
-        *args,
-        **kwargs
-    )
-    shap.summary_plot(
-        shap_values, X_train, feature_names=columns, *args, **kwargs
-    )
-    for i in range(0, len(columns)):
-        shap.dependence_plot(
-            i, shap_values, X_train, feature_names=columns,
+    if show_graph:
+        shap.summary_plot(
+            shap_values,
+            X_train,
+            feature_names=columns,
+            plot_type="bar",
+            *args,
+            **kwargs
         )
+        shap.summary_plot(
+            shap_values, X_train, feature_names=columns, *args, **kwargs
+        )
+        for i in range(0, len(columns)):
+            shap.dependence_plot(
+                i, shap_values, X_train, feature_names=columns,
+            )
 
     return shap_values
diff --git a/anai/utils/tuner/optuna/objectives/regression_objectives.py b/anai/utils/tuner/optuna/objectives/regression_objectives.py
index dfe9eda..a04c9f2 100644
--- a/anai/utils/tuner/optuna/objectives/regression_objectives.py
+++ b/anai/utils/tuner/optuna/objectives/regression_objectives.py
@@ -36,7 +36,6 @@
     SGDRegressor,
     TweedieRegressor,
 )
-from sklearn.linear_model._glm import GeneralizedLinearRegressor
 from sklearn.model_selection import cross_val_score
 from sklearn.neighbors import KNeighborsRegressor
 from sklearn.neural_network import MLPRegressor
diff --git a/docs/Features.md b/docs/Features.md
index 77d82e8..22aedf3 100644
--- a/docs/Features.md
+++ b/docs/Features.md
@@ -10,7 +10,7 @@
 ### Initialization
     import anai
     from anai.preprocessing import Preprocessor
-    df = anai.load("data/bodyPerformance.csv")
+    df = anai.load("data/bodyPerformance.csv", df_kwargs={"header": None})
     prep = Preprocessor(dataset=df, target="class", except_columns=['weight_kg'])
 
 ### Available Preprocessing Methods
@@ -89,6 +89,10 @@
 ### Arguments
     df : Pandas DataFrame
         DataFrame to be used for modelling.
+    filepath : str
+        Filepath of the dataframe to be loaded.
+    df_kwargs : dict
+        Keyword arguments for the dataframe loading function. Only used if filepath is not None.
     target : str
         Target Column Name 
     except_columns : list, optional
diff --git a/docs/index.md b/docs/index.md
index 16a3c9c..8a1a352 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -22,6 +22,10 @@ ANAI is an Automated Machine Learning Python Library that works with tabular dat
         DataFrame to be used for modelling.
     target : str
         Target Column Name 
+    filepath : str
+        Filepath of the dataframe to be loaded.
+    df_kwargs : dict
+        Keyword arguments for the dataframe loading function. Only used if filepath is not None.
     except_columns : list, optional
         List of Columns to be excluded from the dataset
     predictor : list
diff --git a/docs/old_doc_files/AutoML.md b/docs/old_doc_files/AutoML.md
index ee41c7d..07ee6a9 100644
--- a/docs/old_doc_files/AutoML.md
+++ b/docs/old_doc_files/AutoML.md
@@ -5,12 +5,16 @@
 ### Initialization
 
     import anai
-    ai = anai.run(filepath="data/iris.csv", target="class", predictor="lr")
+    ai = anai.run(filepath="data/iris.csv", df_kwargs={"header": None}, target="class", predictor="lr")
 
 ### Arguments
 
         df : Pandas DataFrame
             DataFrame to be used for modelling.
+        filepath : str
+            Filepath of the dataframe to be loaded.
+        df_kwargs : dict
+            Keyword arguments for the dataframe loading function. Only used if filepath is not None.
         target : str
             Target Column Name 
         except_columns : list, optional
diff --git a/requirements.txt b/requirements.txt
index 91f9299..e93378b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,7 +10,7 @@ google-cloud-bigquery
 google-cloud-bigquery-storage
 h11
 imbalanced-learn
-lightgbm
+lightgbm; platform_system=="Linux" or platform_system=="Windows"
 matplotlib
 matplotlib-inline
 networkx
@@ -39,4 +39,4 @@ snowflake-connector-python
 snowflake-sqlalchemy
 python-Levenshtein-wheels; platform_system=="Windows"
 python-Levenshtein; platform_system=="Linux" or platform_system=="Darwin"
-mlxtend==0.20.0
+mlxtend==0.20.0
\ No newline at end of file
diff --git a/setup.py b/setup.py
index a9e012c..a8db8ea 100644
--- a/setup.py
+++ b/setup.py
@@ -21,7 +21,7 @@
         ],
         include=["anai.*", "anai"],
     ),
-    version="0.1.5",
+    version="0.1.6-alpha-1",
     license="Apache License 2.0",
     description="Automated ML",
     url="https://github.com/Revca-ANAI/ANAI",