diff --git a/gamestonk_terminal/prediction_techniques/knn.py b/gamestonk_terminal/prediction_techniques/knn.py
index f0cd2e2fbff..e1ae266a72a 100644
--- a/gamestonk_terminal/prediction_techniques/knn.py
+++ b/gamestonk_terminal/prediction_techniques/knn.py
@@ -1,21 +1,23 @@
 import argparse
-import numpy as np
-from gamestonk_terminal.helper_funcs import *
-import pandas as pd
 import matplotlib.pyplot as plt
+import pandas as pd
 from pandas.plotting import register_matplotlib_converters
-
-register_matplotlib_converters()
 from sklearn import neighbors
 from TimeSeriesCrossValidation import splitTrain
+from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
+
+register_matplotlib_converters()
 
 # ----------------------------------------------------- kNN -----------------------------------------------------
+# pylint: disable=unused-argument
 def k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
         prog="knn",
-        description=""" K nearest neighbors is a simple algorithm that stores all 
-                                     available cases and predict the numerical target based on a similarity measure 
-                                     (e.g. distance functions). """,
+        description="""
+            K nearest neighbors is a simple algorithm that stores all
+            available cases and predict the numerical target based on a similarity measure
+            (e.g. distance functions).
+        """,
     )
 
     parser.add_argument(
@@ -108,7 +110,7 @@ def k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock):
         plt.axvspan(
             df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
         )
-        xmin, xmax, ymin, ymax = plt.axis()
+        _, _, ymin, ymax = plt.axis()
         plt.vlines(
             df_stock.index[-1], ymin, ymax, linewidth=1, linestyle="--", color="k"
         )
@@ -120,5 +122,6 @@ def k_nearest_neighbors(l_args, s_ticker, s_interval, df_stock):
         print(df_pred.to_string())
         print("")
 
-    except:
+    except Exception as e:
+        print(e)
         print("")
diff --git a/gamestonk_terminal/prediction_techniques/neural_networks.py b/gamestonk_terminal/prediction_techniques/neural_networks.py
index 0f965151586..49b79bbb668 100644
--- a/gamestonk_terminal/prediction_techniques/neural_networks.py
+++ b/gamestonk_terminal/prediction_techniques/neural_networks.py
@@ -1,14 +1,11 @@
 import argparse
-from gamestonk_terminal.helper_funcs import *
-import pandas as pd
+import os
+from warnings import simplefilter
 import numpy as np
 import matplotlib.pyplot as plt
+import pandas as pd
 from pandas.plotting import register_matplotlib_converters
-
-register_matplotlib_converters()
-
 from TimeSeriesCrossValidation import splitTrain
-
 from sklearn.preprocessing import MinMaxScaler, StandardScaler
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import (
@@ -16,19 +13,19 @@ from tensorflow.keras.layers import (
     SimpleRNN,
     Dense,
     Dropout,
-    Activation,
-    RepeatVector,
-    TimeDistributed,
+    # Activation,
+    # RepeatVector,
+    # TimeDistributed,
 )
 
+from gamestonk_terminal.helper_funcs import check_positive, get_next_stock_market_days
 from gamestonk_terminal import config_neural_network_models as cfg_nn_models
 
-import os
+
+register_matplotlib_converters()
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
 
-from warnings import simplefilter
-
 simplefilter(action="ignore", category=FutureWarning)
 
 
@@ -38,7 +35,7 @@ def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
 
     for idx_layer, d_layer in enumerate(Recurrent_Neural_Network):
         # Recurrent Neural Network
-        if str(*d_layer) is "SimpleRNN":
+        if str(*d_layer) == "SimpleRNN":
             # Is this the input layer? If so, define input_shape
             if idx_layer == 0:
                 model.add(SimpleRNN(**d_layer["SimpleRNN"], input_shape=(n_inputs, 1)))
@@ -49,7 +46,7 @@ def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
                 model.add(SimpleRNN(**d_layer["SimpleRNN"]))
 
         # Long-Short Term-Memory
-        elif str(*d_layer) is "LSTM":
+        elif str(*d_layer) == "LSTM":
             # Is this the input layer? If so, define input_shape
             if idx_layer == 0:
                 model.add(LSTM(**d_layer["LSTM"], input_shape=(n_inputs, 1)))
@@ -60,7 +57,7 @@ def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
                 model.add(LSTM(**d_layer["LSTM"]))
 
         # Dense (Simple Neuron)
-        elif str(*d_layer) is "Dense":
+        elif str(*d_layer) == "Dense":
             # Is this the input layer? If so, define input_shape
             if idx_layer == 0:
                 model.add(Dense(**d_layer["Dense"], input_dim=n_inputs))
@@ -71,7 +68,7 @@ def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
                 model.add(Dense(**d_layer["Dense"]))
 
         # Dropout (Regularization)
-        elif str(*d_layer) is "Dropout":
+        elif str(*d_layer) == "Dropout":
             model.add(Dropout(**d_layer["Dropout"]))
 
         else:
@@ -81,6 +78,7 @@ def build_neural_network_model(Recurrent_Neural_Network, n_inputs, n_days):
 
 
 # -------------------------------------------------- MLP --------------------------------------------------
+# pylint: disable=unused-argument
 def mlp(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
         prog="mlp", description="""Multilayer Perceptron. """
@@ -250,7 +248,7 @@ def mlp(l_args, s_ticker, s_interval, df_stock):
         plt.axvspan(
             df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
         )
-        xmin, xmax, ymin, ymax = plt.axis()
+        _, _, ymin, ymax = plt.axis()
         plt.vlines(
             df_stock.index[-1],
             ymin,
@@ -268,11 +266,13 @@ def mlp(l_args, s_ticker, s_interval, df_stock):
         print(df_pred.to_string())
         print("")
 
-    except:
+    except Exception as e:
+        print(e)
         print("")
 
 
 # -------------------------------------------------- RNN --------------------------------------------------
+# pylint: disable=unused-argument
 def rnn(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
         prog="rnn", description="""Recurrent Neural Network. """
@@ -442,7 +442,7 @@ def rnn(l_args, s_ticker, s_interval, df_stock):
         plt.axvspan(
             df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
         )
-        xmin, xmax, ymin, ymax = plt.axis()
+        _, _, ymin, ymax = plt.axis()
         plt.vlines(
             df_stock.index[-1],
             ymin,
@@ -460,11 +460,13 @@ def rnn(l_args, s_ticker, s_interval, df_stock):
         print(df_pred.to_string())
         print("")
 
-    except:
+    except Exception as e:
+        print(e)
         print("")
 
 
 # -------------------------------------------------- LSTM --------------------------------------------------
+# pylint: disable=unused-argument
 def lstm(l_args, s_ticker, s_interval, df_stock):
     parser = argparse.ArgumentParser(
         prog="lstm", description="""Long-Short Term Memory. """
@@ -634,7 +636,7 @@ def lstm(l_args, s_ticker, s_interval, df_stock):
         plt.axvspan(
             df_stock.index[-1], df_pred.index[-1], facecolor="tab:orange", alpha=0.2
         )
-        xmin, xmax, ymin, ymax = plt.axis()
+        _, _, ymin, ymax = plt.axis()
         plt.vlines(
             df_stock.index[-1],
             ymin,
@@ -652,5 +654,6 @@ def lstm(l_args, s_ticker, s_interval, df_stock):
         print(df_pred.to_string())
         print("")
 
-    except:
+    except Exception as e:
+        print(e)
         print("")