Libraries used for Machine Learning development

import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import cm
import seaborn as sns
import numpy as np
from statistics import mean 

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder

from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn import neighbors

from my_fns import modify_to_grid_zero_fn, get_knn_n_plot

Libraries used for neural network development

import numpy as np
import pandas as pd
import sklearn
import keras

import matplotlib.pyplot as plt

import seaborn as sns
from statistics import mean 

from matplotlib import cm
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder

from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn import neighbors

import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

from numpy import sqrt
from pandas import read_csv
from keras.layers import Dense, SimpleRNN
from keras import Sequential
from sklearn.model_selection import train_test_split
from keras.engine.input_layer import Input
from keras.optimizers import SGD

from sklearn.datasets import make_moons, make_circles, make_classification

import tensorflow as tf