import pandas as pd
import pandas as pd
df = pd.read_csv("customers.csv")

import matplotlib.pyplot as plt
import seaborn as sns


import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import load_model
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.callbacks import EarlyStopping

df.head()

from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import OrdinalEncoder
from sklearn.metrics import classification_report as report
from sklearn.metrics import accuracy_score as acc
from sklearn.metrics import confusion_matrix as conf

df.isnull().sum()

df = df.drop('ID',axis=1)
df = df.drop('Var_1',axis=1)

df_cleaned = df.dropna(axis=0)

df_cleaned.isnull().sum()

categories_list=[['Male', 'Female'],['No', 'Yes'],
               ['No', 'Yes'],['Healthcare', 'Engineer',
               'Lawyer','Artist', 'Doctor','Homemaker',
               'Entertainment', 'Marketing', 'Executive'],
               ['Low', 'Average', 'High']]
enc = OrdinalEncoder(categories=categories_list)

df1 = df_cleaned.copy()

df1[['Gender','Ever_Married',
     'Graduated','Profession',
     'Spending_Score']] = enc.fit_transform(df1[['Gender',
     						'Ever_Married','Graduated',
                            'Profession','Spending_Score']])
df1
df1.dtypes

le = LabelEncoder()
df1['Segmentation'] = le.fit_transform(df1['Segmentation'])

df1.dtypes

corr = df1.corr()

sns.heatmap(corr,
            xticklabels=corr.columns,
            yticklabels=corr.columns,
            cmap="Greys",
            annot= True)

sns.distplot(df1['Age'])

scale = MinMaxScaler()
scale.fit(df1[["Age"]]) # Fetching Age column alone
df1[["Age"]] = scale.transform(df1[["Age"]])

df1.describe()

df1['Segmentation'].unique()

x = df1[['Gender','Ever_Married','Age','Graduated',
		 'Profession','Work_Experience','Spending_Score',
         'Family_Size']].values

y1 = df1[['Segmentation']].values

ohe = OneHotEncoder()
ohe.fit(y1)

y = ohe.transform(y1).toarray()

from sklearn.model_selection import train_test_split
x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=1/3,random_state=50)

ai = Sequential([Dense(9,input_shape = [8]),
               Dense(19,activation="relu"),
               Dense(19,activation="relu"),
               Dense(19,activation="relu"),
               Dense(4,activation="softmax")])

ai.compile(optimizer='adam',
         loss='categorical_crossentropy',
         metrics=['accuracy'])

early_stop = EarlyStopping(
  monitor='val_loss',
  mode='max', 
  verbose=1, 
  patience=20)

ai.fit( x = x_train, y = y_train,
      epochs=500, batch_size=256,
      validation_data=(x_test,y_test),
      callbacks = [early_stop]
      )

import numpy as np
metrics = pd.DataFrame(ai.history.history)


metrics[['loss','val_loss']].plot()

metrics[['accuracy','val_accuracy']].plot()

x_pred = np.argmax(ai.predict(x_test), axis=1)
x_pred.shape

y_truevalue = np.argmax(y_test,axis=1)
y_truevalue.shape

x_prediction = np.argmax(ai.predict(x_test[1:17,:]), axis=1)

print(x_prediction)

print(le.inverse_transform(x_prediction))