To develop a neural network classification model for the given dataset.

An automobile company has plans to enter new markets with their existing products. After intensive market research, they’ve decided that the behavior of the new market is similar to their existing market.

In their existing market, the sales team has classified all customers into 4 segments (A, B, C, D ). Then, they performed segmented outreach and communication for a different segment of customers. This strategy has work exceptionally well for them. They plan to use the same strategy for the new markets.

You are required to help the manager to predict the right group of the new customers.

Include the neural network model diagram.

Load the csv file and then use the preprocessing steps to clean the data

Split the data to training and testing

Train the data and then predict using Tensorflow

import pandas as pd from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential from tensorflow.keras.models import load_model import pickle from tensorflow.keras.layers import Dense from tensorflow.keras.layers import Dropout from tensorflow.keras.layers import BatchNormalization import tensorflow as tf import seaborn as sns from tensorflow.keras.callbacks import EarlyStopping 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,confusion_matrix import numpy as np import matplotlib.pylab as plt

customer_df = pd.read_csv('customers.csv')

customer_df.columns

customer_df.dtypes

customer_df.shape

customer_df.isnull().sum()

customer_df_cleaned = customer_df.dropna(axis=0)

customer_df_cleaned.isnull().sum()

customer_df_cleaned.shape

customer_df_cleaned.dtypes

customer_df_cleaned['Gender'].unique()

customer_df_cleaned['Ever_Married'].unique()

customer_df_cleaned['Graduated'].unique()

customer_df_cleaned['Profession'].unique()

customer_df_cleaned['Spending_Score'].unique()

customer_df_cleaned['Var_1'].unique()

customer_df_cleaned['Segmentation'].unique()

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)

customers_1 = customer_df_cleaned.copy()

customers_1[['Gender', 'Ever_Married', 'Graduated','Profession', 'Spending_Score']] = enc.fit_transform(customers_1[['Gender', 'Ever_Married', 'Graduated','Profession', 'Spending_Score']])

customers_1.dtypes

le = LabelEncoder()

customers_1['Segmentation'] = le.fit_transform(customers_1['Segmentation'])

customers_1.dtypes

customers_1 = customers_1.drop('ID',axis=1) customers_1 = customers_1.drop('Var_1',axis=1)

customers_1.dtypes

corr = customers_1.corr()

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

sns.pairplot(customers_1)

sns.distplot(customers_1['Age'])

plt.figure(figsize=(10,6)) sns.countplot(customers_1['Family_Size'])

plt.figure(figsize=(10,6)) sns.boxplot(x='Family_Size',y='Age',data=customers_1)

plt.figure(figsize=(10,6)) sns.scatterplot(x='Family_Size',y='Spending_Score',data=customers_1)

plt.figure(figsize=(10,6)) sns.scatterplot(x='Family_Size',y='Age',data=customers_1)

customers_1.describe()

customers_1['Segmentation'].unique()

X=customers_1[['Gender','Ever_Married','Age','Graduated','Profession','Work_Experience','Spending_Score','Family_Size']].values

y1 = customers_1[['Segmentation']].values

one_hot_enc = OneHotEncoder()

one_hot_enc.fit(y1)

y1.shape

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

y.shape

y1[0]

y[0]

X.shape

X_train,X_test,y_train,y_test=train_test_split(X,y, test_size=0.33, random_state=50)

X_train[0]

X_train.shape

scaler_age = MinMaxScaler()

scaler_age.fit(X_train[:,2].reshape(-1,1))

X_train_scaled = np.copy(X_train) X_test_scaled = np.copy(X_test)

X_train_scaled[:,2] = scaler_age.transform(X_train[:,2].reshape(-1,1)).reshape(-1) X_test_scaled[:,2] = scaler_age.transform(X_test[:,2].reshape(-1,1)).reshape(-1)

ai_brain = Sequential([ Dense(8,input_shape=[8]), Dense(4,activation='relu'), Dense(16,activation='tanh'), Dense(8,activation='relu'), Dense(16,activation='tanh'), Dense(4,activation='softmax') ])

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

early_stop = EarlyStopping(monitor='val_loss', patience=2)

ai_brain.fit(x=X_train_scaled,y=y_train, epochs=2000,batch_size=256, validation_data=(X_test_scaled,y_test), )

metrics = pd.DataFrame(ai_brain.history.history)

metrics.head()

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

x_test_predictions = np.argmax(ai_brain.predict(X_test_scaled), axis=1)

x_test_predictions.shape

y_test_truevalue = np.argmax(y_test,axis=1)

y_test_truevalue.shape

print(confusion_matrix(y_test_truevalue,x_test_predictions))

print(classification_report(y_test_truevalue,x_test_predictions))

ai_brain.save('customer_classification_model.h5')

with open('customer_data.pickle', 'wb') as fh: pickle.dump([X_train_scaled,y_train,X_test_scaled,y_test,customers_1,customer_df_cleaned,scaler_age,enc,one_hot_enc,le], fh)

ai_brain = load_model('customer_classification_model.h5')

with open('customer_data.pickle', 'rb') as fh: [X_train_scaled,y_train,X_test_scaled,y_test,customers_1,customer_df_cleaned,scaler_age,enc,one_hot_enc,le]=pickle.load(fh)

x_single_prediction = np.argmax(ai_brain.predict(X_test_scaled[1:2,:]), axis=1)

print(x_single_prediction)

print(le.inverse_transform(x_single_prediction))

Include screenshot of the dataset

Thus, a neural network classification model is created and executed sucessfully.