Developing a Neural Network Classification Model

AIM

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

Problem Statement

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.

DESIGN STEPS

STEP 1:

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

STEP 2:

Split the data to training and testing

STEP 3:

Train the data and then predict using Tensorflow Write your own steps

PROGRAM

/*

Devoloped by:Atluru Sai Vardhan Reddy

RegisterNumber:212221040022

*/

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

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

df.columns

df.dtypes

df.shape

df.isnull().sum()

df_cleaned = df.dropna(axis=0) df_cleaned.isnull().sum()

df_cleaned.shape

df_cleaned.dtypes

df_cleaned['Gender'].unique()

df_cleaned['Ever_Married'].unique()

df_cleaned['Profession'].unique()

df_cleaned['Spending_Score'].unique()

df_cleaned['Var_1'].unique()

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)

customer_1 = df_cleaned.copy() customer_1[['Gender','Ever_Married','Graduated','Profession','Spending_Score']] = enc.fit_transform(customer_1[['Gender','Ever_Married','Graduated','Profession','Spending_Score']])

customer_1.dtypes le = LabelEncoder() customer_1['Segmentation'] = le.fit_transform(customer_1['Segmentation']) customer_1 = customer_1.drop('ID',axis=1) customer_1 = customer_1.drop('Var_1',axis=1) customer_1.dtypes coor = customer_1.corr() corr = customer_1.corr()

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

sns.pairplot(customer_1)

sns.distplot(customer_1['Age']) plt.figure(figsize=(5,3)) sns.countplot(customer_1['Family_Size'])

customer_1.describe()

customer_1['Segmentation'].unique()

X=customer_1[['Gender','Ever_Married','Age','Graduated','Profession','Work_Experience','Spending_Score','Family_Size']].values

y1 = customer_1[['Segmentation']].values

one_hot_enc = OneHotEncoder()

one_hot_enc.fit(y1)

y1.shape

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

X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.22,random_state=50)

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)

from sklearn.utils.extmath import softmax from tensorflow import keras ai_brain = keras.Sequential([ Dense(6,input_shape = (8,) ), Dense(64,activation="relu"), Dropout(rate=0.3), Dense(128,activation="relu"), Dropout(rate=0.3), #BatchNormalization(), Dense(32,activation="relu"), Dense(4,activation='softmax') ])

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

early_stop = EarlyStopping(monitor='val_loss', patience=250)

ai_brain.fit(x=X_train_scaled,y=y_train,epochs=250,batch_size=256,validation_data=(X_test_scaled,y_test),callbacks=early_stop)

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

metrics.head()

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

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))

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))

Dataset Information

OUTPUT

Training Loss, Validation Loss Vs Iteration Plot

Classification Report

Confusion Matrix

New Sample Data Prediction

RESULT

Thus a Neural Network Classification Model is developed and executed successfully