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.

Neural Network Model

Include the neural network model diagram.

DESIGN STEPS

STEP 1:

Import the required packages

STEP 2:

Import the dataset to manipulate on

STEP 3:

Clean the dataset and split to training and testing data

STEP 4:

Create the Model and pass appropriate layer values according the input and output data

STEP 5:

Compile and fit the model

STEP 6:

Load the dataset into the model

STEP 7:

Test the model by predicting and output

PROGRAM

Developed by:M.Gunasekhar
Reg No:212221240014

### Importing the require packages
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
import sklearn.metrics as metrics

### Importing the dataset

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

## Data exploration

customer_df.columns
customer_df.dtypes
customer_df.shape
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()
## Encoding of input values
gories_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']])
## Encoding of output values

le = LabelEncoder()
customers_1['Segmentation'] = le.fit_transform(customers_1['Segmentation'])
customers_1 = customers_1.drop('ID',axis=1)
customers_1 = customers_1.drop('Var_1',axis=1)
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)
y = one_hot_enc.transform(y1).toarray()
## Spliting the data
X_train,X_test,y_train,y_test=train_test_split(X,y,
                                               test_size=0.33,
                                               random_state=50)
X_train.shape
## Scaling the features of input

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)
Creation of model

ai_brain = Sequential([
  Dense(units = 8, input_shape=[8]),
  Dense(units =16, activation='relu'),
  Dense(units =4, activation ='softmax')
])
ai_brain.compile(optimizer='adam',
                 loss='categorical_crossentropy',
                 metrics=['accuracy'])
ai_brain.fit(x=X_train_scaled,y=y_train,
             epochs=2000,batch_size=256,
             validation_data=(X_test_scaled,y_test),
             )
## Ploting the metrics

metrics = pd.DataFrame(ai_brain.history.history)
metrics.head()
metrics[['accuracy','val_accuracy']].plot()
metrics[['loss','val_loss']].plot()
## Making the prediction
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)

## Saving and loading the model

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)
## Making the prediction for single input
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

Include your plot here

Classification Report

Confusion Matrix

New Sample Data Prediction

RESULT

Therefore a Neural network classification model is developed and executed successfully.

gunasekhar159 / nn-classification Goto Github PK

nn-classification's Introduction