Developing a Neural Network Regression Model

AIM

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

THEORY

Neurons are the basic input/output units found in neural networks. These units are connected to one another, and each connection carries a weight. Because they are adaptable, neural networks can be applied to both classification and regression. We'll examine how neural networks can be used to tackle regression issues in this post.

A relationship between a dependent variable and one or more independent variables can be established with the aid of regression. Only when the regression equation is a good fit for the data can regression models perform well. Although sophisticated and computationally expensive, neural networks are adaptable and can choose the optimum form of regression dynamically. If that isn't sufficient, hidden layers can be added to enhance prediction. Create your training and test sets using the dataset; in this case, we are creating a neural network with a second hidden layer that uses the activation layer as relu and contains its nodes. We will now fit our dataset before making a value prediction.

Neural Network Model

DESIGN STEPS

STEP 1:

Loading the dataset

STEP 2:

Split the dataset into training and testing

STEP 3:

Create MinMaxScalar objects ,fit the model and transform the data.

STEP 4:

Build the Neural Network Model and compile the model.

STEP 5:

Train the model with the training data.

STEP 6:

Plot the performance plot

STEP 7:

Evaluate the model with the testing data.

PROGRAM

Importing Modules

from google.colab import auth
import gspread
from google.auth import default
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

Authenticate & Create DataFrame using Data in Sheets

auth.authenticate_user()
creds, _ = default()
gc = gspread.authorize(creds)

worksheet = gc.open('dp').sheet1
rows = worksheet.get_all_values()
df = pd.DataFrame(rows[1:], columns=rows[0])
df = df.astype({'Input':'float'})
df = df.astype({'Output':'float'})

Assign X and Y values

X=df[['Input']].values
y=df[['Output']].values

Normalize the value & split the data

Scaler=MinMaxScaler()
Scaler.fit(X)
X=Scaler.fit_transforms(x)
X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.33,random_state=33)

Create a Neural Network & Train it

ai_brain=Sequential([
    Dense(9,activation='relu'),
    Dense(11,activation='relu'),
    
    Dense(1)
])
ai_brain.compile(
    optimizer = 'rmsprop',
    loss = 'mse'
)

ai_brain.fit(X_train1,y_train,epochs = 3000)

Plot the Loss

loss_df = pd.DataFrame(ai_brain.history.history)
loss_df.plot()

Evaluate the model

ai_brain.evaluate(X_test,y_test)

Predict the Value

X_n1 = [[101]]

X_n1_1 = Scaler.transform(X_n1)

ai_brain.predict(X_n1_1)

Dataset Information

OUTPUT

Training Loss Vs Iteration Plot

Test Data Root Mean Squared Error

New Sample Data Prediction

RESULT

Thus a neural network regression model for the given dataset is written and executed successfully

manojvenaram/basic-nn-model