Developing a Neural Network Regression Model

AIM

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

THEORY

Explain the problem statement

Neural Network Model

Include the neural network model diagram.

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

  Name:NIVETHA A
  Register Number:212222230101
 
DEPENDENCIES

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import MinMaxScaler

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

DATA from google.colab import auth

import gspread

from google.auth import default

import pandas as pd

auth.authenticate_user()

creds, _ = default()

gc = gspread.authorize(creds)

worksheet = gc.open('DL').sheet1

rows = worksheet.get_all_values()

DATA PROCESSING

df = pd.DataFrame(rows[1:], columns=rows[0])

df.head()

df['Input']=pd.to_numeric(df['Input'])

df['Output']=pd.to_numeric(df['Output'])

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

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

x_train,x_test,y_train,y_test = train_test_split(X,y,test_size = 0.33,random_state = 33)

Scaler = MinMaxScaler()

Scaler.fit(x_train)

x_train.shape

x_train1 = Scaler.transform(x_train)

x_train1.shape

MODEL ARCHITECTURE AND TRAINING

model = Sequential([

Dense(units = 5,activation = 'relu',input_shape=[1]),
Dense(units = 2, activation = 'relu'),
Dense(units = 1)

])

model.compile(optimizer='rmsprop', loss = 'mae')

model.fit(x_train1,y_train,epochs = 2000)

model.summary()

LOSS CALCULATION

loss_df = pd.DataFrame(model.history.history)

loss_df.plot()

PREDICTION

x_test1 = Scaler.transform(x_test)

model.evaluate(x_test1,y_test)

x_n = [[21]]

x_n1 = Scaler.transform(x_n)

model.predict(x_n1)

OUTPUT

DATASET INFORMATION

Training Loss Vs Iteration Plot

Test Data Root Mean Squared Error

New Sample Data Prediction

RESULT

Summarize the overall performance of the model based on the evaluation metrics obtained from testing data as a regressive neural network based prediction has been obtained.

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