harshini1331 / basic-nn-model

Developing a Neural Network Regression Model

AIM

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

THEORY

First we can take the dataset based on one input value and some mathematical calculus output value.Next define the neural network model in three layers.First layer has six neurons and second layer has four neurons,third layer has one neuron.The neural network model takes the input and produces the actual output using regression.

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

# Developed By: Harshini M
# Register Number: 212220230022
from google.colab import auth
import gspread
from google.auth import default
import pandas as pd
import matplotlib.pyplot as plt
auth.authenticate_user()
creds, _ = default()
gc = gspread.authorize(creds)
worksheet = gc.open('StudentsData').sheet1
rows = worksheet.get_all_values()
df = pd.DataFrame(rows[1:], columns=rows[0])
df = df.astype({'Input':'float','Output':'float'})
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
df.head()
x=df[['Input']].values
y=df[['Output']].values
x
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.33,random_state=11)
Scaler=MinMaxScaler()
Scaler.fit(x_train)
Scaler.fit(x_test)
x_train1=Scaler.transform(x_train)
x_test1=Scaler.transform(x_test)
x_train1
ai_brain = Sequential([
    Dense(6,activation='relu'),
    Dense(4,activation='relu'),
    Dense(1)
])
ai_brain.compile(
    optimizer='rmsprop',
    loss='mse'
)
ai_brain.fit(x_train1,y_train,epochs=4000)
loss_df=pd.DataFrame(ai_brain.history.history)
loss_df.plot()
plt.title('Training Loss Vs Iteration Plot')
ai_brain.evaluate(x_test1,y_test)
x_n1=[[66]]
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

Succesfully created and trained a neural network regression model for the given dataset.