Developing a Neural Network Regression Model

AIM:

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

THEORY:

Neural network regression models learn complex relationships between input variables and continuous outputs through interconnected layers of neurons. By iteratively adjusting parameters via forward and backpropagation, they minimize prediction errors. Their effectiveness hinges on architecture design, regularization, and hyperparameter tuning to prevent overfitting and optimize performance.

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:

#Name: Laakshit D
#Register Number: 212222230071

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('dldata').sheet1
rows = worksheet.get_all_values()

df = pd.DataFrame(rows[1:], columns=rows[0])
df = df.astype({'INPUT':'float'})
df = df.astype({'OUTPUT':'float'})
df.head()

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler

import numpy as np
x=[]
y=[]
for i in range(60):
  num = i+1
  x.append(num)
  y.append(num*12) 
df=pd.DataFrame({'INPUT': x, 'OUTPUT': y})
df.head()

inp=df[["INPUT"]].values
out=df[["OUTPUT"]].values
Input_train,Input_test,Output_train,Output_test=train_test_split(inp,out,test_size=0.33)
Scaler=MinMaxScaler()
Scaler.fit(Input_train)
Scaler.fit(Input_test)
Input_train=Scaler.transform(Input_train)
Input_test=Scaler.transform(Input_test)

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model=Sequential([Dense(5,activation='relu'),
                  Dense(10,activation='relu'),
                  Dense(1)])
model.compile(loss="mse",optimizer="rmsprop")
history=model.fit(Input_train,Output_train, epochs=3000,batch_size=32)

prediction_test=int(input("Enter the value to predict:"))
preds=model.predict(Scaler.transform([[prediction_test]]))
print("The prediction for the given input "+str(prediction_test)+" is:"+str(int(np.round(preds))))

model.evaluate(Input_test,Output_test)

import matplotlib.pyplot as plt
plt.suptitle("   Laakshit")
plt.title("Error VS Iteration")
plt.ylabel('MSE')
plt.xlabel('Iteration')
plt.plot(pd.DataFrame(history.history))
plt.legend(['train'] )
plt.show()

worksheet = gc.open('dldata').sheet1
data = worksheet.get_all_values()

dataset1 = pd.DataFrame(data[1:], columns=data[0])
dataset1 = dataset1.astype({'INPUT':'float'})
dataset1 = dataset1.astype({'OUTPUT':'float'})

dataset1.head()

X = dataset1[['INPUT']].values
y = dataset1[['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_train1 = Scaler.transform(X_train)

ai_brain = Sequential([
    Dense(3,activation='relu'),
    Dense(4,activation='relu'),
    Dense(1)
])
ai_brain.compile(optimizer='rmsprop',loss='mse')
ai_brain.fit(x=X_train1,y=y_train,epochs=50)

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

laakshit-d / basic-nn-model Goto Github PK

basic-nn-model's Introduction

Developing a Neural Network Regression Model

AIM:

THEORY:

Neural Network Model

DESIGN STEPS

STEP 1:

STEP 2:

STEP 3:

STEP 4:

STEP 5:

STEP 6:

STEP 7:

PROGRAM:

Dataset Information

OUTPUT

Training Loss Vs Iteration Plot

Test Data Root Mean Squared Error

New Sample Data Prediction

RESULT

basic-nn-model's People

Contributors

Recommend Projects

Recommend Topics

Recommend Org