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

Training the algorithm to predict the price of house with square feet values.

Loading the dataset

Split the dataset into training and testing

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

Build the Neural Network Model and compile the model.

Train the model with the training data.

Plot the performance plot

Evaluate the model with the testing 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('PricePrediction').sheet1 rows = worksheet.get_all_values() df1 = pd.DataFrame(rows[1:], columns=rows[0])

df=df1.drop(["lakhs","Price"],axis=1) df.head()

df.dtypes

df=df.astype({'Sq.feet':'float'}) df=df.astype({'Price.(in Lakhs)':'float'})

x=df[['Sq.feet']].values y=df[['Price.(in Lakhs)']].values x y

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

x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.33,random_state=50)

scaler=MinMaxScaler() scaler.fit(x_train) x_train_scaled = scaler.transform(x_train) x_train_scaled

ai_brain = Sequential([ Dense(12,activation='relu'), Dense(6,activation='relu'), Dense(3,activation='relu'), Dense(1) ]) ai_brain.compile(optimizer='rmsprop',loss='mse')

ai_brain.fit(x=x_train_scaled,y=y_train,epochs=5000)

loss_df=pd.DataFrame(ai_brain.history.history) loss_df.plot() import matplotlib.pyplot as plt plt.title("Iteration vs Loss")

x_test

x_test_scaled=scaler.transform(x_test) x_test_scaled

ai_brain.evaluate(x_test,y_test)

input=[[2000]] input_scaled=scaler.transform(input) input_scaled.shape

input_scaled

ai_brain.predict(input_scaled)

Thus the price of the house is predicted.