Best Answer · By JanBask Data Science Expert
Ranjana Admin JanBask Expert
Answered on Jan 15, 2020
Let us load housing data to perform linear regression using keras.
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
Now we will load the data
df = pd.read_csv('../data/housing-data.csv')
Now we create feature and target variables
X = df[['sqft', 'bdrms', 'age']].values
y = df['price'].values
Now we will import keras and other libraries
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
Now we create a regression model
model = Sequential()
model.add(Dense(1, input_shape=(3,)))
model.compile(Adam(lr=0.8), 'mean_squared_error')
Now we split the data
from sklearn.model_selection import train_test_split
# split the data into train and test with a 20% test size
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
Now we fit and predict the model
model.fit(X_train, y_train)
from sklearn.metrics import r2_score
# check the R2score on training and test set
y_train_pred = model.predict(X_train)
y_test_pred = model.predict(X_test)
print("The R2 score on the Train set is: {:0.3f}".format(r2_score(y_train, y_train_pred)))
print("The R2 score on the Test set is: {:0.3f}".format(r2_score(y_test, y_test_pred)))
