[Python] linear regression

TensorFlow Linear regression

import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt
# Linear regression 
print(tf.__version__)
# Data sets - Relationship between years of education and income 
data = pd.read_csv('Income1.csv')
x = data.Education
y = data.Income
print(data)
# Data visualization 
plt.scatter(x, y)
# plt.show()
# Build the model 
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(1, input_shape=(1,)))
print(model.summary())
# Gradient descent algorithm - adam And the loss function - Mean square error mse Least square method 
model.compile(optimizer='adam', loss='mse')
# Training 
history = model.fit(x, y, epochs=5000)
# forecast 
pre = model.predict(x)
print(pre)
pre_20 = model.predict(pd.Series([20]))
print(pre_20)