trainig_example.py

import numpy as np
import tensorflow as tf
import pandas as pd

FEATURES = ["SqFt", "Bedrooms", "Offers"]
LABEL = "Price"

feature_cols = [tf.feature_column.numeric_column(k) for k in FEATURES]
estimator = tf.estimator.LinearRegressor(    
        feature_columns=feature_cols,   
        model_dir="train")

def get_input_fn(data_set):    
         return tf.estimator.inputs.pandas_input_fn(       
             x=pd.DataFrame({k: data_set[k].values for k in FEATURES}),       
             y=pd.Series(data_set[LABEL].values),       
             batch_size=10,          
             num_epochs=100,       
             shuffle=True)
    
estimator.train(input_fn=get_input_fn(house_pricing))