When performing the linear model with l2 norm loss and gradient decent loss function reaches infinity and then nan

going through the TensorFlow with neural networks, and I tried to build a linear model with house price data set from Kaggle, where I took two inputs and assigned the sales price as the target variable. when I tried training the model with 100 iterations with the learning rate 0.02 initially, it gave me iterative values for the first 10 instances and started giving me infinity values and then Nan. I even tried tweaking the learning rate and the iteration value to 10  but still, I got a non-linear graph for the model which is unlikely.

Start your code here
import pandas as pd 
import numpy as np
import matplotlib.pyplot as plt
train=pd.read_csv('C:/Users/HP/Downloads/train.csv')
inputs=train[['LotArea','LotFrontage']]
inputs=inputs.interpolate(method='linear',limit_direction='forward')
inputn=inputs.to_numpy()
inputn.shape
target=train['SalePrice']
targetn=train['SalePrice'].to_numpy()
targetn=targetn.reshape((1460,1))
targetn.shape
observations=len(targetn)
print(observations)
init_range=0.1
weights=np.random.uniform(-init_range,init_range,size=(2,1))
bias=np.random.uniform(-init_range,init_range,size=1)
print(weights,bias)
learning_rate=0.02
for i in range(10):
    outputs=np.dot(inputn,weights)+bias
    deltas=outputs-targetn
    loss=np.sum(deltas ** 2)/2/observations
    print(loss)
    deltas_scaled=deltas/observations
    weights=weights- learning_rate*np.dot(inputn.T,deltas_scaled)
    bias=bias-learning_rate*np.sum(deltas_scaled)
print(weights,bias)
plt.plot(outputs,target)
plt.xlabel('Output')
plt.ylabel('Target')
plt.show()

Link for the dataset used:
https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data?select=train.csv