```
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

data=pd.DataFrame(
{"input": 
[0.001,0.015,0.066,0.151,0.266,0.402,0.45,0.499,0.598,0.646,0.738,0.782,0.86,0.894,0.924,0.95],
"output":[0.5263157894736842,0.5789473684210524,0.6315789473684206,0.6842105263157897, 
0.6315789473684206, 0.7894736842105263, 0.8421052631578945, 0.7894736842105263,  0.736842105263158,
0.6842105263157897,  0.736842105263158,  0.736842105263158,0.6842105263157897, 0.6842105263157897, 
0.6315789473684206,0.5789473684210524]})

```

I have the above data that includes input and output data and ı want to make a curve that properly fits this data. Firstly plotting of input and output values are here :
[![enter image description here](https://i.stack.imgur.com/1O8Fp.png)](https://i.stack.imgur.com/1O8Fp.png)

I have made this code:

```
X=data.iloc[:,0].to_numpy()
X=X.reshape(-1,1)
y=data.iloc[:,1].to_numpy()
y=y.reshape(-1,1)

poly=PolynomialFeatures(degree=2)
poly.fit(X,y)
X_poly=poly.transform(X)

reg=LinearRegression().fit(X_poly,y)
plt.scatter(X,y,color="blue")
plt.plot(X,reg.predict(X_poly),color="orange",label="Polynomial Linear Regression")
plt.xlabel("Temperature")
plt.ylabel("Pressure")
plt.legend(loc="upper left")

```

plot is:

[![enter image description here](https://i.stack.imgur.com/4uCxz.png)](https://i.stack.imgur.com/4uCxz.png)

But ı don't find the above curve's equation (orange curve) how can ı find?