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This article introduces you how to achieve nonlinear regression in python, the content is very detailed, interested friends can refer to, hope to be helpful to you.

Nonlinear regression analysis by python

Use matplotlib to visualize the scatter plot.

Csv files with date-dependent number of infections

The code is as follows:

From pandas import read_csv

Import matplotlib.pyplot as plt

Plt.rcParams ['font.family'] =' SimHei'# solves Chinese fonts

Data=read_csv ('FRV 2.csventing camera encodingless GBK')

Plt.scatter (data. Date, data. Number of infections)

Data.corr ()

LrModel=LinearRegression ()

X=data [['date']]

Y=data [['number of infections']]

Plt.scatter (XBI y)

Plt.xlabel ('date ordinal')

Plt.ylabel ('number of infections')

Plt.show ()

The result is as shown in the picture (I will take March 11 as my first day)

According to the figure is very close to the quadratic function and the cubic function, I choose to use the cubic function to fit. The code is as follows:

From sklearn.preprocessing import

PolynomialFeatures as pf

Pd=pf (degree=3)

X1=pd.fit_transform (x)

Irmodle=LinearRegression ()

Irmodle.fit (x1BI y)

A=irmodle.score (x1BI y)

B=pd.fit_transform ([[18]])

C=irmodle.predict (b)

Print (a)

Print ("estimated number of infections in 3 / 29 days", irmodle.predict (pd.fit_transform ([17])

Print ("estimated number of infections in 3 / 30 days", c)

Print ("estimated number of infections in March / 31 is", irmodle.predict (pd.fit_transform ([19])

Print ("estimated number of infections per 4 / 1 day", irmodle.predict (pd.fit_transform ([[20]])

The results are as follows:

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