How can I train a machine learning model with below characterstics? [closed]

Question

Hi I have a classifier model to solve, which has close to 56k samples and 30 features which are numbers that represents a time series.

I was doing EDA and found the description of my classes as the following. The below represents the following code info

print("*******Details of Class A*********")
df_class_A.mean(axis=1).describe()
df_class_A.median(axis=1).describe()
df_class_A.std(axis=1).describe()

Similarly for B and C

*******Details of Class A Describe() of Mean ,Median,Std,resp*********

count    31840.000000
mean        46.604018
std         63.612574
min          0.500179
25%          2.160353
50%         20.170554
75%         68.354666
max        829.919887
dtype: float64

count    31840.000000
mean        17.071748
std         36.793017
min          0.095262
25%          0.863727
50%          2.855242
75%         18.127796
max        847.765500
dtype: float64

count    31840.000000
mean        66.642554
std         84.567681
min          0.188229
25%          2.881418
50%         33.370476
75%         94.866440
max        780.389461
dtype: float64

*******Details of Class B describe() of mean,median and std resp*********

count    9479.000000
mean       17.196183
std        31.661202
min         0.500274
25%         1.894708
50%         5.813205
75%        22.524316
max       752.849134
dtype: float64

count    9479.000000
mean        6.432351
std        19.211396
min         0.104818
25%         0.516110
50%         1.472833
75%         4.798152
max       725.847000
dtype: float64

count    9479.000000
mean       25.206248
std        38.026784
min         0.252663
25%         2.205550
50%        11.933991
75%        38.126548
max       672.086071
dtype: float64

*******Details of Class C describe() of mean,median,std resp*********

count    9038.000000
mean       15.199317
std        25.566304
min         0.500148
25%         1.272033
50%         3.649730
75%        17.097649
max       268.214901
dtype: float64

count    9038.000000
mean        6.637040
std        17.216870
min         0.097571
25%         0.348909
50%         0.892585
75%         2.727113
max       224.405060
dtype: float64

count    9038.000000
mean       21.398411
std        30.299394
min         0.294747
25%         1.484780
50%         6.988727
75%        30.081947
max       242.581103
dtype: float64

How can I create a machine learning model such that, I can classify a sample such that it follows the chracterstics of above info. What algorithm can I apply and in what way.

I want to classify the sample(30 featured number) which falls into the charactertics of above 3 classes