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How to know Polynomial Regression in Python

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0

I am learning about Polynomial Regression Model in Python

I found this dataset in Kaggle, it says, it is a Polynomial Regression Dataset

I want to see the graph that shows the curve in the values ( to know it is a Polynomial Regression)

Something like this

Or like this

But when I draw the relationship I is never anything like this

What I get when I use

sns.pairplot(dt[['t_size','t_avg']])

I get this
which does not have any sign of polynomial regression

I am sure I am doing something wrong but what is it, I dont know.

now my question

Is there any way to get the polynomial curve for this dataset?

is there any other way other than the graph to know if a dataset is polynomial regression?

here is my code

#https://www.kaggle.com/residentmario/pumpkin-price-polynomial-regression

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)


dt= pd.read_csv("C:/Users/asmgx/OneDrive/__PhD/Code/Models/PolynomialRegression/Pumpkins.csv")




SizeMap = 'sml':0, 'med':1, 'med-lrg':2, 'lrg':3, 'xlrg':4, 'jbo':5, 'exjbo':6
dt = dt.assign(
 t_size = dt['Item Size'].map(SizeMap),
 t_avg = (dt['Low Price'] + dt['High Price']) / 2
)



dt = dt[(dt['City Name'] == 'ATLANTA') & (dt['Package'] == '24 inch bins') & (dt['Variety'] == 'HOWDEN TYPE')]


## draw features

sns.pairplot(dt[['t_size','t_avg']])

and this is the result i got

python linear-regression

share|improve this question

asked Mar 9 at 4:02

asmgxasmgx

1,67622343

add a comment | 

0

I am learning about Polynomial Regression Model in Python

I found this dataset in Kaggle, it says, it is a Polynomial Regression Dataset

I want to see the graph that shows the curve in the values ( to know it is a Polynomial Regression)

Something like this

Or like this

But when I draw the relationship I is never anything like this

What I get when I use

sns.pairplot(dt[['t_size','t_avg']])

I get this
which does not have any sign of polynomial regression

I am sure I am doing something wrong but what is it, I dont know.

now my question

Is there any way to get the polynomial curve for this dataset?

is there any other way other than the graph to know if a dataset is polynomial regression?

here is my code

#https://www.kaggle.com/residentmario/pumpkin-price-polynomial-regression

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)


dt= pd.read_csv("C:/Users/asmgx/OneDrive/__PhD/Code/Models/PolynomialRegression/Pumpkins.csv")




SizeMap = 'sml':0, 'med':1, 'med-lrg':2, 'lrg':3, 'xlrg':4, 'jbo':5, 'exjbo':6
dt = dt.assign(
 t_size = dt['Item Size'].map(SizeMap),
 t_avg = (dt['Low Price'] + dt['High Price']) / 2
)



dt = dt[(dt['City Name'] == 'ATLANTA') & (dt['Package'] == '24 inch bins') & (dt['Variety'] == 'HOWDEN TYPE')]


## draw features

sns.pairplot(dt[['t_size','t_avg']])

and this is the result i got

python linear-regression

share|improve this question

asked Mar 9 at 4:02

asmgxasmgx

1,67622343

add a comment | 

I am learning about Polynomial Regression Model in Python

I found this dataset in Kaggle, it says, it is a Polynomial Regression Dataset

I want to see the graph that shows the curve in the values ( to know it is a Polynomial Regression)

Something like this

Or like this

But when I draw the relationship I is never anything like this

What I get when I use

sns.pairplot(dt[['t_size','t_avg']])

I get this
which does not have any sign of polynomial regression

I am sure I am doing something wrong but what is it, I dont know.

now my question

Is there any way to get the polynomial curve for this dataset?

is there any other way other than the graph to know if a dataset is polynomial regression?

here is my code

#https://www.kaggle.com/residentmario/pumpkin-price-polynomial-regression

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)


dt= pd.read_csv("C:/Users/asmgx/OneDrive/__PhD/Code/Models/PolynomialRegression/Pumpkins.csv")




SizeMap = 'sml':0, 'med':1, 'med-lrg':2, 'lrg':3, 'xlrg':4, 'jbo':5, 'exjbo':6
dt = dt.assign(
 t_size = dt['Item Size'].map(SizeMap),
 t_avg = (dt['Low Price'] + dt['High Price']) / 2
)



dt = dt[(dt['City Name'] == 'ATLANTA') & (dt['Package'] == '24 inch bins') & (dt['Variety'] == 'HOWDEN TYPE')]


## draw features

sns.pairplot(dt[['t_size','t_avg']])

and this is the result i got

python linear-regression

share|improve this question

asked Mar 9 at 4:02

asmgxasmgx

1,67622343

sns.pairplot(dt[['t_size','t_avg']])

#https://www.kaggle.com/residentmario/pumpkin-price-polynomial-regression

import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import seaborn as sns

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)


dt= pd.read_csv("C:/Users/asmgx/OneDrive/__PhD/Code/Models/PolynomialRegression/Pumpkins.csv")




SizeMap = 'sml':0, 'med':1, 'med-lrg':2, 'lrg':3, 'xlrg':4, 'jbo':5, 'exjbo':6
dt = dt.assign(
 t_size = dt['Item Size'].map(SizeMap),
 t_avg = (dt['Low Price'] + dt['High Price']) / 2
)



dt = dt[(dt['City Name'] == 'ATLANTA') & (dt['Package'] == '24 inch bins') & (dt['Variety'] == 'HOWDEN TYPE')]


## draw features

sns.pairplot(dt[['t_size','t_avg']])

share|improve this question

asked Mar 9 at 4:02

asmgxasmgx

1,67622343

share|improve this question

asked Mar 9 at 4:02

asmgxasmgx

1,67622343

asked Mar 9 at 4:02

asmgxasmgx

1,67622343

asked Mar 9 at 4:02

asmgxasmgx

1,67622343