将数据集中的特征添加到函数中会导致“TypeError: can't convert type 'ndarray' to numerator/denominator”

Question

The task requires you to load a feature of the diabetes dataset and write your own line of best fit for the training data.

I have written the required line of best fit algorithm, however when trying to add the training data to it, I receive this error:

"TypeError: can't convert type 'ndarray' to numerator/denominator"

import matplotlib.pyplot as plt
import numpy as np
from sklearn import datasets, linear_model
from statistics import mean

diabetes = datasets.load_diabetes()
diabetes_X = diabetes.data[:, np.newaxis, 2]

diabetes_X_train = diabetes_X[:-20] #creating the testing and training data 
diabetes_X_test = diabetes_X[-20:]

diabetes_y_train = diabetes.target[:-20]
diabetes_y_test = diabetes.target[-20:]

## The below code is where the issue is occurring 

xs = np.array(diabetes_X_train, dtype=np.float64)
ys = np.array([diabetes_y_train, dtype=np.float64)

##the algorithm to calculate the line of best  

def best_fit_slope_and_intercept(xs,ys):
    m = (((mean(xs)*mean(ys)) - mean(xs*ys)) /
         ((mean(xs)*mean(xs)) - mean(xs*xs)))

    b = mean(ys) - m*mean(xs)

    return m, b

m, b = best_fit_slope_and_intercept(xs,ys)

print(m,b)

I understand converting the required data into the correct format is the issue but after doing research, I am unable to find the correct way to do so.

All input on how to correctly concatenate or convert the training data as required is appreciated.

Answer 1

the best format for dealing with data is DataFrame, in my view. you could easily make your dataframe like this:

urdataframe={'headername': bestfitted}
urdataframe=pd.DataFrames(data=urdataframe)

then you can contact ur dataframes easily like :

finaldata=pd.concat((traindata,urdataframe),axis=1)

you could do all machine learning functions on DataFrames and less likely to get errors

Answer 2

for example, if ur train data is something like: age sex sugar 42 1 120 45 0 250 32 1 98

and your answer is like: answer yes no yes

so after contact by the code, I mentioned it would be like: age sex sugar answer 42 1 120 yes 45 0 250 no 32 1 98 yes