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Repeated holdout method

How can I make "Repeated" holdout method, I made holdout method and get accuracy but need to repeat holdout method for 30 times
There is my code for holdout method
[IN]
X_train, X_test, Y_train, Y_test = train_test_split(X, Y.values.ravel(), random_state=100)
model = LogisticRegression()
model.fit(X_train, Y_train)
result = model.score(X_test, Y_test)
print("Accuracy: %.2f%%" % (result*100.0))
[OUT]
Accuracy: 49.62%
I see many codes for repeated method but only for K fold cross, nothing for holdout method

So to use a repeated holdout you could use the ShuffleSplit method from sklearn. A minimum working example (following the name conventions that you used) might be as follows:
from sklearn.modelselection import ShuffleSplit
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
# Create some artificial data to train on, can be replace by your own data
X, Y = make_classification()
rs = ShuffleSplit(n_splits=30, test_size=0.25, random_state=100)
model = LogisticRegression()
for train_index, test_index in rs.split(X):
X_train, Y_train = X[train_index], Y[train_index]
X_test, Y_test = X[test_index], Y[test_index]
model.fit(X_train,Y_train)
result = model.score(X_test, Y_test)
print("Accuracy: %.2f%%" % (result*100.0))
n_splits determines how many time you would like to repeat the holdout. test_size deterimines the fraction of samples that is sampled as a test set. In this case 75% is sampled as train set, whereas 25% is sampled to your test set. For reproducible results you can set the random_state (any number suffices, as long as you use the same number consistently).

I am trying to make Simple Linear Regression Model with the salary data csv file , and there are 35 data points . how do i split it in 80-20?

# split train test data
from sklearn.model_selection import train_test_split
X_train, y_train, X_test, y_test = train_test_split(x, y, test_size=0.2, random_state=1)
# import required modules and train the ML algorithm
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
i am getting the error-
Found input variables with inconsistent numbers of samples: [28, 7]

By salary csv data, i will take this one as example
https://www.kaggle.com/karthickveerakumar/salary-data-simple-linear-regression
from sklearn.model_selection import train_test_split
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.linear_model import LinearRegression
#Import the dataset
salary_data=pd.read_csv("/mnt/c/Users/XXXXXXX/Downloads/Salary_Data.csv")
# Here you can split your dataset between train and test using 80% for train
X_train, X_test, y_train, y_test = train_test_split(salary_data["YearsExperience"], salary_data["Salary"], test_size=0.2, random_state=1)
#Then you can fit your linear model on train dataset
#Here the goal is to modelize salary considering years of XP
regressor = LinearRegression()
model = regressor.fit(X_train.values.reshape(-1, 1),y_train.values.reshape(-1, 1))
#Let's plot our model prediction on whole data and compare to real data
plt.title("Salary/Years of XP")
plt.ylabel("Salary $")
plt.xlabel("Years")
plt.plot(salary_data["YearsExperience"],salary_data["Salary"],color="blue",label="real data")
plt.plot(salary_data["YearsExperience"],model.predict(salary_data["YearsExperience"].values.reshape(-1,1)),color="red",label="linear model")
plt.legend()
plt.show()

Machine Learning Using Scikit-Learn & SVM

Load popular digits dataset from sklearn.datasets module and assign it to variable digits.
Split digits.data into two sets names X_train and X_test. Also, split digits.target into two sets Y_train and Y_test.
Hint: Use train_test_split() method from sklearn.model_selection; set random_state to 30; and perform stratified sampling.
Build an SVM classifier from X_train set and Y_train labels, with default parameters. Name the model as svm_clf.
Evaluate the model accuracy on the testing data set and print its score.
I used the following code:
import sklearn.datasets as datasets
import sklearn.model_selection as ms
from sklearn.model_selection import train_test_split
digits = datasets.load_digits();
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=30)
print(X_train.shape)
print(X_test.shape)
from sklearn.svm import SVC
svm_clf = SVC().fit(X_train, y_train)
print(svm_clf.score(X_test,y_test))
I got the below output.
(1347,64)
(450,64)
0.4088888888888889
But I am not able to pass the test. Can someone help with what is wrong?

You are missing the stratified sampling requirement; modify your split to include it:
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=30, stratify=y)
Check the documentation.

How do I properly fit a sci-kit learn model using a pandas dataframe?

I am trying to create a machine learning program in sci-kit learn. I am using a CSV file to store data, and have decided to use Pandas data frame to import and format this data. I cannot figure out how to fit this data frame with the model.
My CSV file has one feature, age, and one target, weight. I am using a linear regression algorithm to predict the weight using the age. I do realize this isn't the best algorithm to use with this data.
When I run this code I get the error "ValueError: Found input variables with inconsistent numbers of samples: [10, 40]"
Here is my code:
# Imports
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
# Load And Split Data
data = pd.read_csv("awd.csv")
feature_cols = ['Ages']
X = data.loc[:, feature_cols]
y = data.loc[:, "Weights"]
X_train, y_train, X_test, y_test = train_test_split(X, y, random_state=0, train_size=0.2)
# Train Model
lr = LinearRegression()
lr.fit(X_train, y_train)
# Scores
print(f"Test set score: {round(lr.score(X_test, y_test), 3)}")
print(f"Training set score: {round(lr.score(X_train, y_train), 3)}")
The first 5 lines of my CSV file:
Ages,Weights
1,19
1,21
2,26
2,32

You're assigning the return values incorrectly. See below:
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, train_size=0.2)

You should correct the order of X_train, X_test, y_train and y_test like this:
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
See the relevant documentation for details.

"Inconsistent numbers of samples" - scikit - learn

I'm learning some basics in machine learning in Python (scikit - learn) and when I tried to implement the K-nearest neighbors algorithm an error occurs: ValueError: Found input variables with inconsistent numbers of samples: [426, 143]. I have no idea how to deal with it.
This is my code:
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
cancer = load_breast_cancer()
X_train, y_train, X_test, y_test = train_test_split(cancer.data,cancer.target,
stratify =
cancer.target,
random_state = 0)
clf = KNeighborsClassifier(n_neighbors = 6)
clf.fit(X_train, y_train)`

train_test_split returns a tuple in the order X_train, X_test, y_train, y_test
You've assigned the return values to the wrong variables so you are fitting with the training data and the test data instead of the training data and the training labels.
It should be
X_train, X_test, y_train, y_test = train_test_split()