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n_splits=10 cannot be greater than the number of members in each class

I'm trying to perform a model selection between KNN and Logistic Regression using sampling technique as 10-fold cross validation, but keep getting the above error after the last part. Could I please be advised on what I'm doing wrong? Thanks.
Here is my code:
import pandas as pd
import numpy as np
import sklearn.model_selection
from sklearn.model_selection import cross_val_score
#Load data
mtcars_df = pd.read_csv('mtcars.csv')
mtcars_df.head()
X = mtcars_df.iloc[:,1:].values
Y = mtcars_df['model'].values
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
print(cross_val_score(knn,X,Y, cv=10, scoring='accuracy').mean())

How to improve accuracy_score in machine learning python for this regression problem?

I am a beginner to machine learning and as part of learning I choose student performance dataset from UCI. I want to predict the final result of a student based on the features given.
I first tried using two main and highly correlated features G1 and G2 that are grades of two exams. I used LinearRegression algorithm and got an accuracy of 0.4 or less.
Then I tried feature engineering on all the features that are objects in dataframe and still the accuracy is same.
How can I improve accuracy score ?
My code as a Python notebook
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import ElasticNet
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error,accuracy_score
df = pd.read_csv('student-mat.csv',sep=';')
df2 = pd.read_csv('student-por.csv',sep=';')
df = [df,df2]
df = pd.concat(df)
df = pd.get_dummies(df)
X = df.drop('G3',axis=1)
y = df['G3']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.1, random_state=42)
model = LinearRegression()
model.fit(X_train,y_train)
y_pred = model.predict(X_test)
y_pred = [int(round(i)) for i in y_pred]
accuracy_score(y_test,y_pred)

The accuracy calculated on continous variables is not very useful. You can use the mean squared error instead, which is relevant for continuous output.
As for improving your model, you can try to use the different tools at your disposal to identify the most relevant features. I recommend statsmodels API (https://www.statsmodels.org/stable/regression.html) to get a more in-depth analysis.

plot training and validation loss curves?

I am working with the yeast dataset available at:
http://archive.ics.uci.edu/ml/datasets/yeast
and I want to make a neural network classifier model and plot the learning curves. So, I have used the model_selection of scikit twice; one for making the training and testing set and once more for selecting the validation set. From these two sets I would like to plot the learning curves, my code is the following:
import numpy as np
import pandas as pd
from sklearn import model_selection, linear_model
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.neural_network import MLPClassifier
import matplotlib.pyplot as plt
def readFile(file):
head=["seq_n","mcg","gvh","alm","mit","erl","pox","vac","nuc","site"]
f=pd.read_csv(file,delimiter=r"\s+")
f.columns=head
return f
def NeuralClass(X,y):
X_train,X_test,y_train,y_test=model_selection.train_test_split(X,y,test_size=0.2)
X_tr,X_val,y_tr,y_val=model_selection.train_test_split(X_train,y_train,test_size=0.2)
mlp=MLPClassifier(activation="relu",max_iter=3000)
mlp.fit(X_train,y_train)
print (mlp.score(X_train,y_train))
plt.plot(mlp.loss_curve_)
mlp.fit(X_val,y_val)
plt.plot(mlp.loss_curve_)
def main():
f=readFile("yeast.data")
list=["seq_n","site"]
X=f.drop(list,1)
y=f["site"]
NeuralClass(X,y)
if __name__=="__main__":
main()
I have obtained a graph like the following which I do not know if it´s correct:
The question is if this would be the correct way to plot the validation curve or if the method I followed is the right one.
Thanks

Didn't test it, but should be something like this:
def NeuralClass(X,y):
X_train,X_test,y_train,y_test = model_selection.train_test_split(
X,y,test_size=0.2)
mlp=MLPClassifier(
activation="relu",
max_iter=3000,
validation_fraction=0.2,
early_stopping=True)
mlp.fit(X_train,y_train)
print (mlp.score(X_train,y_train))
plt.plot(mlp.loss_curve_)
plt.plot(mlp.validation_scores_)

How to predict new output from a linearRegression model build using sklean?

I have been following tutorial on linear regression with scikitlearn.The code works perfectly and now i wanna predict new output by giving a new input.I have used student score and Study hour data set.
Here's the code:
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
data=pd.read_csv("/home/crpsm/Pycharm/DataSet/student_scores.csv")
data.plot(x="Hours",y="Scores",style="o")
plt.title("Score-Hour")
plt.xlabel('Hours ')
plt.ylabel('Percentage ')
x=data.iloc[:,:-1]
y=data.iloc[:,1]
x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.55,random_state=5)
regression_model=LinearRegression()
regression_model.fit(x_train,y_train)
print(regression_model.coef_)
print(regression_model.intercept_)
regression_model.predict(X_test)

prediction = regression_model.predict(X)
Please read the docs:
http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html

Unable to run logit model/ logistic regression

I'm trying to run a logistic regression. The data has been scrubbed and categorical variables change to dummies however when i run the code i get an error message from the "statsmodels" package outside of my code and not sure how to correct in this case.
A friend of mine ran the same code and he got an output (print screen below), as i'm using spyder with python 3.6 he thinks it might be a version issue - he is using python 3.5
I've got the code below. Any ideas on how to fix it or how better to run a logistic regression is appreciated.
error message i'm getting is in statsmodels library:
File "C:\Users\sebas\Anaconda3\lib\site-packages\statsmodels\discrete\discrete_model.py", line 2405, in llr_pvalue
return stats.chisqprob(self.llr, self.df_model)
AttributeError: module 'scipy.stats' has no attribute 'chisqprob'
thanks!
import pandas as pd
import numpy as np
from sklearn import preprocessing
import matplotlib.pyplot as plt
plt.rc("font", size=14)
from sklearn.linear_model import LogisticRegression
from sklearn.feature_selection import RFE
from sklearn.cross_validation import train_test_split
import seaborn as sns
sns.set(style="white")
sns.set(style="whitegrid", color_codes=True)
################################################################################
## Logistic regression
###############################################################################
data = pd.read_csv(r"log reg test Lending club 2007-2011 car only.csv")
#data = data.dropna()
print(data.shape)
##print(list(data.columns))
print(data['Distressed'].value_counts()) ## number of defaulted car loans is binary
sns.countplot(x='Distressed', data=data, palette='hls')
plt.show ## confrim dependent variable is binary
##basic numerical analysis of variables to check feasibility for model
## we will need to create dummy variables for strings
#print(data.groupby('Distressed').mean()) ##numerical variable means
#print(data.groupby('grade').mean()) ## string variable means
#print(data.groupby('sub_grade').mean())
#print(data.groupby('emp_length').mean())
#print(data.groupby('home_ownership').mean())
##testing for nulls in dataset
print(data.isnull().sum())
scrub_data=data.drop(['mths_since_last_delinq'],1) ## this variable is not statistically significant
print('Here is the logit model data')
print(scrub_data.isnull().sum()) ## removed records of missing info, sample still sufficiently large
print(list(scrub_data.columns))
print(scrub_data.head())
##convert categorical variables to dummies completed in csv file
X=scrub_data.ix[:,(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22)].values
y=scrub_data.ix[:,0].values
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=.3, random_state=25)
LogReg=LogisticRegression()
LogReg.fit(X_train,y_train)
y_pred=LogReg.predict(X_test)
from sklearn.metrics import classification_report
print(classification_report(y_test,y_pred))
print('alternative method using RFE')
#y=['Distressed']
#x=[i for i in data if i not in y]
#print(y)
#print(x)
#print(data.info())
## check for independance between features
correlation=sns.heatmap(data.corr()) ## heatmap showing correlations of the variables
print(correlation)
from sklearn.svm import LinearSVC
#logreg = LogisticRegression()
#rfe = RFE(logreg,10)
#rfe=rfe.fit(x,y)
#print(rfe.support_)
#print(rfe.ranking_)
import statsmodels.api as sm
logit_model=sm.Logit(y,X)
result=logit_model.fit()
print(result.summary())

The error can be fixed by assigning the missing function back into the scipy.stats namespace as shown below:
from scipy import stats
stats.chisqprob = lambda chisq, df: stats.chi2.sf(chisq, df)