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How to evaluate the effect of different methods of handling missing values?

I am a total beginner and I am trying to compare different methods of handling missing data. In order to evaluate the effect of each method (drop raws with missing values, drop columns with missigness over 40%, impute with the mean, impute with the KNN), I compare the results of the LDA accuracy and LogReg accuracy on the training set between a dataset with 10% missing values, 20% missing values against the results of the original complete dataset. Unfortunately, I get pretty much the same results even between the complete dataset and the dataset with 20% missing-ness. I don't know what I am doing wrong.
from numpy import nan
from numpy import isnan
from pandas import read_csv
from sklearn.impute import SimpleImputer
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
#dataset = read_csv('telecom_churn_rev10.csv')
dataset = read_csv('telecom_churn_rev20.csv')
dataset = dataset.replace(nan, 0)
values = dataset.values
X = values[:,1:11]
y = values[:,0]
dataset.fillna(dataset.mean(), inplace=True)
#dataset.fillna(dataset.mode(), inplace=True)
print(dataset.isnull().sum())
imputer = SimpleImputer(missing_values = nan, strategy = 'mean')
transformed_values = imputer.fit_transform(X)
print('Missing: %d' % isnan(transformed_values).sum())
model = LinearDiscriminantAnalysis()
cv = KFold(n_splits = 3, shuffle = True, random_state = 1)
result = cross_val_score(model, X, y, cv = cv, scoring = 'accuracy')
print('Accuracy: %.3f' % result.mean())
#print('Accuracy: %.3f' % result.mode())
print(dataset.describe())
print(dataset.head(20))
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix, accuracy_score
cm = confusion_matrix(y_test, y_pred)
print(cm)
accuracy_score(y_test,y_pred)
from sklearn import metrics
# make predictions on X
expected = y
predicted = classifier.predict(X)
# summarize the fit of the model
print(metrics.classification_report(expected, predicted))
print(metrics.confusion_matrix(expected, predicted))
# make predictions on X test
expected = y_test
predicted = classifier.predict(X_test)
# summarize the fit of the model
print(metrics.confusion_matrix(expected, predicted))
print(metrics.classification_report(expected, predicted))

You replace all your missing values with 0 at that line : dataset = dataset.replace(nan, 0). After this line, you have a full dataset without missing values. So, the .fillna() and the SimpleImputer() are useless after that line.

Calculating AUC for LogisticRegression model

Let's take data
import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.decomposition import PCA
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
data = load_breast_cancer()
X = data.data
y = data.target
I want to create model using only first principal component and calculate AUC for it.
My work so far
scaler = StandardScaler()
scaler.fit(X_train)
X_scaled = scaler.transform(X)
pca = PCA(n_components=1)
principalComponents = pca.fit_transform(X)
principalDf = pd.DataFrame(data = principalComponents
, columns = ['principal component 1'])
clf = LogisticRegression()
clf = clf.fit(principalDf, y)
pred = clf.predict_proba(principalDf)
But while I'm trying to use
fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2)
Following error occurs :
y should be a 1d array, got an array of shape (569, 2) instead.
I tried to reshape my data
fpr, tpr, thresholds = metrics.roc_curve(y.reshape(1,-1), pred, pos_label=2)
But it didn't solve the issue (it outputs) :
multilabel-indicator format is not supported
Do you have any idea how can I perform AUC on this first principal component?

You may wish to try:
from sklearn.datasets import load_breast_cancer
from sklearn.decomposition import PCA
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
X,y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X,y)
scaler = StandardScaler()
pca = PCA(2)
clf = LogisticRegression()
ppl = Pipeline([("scaler",scaler),("pca",pca),("clf",clf)])
ppl.fit(X_train, y_train)
preds = ppl.predict(X_test)
fpr, tpr, thresholds = metrics.roc_curve(y_test, preds, pos_label=1)
metrics.plot_roc_curve(ppl, X_test, y_test)

The problem is that predict_proba returns a column for each class. Generally with binary classification, your classes are 0 and 1, so you want the probability of the second class, so it's quite common to slice as follows (replacing the last line in your code block):
pred = clf.predict_proba(principalDf)[:, 1]

100% accuracy in random forest and 94% accuracy in KNN?

I was going to classify into the following dataset.
Dataset
I achieved amazingly high results (too high) and I think I had to do something wrong.
I'm trying to make a clasification for the age group based on the rest of the characteristics. I know there is a large correlation between variables and I will have to fix it all but for now I wanted to use everything as dependent variables.
Here is the code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
dataset = pd.read_csv('abalone.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1:].values
dataset["Sex"]= dataset["Sex"].replace('M', 0)
dataset["Sex"]= dataset["Sex"].replace('F', 1)
dataset["Sex"]= dataset["Sex"].replace('I', 2)
dataset["Age group"]= dataset["Age group"].replace('young abalone', 0)
dataset["Age group"]= dataset["Age group"].replace('middle-aged abalone', 1)
dataset["Age group"]= dataset["Age group"].replace('mature abalone', 2)
dataset["Age group"]= dataset["Age group"].replace('senior abalone', 3)
dataset['Age group'] = dataset['Age group'].astype(int).astype(float)
dataset['Rings'] = dataset['Rings'].astype(int).astype(float)
dataset['Sex'] = dataset['Sex'].astype(int).astype(float)
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1:].values
# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder = LabelEncoder()
X[:, 0] = labelencoder.fit_transform(X[:, 0])
onehotencoder = OneHotEncoder(categorical_features = [0])
X = onehotencoder.fit_transform(X).toarray()
# Avoiding the Dummy Variable Trap
X = X[:, 1:]
# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Fitting Random Forest Classification to the Training set
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm
## KNN
# Fitting K-NN to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier2 = KNeighborsClassifier(n_neighbors = 7, metric = 'minkowski', p = 2)
classifier2.fit(X_train, y_train)
# Predicting the Test set results
y_pred = classifier2.predict(X_test)
# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm
I put all the code because I don't know exactly what went wrong.
I haven't checked yet other accuracy measures because the confusion matrix itself says something probably went wrong

added Standardscaler but receive errors in Cross Validation and the correlation matrix

This is the code I built to apply a multiple linear regression. I added standard scaler to fix the Y intercept p-value which was not significant but the problem that the results of CV RMSE in the end changed and have nosense anymore and received an error in the code for plotting the correlation Matrix saying : AttributeError: 'numpy.ndarray' object has no attribute 'corr'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import statsmodels.api as sm
from scipy import stats
from scipy.stats.stats import pearsonr
# Import Excel File
data = pd.read_excel("C:\\Users\\AchourAh\\Desktop\\Multiple_Linear_Regression\\SP Level Reasons Excels\\SP000273701_PL14_IPC_03_09_2018_Reasons.xlsx",'Sheet1') #Import Excel file
# Replace null values of the whole dataset with 0
data1 = data.fillna(0)
print(data1)
# Extraction of the independent and dependent variables
X = data1.iloc[0:len(data1),[1,2,3,4,5,6,7]] #Extract the column of the COPCOR SP we are going to check its impact
Y = data1.iloc[0:len(data1),9] #Extract the column of the PAUS SP
# Data Splitting to train and test set
from sklearn.model_selection import train_test_split
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size =0.25,random_state=1)
from sklearn.preprocessing import StandardScaler
ss = StandardScaler()
X_train = ss.fit_transform(X_train)
X_test = ss.transform(X_test)
# Statistical Analysis of the training set with Statsmodels
X = sm.add_constant(X_train) # add a constant to the model
est = sm.OLS(Y_train, X).fit()
print(est.summary()) # print the results
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import math
lm = LinearRegression() # create an lm object of LinearRegression Class
lm.fit(X_train,Y_train) # train our LinearRegression model using the training set of data - dependent and independent variables as parameters. Teaching lm that Y_train values are all corresponding to X_train.
print(lm.intercept_)
print(lm.coef_)
mse_test = mean_squared_error(Y_test, lm.predict(X_test))
print(math.sqrt(mse_test))
# Data Splitting to train and test set of the reduced data
X_1 = data1.iloc[0:len(data1),[1,2]] #Extract the column of the COPCOR SP we are going to check its impact
X_train2, X_test2, Y_train2, Y_test2 = train_test_split(X_1, Y, test_size =0.25,random_state=1)
X_train2 = ss.fit_transform(X_train2)
X_test2 = ss.transform(X_test2)
# Statistical Analysis of the reduced model with Statsmodels
X_reduced = sm.add_constant(X_train2) # add a constant to the model
est_reduced = sm.OLS(Y_train2, X_reduced).fit()
print(est_reduced.summary()) # print the results
# Fitting a Linear Model for the reduced model with Scikit-Learn
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
import math
lm1 = LinearRegression() #create an lm object of LinearRegression Class
lm1.fit(X_train2, Y_train2)
print(lm1.intercept_)
print(lm1.coef_)
mse_test1 = mean_squared_error(Y_test2, lm1.predict(X_test2))
print(math.sqrt(mse_test1))
#Cross Validation and Training again the model
from sklearn.model_selection import KFold
from sklearn import model_selection
kf = KFold(n_splits=6, random_state=1)
for train_index, test_index in kf.split(X_train2):
print("Train:", train_index, "Validation:",test_index)
X_train1, X_test1 = X[train_index], X[test_index]
Y_train1, Y_test1 = Y[train_index], Y[test_index]
results = -1 * model_selection.cross_val_score(lm1, X_train1, Y_train1,scoring='neg_mean_squared_error', cv=kf)
print(np.sqrt(results))
#RMSE values interpretation
print(math.sqrt(mse_test1))
print(math.sqrt(results.mean()))
#Good model built no overfitting or underfitting (Barely Same for test and training :Goal of Cross validation but low prediction accuracy = Value is big
import seaborn
Corr=X_train2.corr(method='pearson')
mask=np.zeros_like(Corr)
mask[np.triu_indices_from(mask)]=True
seaborn.heatmap(Corr,cmap='RdYlGn_r',vmax=1.0,vmin=-1.0,mask=mask, linewidths=2.5)
plt.yticks(rotation=0)
plt.xticks(rotation=90)
plt.show()
enter code here
Do you have an idea how to fix the issue ?

I'm guessing the problem lies with:
Corr=X_train2.corr(method='pearson')
.corr is a pandas dataframe method but X_train2 is a numpy array at that stage. If a dataframe/series is passed into StandardScaler, a numpy array is returned. Try replacing the above with:
Corr=pd.DataFrame(X_train2).corr(method='pearson')
or make use of numpy.corrcoef or numpy.correlate in their respective forms.

How to do PCA and SVM for classification in python

I am doing classification, and I have a list with two sizes like this;
Data=[list1,list2]
list1 is 1000*784 size. It means that 1000 images the have been reshaped from 28*28 size into 784.
list2 is 1000*1 size. It shows the label that each images is belonged to.
With the below code, I applied PCA:
from matplotlib.mlab import PCA
results = PCA(Data[0])
the output is like this:
Out[40]: <matplotlib.mlab.PCA instance at 0x7f301d58c638>
now, I want to use SVM as classifier.
I should add the labels. So I have the new data like this for SVm:
newData=[results,Data[1]]
I do not know how use SVM here.

from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn import cross_validation
Data=[list1,list2]
X = Data[0]
y = Data[1]
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=0)
pca = PCA(n_components=2)# adjust yourself
pca.fit(X_train)
X_t_train = pca.transform(X_train)
X_t_test = pca.transform(X_test)
clf = SVC()
clf.fit(X_t_train, y_train)
print 'score', clf.score(X_t_test, y_test)
print 'pred label', clf.predict(X_t_test)
Here is an tested code on another dataset.
import numpy as np
from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.svm import SVC
from sklearn import cross_validation
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=0.4, random_state=0)
pca = PCA(n_components=2)# adjust yourself
pca.fit(X_train)
X_t_train = pca.transform(X_train)
X_t_test = pca.transform(X_test)
clf = SVC()
clf.fit(X_t_train, y_train)
print 'score', clf.score(X_t_test, y_test)
print 'pred label', clf.predict(X_t_test)
Based on these references:
http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html
http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
http://scikit-learn.org/stable/modules/cross_validation.html

I think what you are looking for is http://scikit-learn.org/. It's a python library where you'll find PCA, SVM and other cool algorithms for Machine Learning. It has a good tutorial, but I recommend you follow this guy's http://www.astroml.org/sklearn_tutorial/general_concepts.html . For your particular question, the SVM page of scikit-learn should suffice http://scikit-learn.org/stable/modules/svm.html.