import os
import glob
import pandas as pd
import numpy as np
from sklearn.svm import SVC
 
path = "/home/me/Desktop/pca-comma-seperated-csv" #path to my pca'd files 
all_files = glob.glob(os.path.join(path, "*.csv"))
df_from_each_file = (pd.read_csv(f) for f in all_files) #creating dataframe from 90 csv files,each csv file containing 2 columns and upto 23000 columns
X   = pd.concat(df_from_each_file, ignore_index=True)
classfile = "/home/me/Desktop/1.csv" #path to my label file
Y = pd.read_csv(classfile, header=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
clf = SVC()
clf.fit(X_train, y_train)
y_pred = clf.predict(x_test)
confusion_matrix(y_test, y_pred)
# your code goes here

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