<jupyter_start><jupyter_text>This is a test/reference notebook, and is in an interactive Jupyter notebook through Binder. Feel free to run the first two cells below, or add your own code! Import packages as well as a toy dataset, and then split the data.<jupyter_code>from sklearn.model_selection import train_test_split
from sklearn.datasets import load_wine
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
import pandas as pd
import numpy as np
#import watermark

X, y = load_wine(return_X_y = True)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .3)<jupyter_output><empty_output><jupyter_text>Call two seperate models and fit the model to the training data. Print the accuracy of the models.<jupyter_code>clf1 = SVC(gamma = 'scale', probability = True)
clf1.fit(X_train, y_train)

svc_score = clf1.score(X_test, y_test)

clf2 = RandomForestClassifier(n_estimators = 100)
clf2.fit(X_train, y_train)

rfc_score = clf2.score(X_test, y_test)

print('svc score was... ', svc_score, ' ,rfc score was... ', rfc_score)

print('Success!')<jupyter_output>svc score was...  0.5370370370370371  ,rfc score was...  0.9629629629629629
Success!<jupyter_text>Print notebook dependencies. Note: This only works on your machine to print dependencies for the requirements.txt file, and will not work within the cloud.<jupyter_code>%load_ext watermark

%watermark -v -m -p sklearn,pandas,numpy

print(" ")
%watermark -u -n -t -z<jupyter_output>The watermark extension is already loaded. To reload it, use:
  %reload_ext watermark
CPython 3.7.4
IPython 7.8.0

sklearn 0.22.2.post1
pandas 0.25.1
numpy 1.16.5

compiler   : MSC v.1915 64 bit (AMD64)
system     : Windows
release    : 10
machine    : AMD64
processor  : Intel64 Family 6 Model 158 Stepping 13, GenuineIntel
CPU cores  : 12
interpreter: 64bit
 
last updated: Sun Jul 12 2020 19:08:10 Mountain Standard Time
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