In [0]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
In [0]:
url = 'https://raw.githubusercontent.com/jimsjoo/gilbut_portfolio/master/stocks.csv'
dataset = pd.read_csv(url)
In [3]:
dataset.head()
Out[3]:
| Date | Apple | MSFT | Increase | ||
|---|---|---|---|---|---|
| 0 | 2010-01-04 | 214.009998 | 626.750011 | 30.950001 | True |
| 1 | 2010-01-05 | 214.379993 | 623.990017 | 30.959999 | True |
| 2 | 2010-01-06 | 210.969995 | 608.259974 | 30.770000 | False |
| 3 | 2010-01-07 | 210.580000 | 594.100015 | 30.450001 | False |
| 4 | 2010-01-08 | 211.980005 | 602.020005 | 30.660000 | True |
In [0]:
crit_train=dataset['Date']<'2014'
crit_test =dataset['Date']>='2014'
print(dataset[crit_train].tail())
print(dataset[crit_test].head())
Date Apple Google MSFT Increase
1001 2013-12-24 567.669975 1111.840064 37.080002 False
1002 2013-12-26 563.900009 1117.460049 37.439999 False
1003 2013-12-27 560.089989 1118.400026 37.290001 False
1004 2013-12-30 554.519981 1109.460000 37.290001 False
1005 2013-12-31 561.019997 1120.709970 37.410000 True
Date Apple Google MSFT Increase
1006 2014-01-02 553.129990 1113.120033 37.160000 False
1007 2014-01-03 540.980019 1104.999987 36.910000 False
1008 2014-01-06 543.929993 1117.320052 36.130001 True
1009 2014-01-07 540.040024 1138.860015 36.410000 False
1010 2014-01-08 543.460022 1141.230079 35.759998 True
In [0]:
predictors = dataset[['Apple','Google','MSFT']]
data_train = predictors[crit_train]
data_test = predictors[crit_test]
data_inc = dataset[['Increase']][crit_train]
In [15]:
X=dataset.iloc[:,1:-1]
y=dataset.iloc[:,-1]
print(dataset.head())
print(X[:5])
print(y[:5])
Date Apple Google MSFT Increase
0 2010-01-04 214.009998 626.750011 30.950001 True
1 2010-01-05 214.379993 623.990017 30.959999 True
2 2010-01-06 210.969995 608.259974 30.770000 False
3 2010-01-07 210.580000 594.100015 30.450001 False
4 2010-01-08 211.980005 602.020005 30.660000 True
Apple Google MSFT
0 214.009998 626.750011 30.950001
1 214.379993 623.990017 30.959999
2 210.969995 608.259974 30.770000
3 210.580000 594.100015 30.450001
4 211.980005 602.020005 30.660000
0 True
1 True
2 False
3 False
4 True
Name: Increase, dtype: bool
In [0]:
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20)
In [17]:
print(X_train[:5])
print(X_test[:5])
Apple Google MSFT
384 358.019997 538.260036 26.629999
653 622.550003 642.819985 29.950001
234 318.210007 587.140002 26.870001
365 326.749989 502.950014 23.740000
570 636.229996 630.840033 31.100000
Apple Google MSFT
1357 132.039993 554.250000 47.610001
577 608.340012 607.449994 31.139999
893 418.989990 903.800019 31.820000
45 224.839993 576.450019 28.969999
1373 127.879997 556.179993 46.720001
In [0]:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
In [19]:
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors=5)
classifier.fit(X_train, y_train)
Out[19]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=5, p=2,
weights='uniform')
In [0]:
y_pred = classifier.predict(X_test)
In [21]:
from sklearn.metrics import classification_report, confusion_matrix
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
[[64 79]
[76 74]]
precision recall f1-score support
False 0.46 0.45 0.45 143
True 0.48 0.49 0.49 150
accuracy 0.47 293
macro avg 0.47 0.47 0.47 293
weighted avg 0.47 0.47 0.47 293
In [0]:
In [0]:
error = []
# Calculating error for K values between 1 and 40
for i in range(1, 40):
knn = KNeighborsClassifier(n_neighbors=i)
knn.fit(X_train, y_train)
pred_i = knn.predict(X_test)
error.append(np.mean(pred_i != y_test))
In [0]:
In [23]:
plt.figure(figsize=(12, 6))
plt.plot(range(1, 40), error, color='red', linestyle='dashed', marker='o',
markerfacecolor='blue', markersize=10)
plt.title('Error Rate K Value')
plt.xlabel('K Value')
plt.ylabel('Mean Error')
Out[23]:
Text(0, 0.5, 'Mean Error')
