Tugas 6.1 : Implementasi Logistic Regression dengan SVD

Tugas 6.1 : Implementasi Logistic Regression dengan SVD#

Pada Tugas 6.1 diminta untuk melakukan proses pembuatan model menggunakan algoritma Logistic Regression dengan data yang berbentuk SVD (Singular Value Decomposition).

Dibuat Oleh:

  • Nama : Sabil Ahmad Hidayat

  • NIM : 220411100058

  • Kelas : PPW A

Link Code : https://colab.research.google.com/drive/1z440r1BVFFN0iuTSCtHVKee5VOqYE-o9?usp=sharing

Link Github : meinhere/ppw

Import Library#

# library awal untuk perhitungan dan pengolahan teks
import numpy as np
import pandas as pd

# library untuk proses modeling
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.decomposition import TruncatedSVD

# library untuk evaluasi model
from sklearn.metrics import classification_report, confusion_matrix

# plotting
import matplotlib.pyplot as plt
import seaborn as sns

# load and save model
import pickle
import requests

Preprocessing Data#

Load Data#

Train#

# train_df
train_tfidf_df = pd.read_csv('https://raw.githubusercontent.com/meinhere/ppw/master/publish/tugas-2/dataset/train_df_tfidf.csv', delimiter=',')

train_tfidf_df.head()
aaion aali abadi abai abenkh abnormal absurd ac acara access ... yzr za zad zaman zarco zenix zero zigzag zona label
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.058149 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MONEY
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 MONEY
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 OTOMOTIF
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 OTOMOTIF
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 OTOMOTIF

5 rows × 3107 columns

Test#

# test_df
test_df = pd.read_csv('https://raw.githubusercontent.com/meinhere/ppw/master/publish/tugas-2/dataset/test_df.csv', delimiter=',')

test_df.head()
desc_clean_stem label
0 kompas com unit kelola giat dana amanah daya m... MONEY
1 jakarta kompas com balap gresini racing alex m... OTOMOTIF
2 jakarta kompas com jaksa agung jagung ri salah... MONEY
3 jakarta kompas com pt toyota astra motor tam l... OTOMOTIF
4 jakarta kompas com temu indonesia africa forum... MONEY

Preparing Data Train#

Convert to SVD#

svd_train_df = train_tfidf_df.copy()
svd_train_df.drop(columns=['label'], inplace=True)

svd_train_df
aaion aali abadi abai abenkh abnormal absurd ac acara access ... yusdistira yzr za zad zaman zarco zenix zero zigzag zona
0 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.058149 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
1 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
2 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
3 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
4 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
75 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
76 0.053854 0.0 0.0 0.0 0.0 0.0 0.0 0.037974 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
77 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
78 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.121219 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
79 0.000000 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.059581 0.0

80 rows × 3106 columns

# Menerapkan untuk LSA
n = 80 # Jumlah component/fitur yang ingin dihasilkan dari svd
svd = TruncatedSVD(n_components=n)
svd_matrix = svd.fit_transform(svd_train_df)

# Menampilkan matrix svd
print(f"Shape : {svd_matrix.shape}")
print(svd_matrix)

Shape : (80, 80)
[[ 1.54510634e-01 -9.21097057e-02  9.38904274e-03 ... -1.14938909e-03
  -1.51825296e-04 -1.02999206e-18]
 [ 1.67615266e-01 -1.60479805e-01  1.23665623e-01 ...  3.11792566e-03
   2.75914213e-04 -6.93889390e-18]
 [ 2.80561596e-01 -7.35551633e-02 -6.58748219e-02 ... -1.58720646e-04
  -1.83127832e-03  1.12757026e-17]
 ...
 [ 2.45615049e-01  3.41531512e-02  2.11698472e-01 ...  1.37748793e-03
  -9.76249092e-04  2.60208521e-18]
 [ 3.50620184e-01  4.61250396e-01  1.63725318e-01 ... -2.53879878e-03
   4.38598240e-04  1.40268656e-17]
 [ 2.02074170e-01  4.51572108e-02  2.29484661e-01 ...  3.75707600e-02
   8.40612527e-04  8.67361738e-18]]

# Save the SVD model
with open('svd_model.pkl', 'wb') as f:
    pickle.dump(svd, f)

Preparing Data Test#

Load Vectorizer#

github_raw_url = "https://raw.githubusercontent.com/meinhere/ppw/master/publish/tugas-3/model/tfidf_vectorizer.sav"

response = requests.get(github_raw_url)
response.raise_for_status()

vectorizer = pickle.loads(response.content)
vectorizer

TfidfVectorizer()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Load SVD Model#

svd = pickle.load(open('svd_model.pkl', 'rb'))
svd

TruncatedSVD(n_components=80)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Convert to SVD#

test = test_df['desc_clean_stem']
test_tfidf = vectorizer.transform(test)
vocabulary = vectorizer.get_feature_names_out().tolist()

# Convert to DataFrame for easier handling
test_tfidf_df = pd.DataFrame(test_tfidf.toarray(), columns=vocabulary)
test_tfidf_df['label'] = test_df['label'].tolist()

test_tfidf_df
aaion aali abadi abai abenkh abnormal absurd ac acara access ... yzr za zad zaman zarco zenix zero zigzag zona label
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.041038 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 OTOMOTIF
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 OTOMOTIF
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 OTOMOTIF
7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 OTOMOTIF
10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.113705 0.0 OTOMOTIF
12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 OTOMOTIF
13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 OTOMOTIF
14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 OTOMOTIF
17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY
18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 OTOMOTIF
19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 MONEY

20 rows × 3107 columns

svd_test_df = test_tfidf_df.copy()
svd_test_df.drop(columns=['label'], inplace=True)

svd_test_df
aaion aali abadi abai abenkh abnormal absurd ac acara access ... yusdistira yzr za zad zaman zarco zenix zero zigzag zona
0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.041038 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
5 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
10 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
11 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.113705 0.0
12 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
13 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
14 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
15 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
16 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
17 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
18 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0
19 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.000000 0.0

20 rows × 3106 columns

svd.transform(svd_test_df)

array([[ 0.13890281, -0.12079558,  0.01487368, ..., -0.01012647,
        -0.00392951, -0.00381805],
       [ 0.06850116, -0.02670307,  0.01521463, ..., -0.00611763,
         0.01411249,  0.01274348],
       [ 0.14083866, -0.14551061,  0.07379427, ...,  0.0020201 ,
         0.0315353 , -0.00380684],
       ...,
       [ 0.21009956, -0.18191961, -0.03705772, ..., -0.00539651,
        -0.00278399, -0.00832235],
       [ 0.28349708,  0.09571222,  0.06388888, ..., -0.06758286,
        -0.00434409, -0.00416499],
       [ 0.22535405, -0.16832362, -0.02557858, ..., -0.01824684,
         0.03409403, -0.02484877]])

Encode Label#

Train#

# menggunakan label_encoder untuk merubah kata menjadi angka
label_encoder = preprocessing.LabelEncoder()

svd_train_df = pd.DataFrame(svd_matrix, columns=[f"fitur_{i}" for i in range(n)])
svd_train_df['label'] = label_encoder.fit_transform(train_tfidf_df['label'])

svd_train_df
fitur_0 fitur_1 fitur_2 fitur_3 fitur_4 fitur_5 fitur_6 fitur_7 fitur_8 fitur_9 ... fitur_71 fitur_72 fitur_73 fitur_74 fitur_75 fitur_76 fitur_77 fitur_78 fitur_79 label
0 0.154511 -0.092110 0.009389 -0.057277 0.023159 -0.015457 -0.085723 0.009479 0.042742 0.151400 ... 0.008182 -0.029483 -0.003838 0.006198 -0.000624 -0.001649 -0.001149 -0.000152 -1.029992e-18 0
1 0.167615 -0.160480 0.123666 -0.087440 -0.021450 0.036397 -0.116556 0.003932 0.328147 -0.080676 ... 0.009005 0.004962 0.001842 0.011955 0.000723 -0.003098 0.003118 0.000276 -6.938894e-18 0
2 0.280562 -0.073555 -0.065875 -0.052997 0.086978 0.158835 0.020187 -0.098443 0.021328 0.184733 ... -0.034219 0.032068 0.010054 -0.047236 -0.007279 0.000428 -0.000159 -0.001831 1.127570e-17 1
3 0.228636 -0.021336 0.018543 0.027823 -0.020240 0.121039 -0.021710 -0.057752 -0.041176 0.046354 ... -0.004470 0.011823 0.005718 -0.007954 0.004140 -0.005867 0.000041 0.000736 -6.358846e-17 1
4 0.216845 -0.247775 0.413091 0.035352 -0.232263 0.144427 -0.053011 0.110312 -0.116285 0.000692 ... 0.029571 -0.046789 -0.007026 -0.005451 0.006092 -0.021225 -0.000366 0.000847 -6.125742e-18 1
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
75 0.348659 -0.019416 -0.431868 0.182309 -0.252674 0.160199 -0.213033 0.105406 -0.064889 -0.186460 ... 0.115925 0.016511 0.008167 0.016517 -0.003211 0.003145 0.002238 -0.000534 4.065758e-17 1
76 0.162660 -0.012383 -0.022736 -0.008033 0.005046 -0.014277 -0.012398 0.013250 0.031806 0.147201 ... -0.001109 0.020118 0.003685 -0.014810 0.000066 0.000631 0.001712 0.000256 8.662775e-17 1
77 0.245615 0.034153 0.211698 0.631479 0.367951 -0.225138 -0.035274 0.043684 0.040226 -0.075333 ... 0.023762 0.016052 0.026303 -0.001379 -0.257027 -0.011250 0.001377 -0.000976 2.602085e-18 1
78 0.350620 0.461250 0.163725 -0.290131 0.014102 -0.289675 -0.179755 0.312955 -0.074864 -0.037487 ... 0.420856 0.052031 -0.201563 0.008192 -0.003856 0.008485 -0.002539 0.000439 1.402687e-17 1
79 0.202074 0.045157 0.229485 0.604828 0.406586 -0.134205 -0.103405 0.024887 -0.012957 -0.033503 ... -0.028607 -0.009856 -0.027773 0.014170 -0.164782 -0.006187 0.037571 0.000841 8.673617e-18 1

80 rows × 81 columns

Test#

svd_test_df = pd.DataFrame(svd.transform(svd_test_df), columns=[f"fitur_{i}" for i in range(n)])
svd_test_df['label'] = label_encoder.transform(test_df['label'])

svd_test_df
fitur_0 fitur_1 fitur_2 fitur_3 fitur_4 fitur_5 fitur_6 fitur_7 fitur_8 fitur_9 ... fitur_71 fitur_72 fitur_73 fitur_74 fitur_75 fitur_76 fitur_77 fitur_78 fitur_79 label
0 0.138903 -0.120796 0.014874 -0.047188 0.068682 -0.046017 -0.020225 -0.047264 -0.006490 0.014544 ... 0.012030 0.013598 0.010907 0.018831 0.011180 -0.009576 -0.010126 -0.003930 -0.003818 0
1 0.068501 -0.026703 0.015215 0.007021 0.002277 -0.003760 0.002439 -0.009288 -0.007944 0.000531 ... -0.000678 -0.002572 0.003208 -0.018037 0.026713 -0.003458 -0.006118 0.014112 0.012743 1
2 0.140839 -0.145511 0.073794 -0.097229 0.033568 0.014285 -0.110476 -0.012968 0.388776 -0.090990 ... 0.009462 -0.021879 -0.001093 -0.043368 0.001179 -0.008323 0.002020 0.031535 -0.003807 0
3 0.240121 0.009897 -0.302969 0.179268 -0.162708 0.111860 -0.179074 0.086793 -0.042947 -0.125300 ... 0.089958 0.004362 0.005730 0.014823 -0.007060 0.009424 -0.002525 0.001714 0.008891 1
4 0.176573 -0.151630 -0.038109 -0.075819 0.052371 -0.061953 -0.020599 -0.062116 -0.038795 -0.026010 ... -0.004206 -0.028352 0.003585 -0.020259 -0.003235 -0.006387 -0.001796 0.030949 0.011851 0
5 0.190948 -0.146426 -0.058740 -0.081921 0.121821 0.007882 0.054739 0.074122 0.027870 0.059984 ... 0.006581 -0.004582 0.002811 0.010163 -0.008788 -0.006214 0.014071 -0.107075 0.003470 0
6 0.253080 -0.045199 -0.072818 -0.008129 0.043600 0.101491 0.050720 -0.090886 0.022288 0.168519 ... -0.016157 -0.004601 0.014144 0.009836 -0.008667 0.024004 -0.011852 0.009163 0.003263 1
7 0.160402 -0.121466 -0.013509 -0.035045 0.067659 -0.045630 -0.036278 -0.017147 0.012283 0.017196 ... 0.016810 0.025732 -0.008765 0.016042 0.008459 -0.006878 -0.021374 0.008854 0.000755 0
8 0.057781 -0.055509 0.031545 -0.045710 0.027827 0.008927 -0.052920 -0.010795 0.130949 -0.028965 ... -0.023689 0.017378 -0.014337 0.018912 -0.006293 -0.009305 -0.007926 0.006163 -0.001319 0
9 0.095287 -0.054342 0.071672 0.052127 0.042525 0.001520 -0.043533 0.005327 0.051439 0.001987 ... 0.017279 0.000942 0.012163 0.003978 -0.075792 -0.038897 0.000935 0.003210 0.002725 1
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20 rows × 81 columns

Logistic Regression#

Splitting Data#

# Separate features (X) and target (y) for training data
X_train = svd_train_df.drop('label', axis=1)
y_train = svd_train_df['label']

# Separate features (X) and target (y) for testing data
X_test = svd_test_df.drop('label', axis=1)
y_test = svd_test_df['label']

Training#

# fit model untuk training
lr_model = LogisticRegression()
lr_model.fit(X_train, y_train)

LogisticRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Testing#

# mencoba prediksi dari hasi fitting model
y_pred = lr_model.predict(X_test)

y_pred

array([0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0])

Evaluation#

# melihat nilai actual dan predicted
a = pd.DataFrame({'Actual value': y_test, 'Predicted value':y_pred})
a.head()
Actual value Predicted value
0 0 0
1 1 1
2 0 0
3 1 1
4 0 0 
# Confusion matrix dan classification report
matrix = confusion_matrix(y_test, y_pred)
sns.heatmap(matrix, annot=True, fmt="d")
plt.title('Confusion Matrix')
plt.xlabel('Predicted')
plt.ylabel('True')
print(classification_report(y_test, y_pred))

              precision    recall  f1-score   support

           0       0.92      1.00      0.96        11
           1       1.00      0.89      0.94         9

    accuracy                           0.95        20
   macro avg       0.96      0.94      0.95        20
weighted avg       0.95      0.95      0.95        20
../_images/838845cdf654d3581751d99d2dab4c9ddcf3da9f79fbebf9a381e85f6b8a5b88.png

Saving Model#

# Save the pipeline to a file
filename = 'lr_model.sav'
pickle.dump(lr_model, open(filename, 'wb'))
Contents