{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# **Graded Challenge 5**\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **Perkenalan**\n", "\n", "Nama : Darly Guntur Darris Purba\n", "\n", "Batch : RMT-030\n", "\n", "Data : Credit_card_default\n", "\n", "Link Data : [Credit_Card_Default](https://console.cloud.google.com/bigquery?p=bigquery-public-data&d=ml_datasets&t=credit_card_default&page=table&project=adroit-braid-417802&ws=!1m14!1m3!8m2!1s346962191336!2s438dcd490ec649929d0e8ec157b38f3f!1m4!4m3!1sbigquery-public-data!2sml_datasets!3scredit_card_default!1m4!4m3!1sadroit-braid-417802!2s_5fa8e0501cf3b1f43e47211538afbdfa0fb644d2!3sanon1fbfb8f1383b44830542c5f6ff644faeb959e372097d43a67d6cd0534815aa42)\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **Objektif**\n", "\n", "Seorang *data science* yang bekerja dalam instusi perbankan sedang menganalisa apakah ada nasabah yang mengalami gagal membayar tagihan pada kartu kredit. Untuk itu diberikan data yang berisi data nasabah beserta dengan pembayaran. Untuk menemukan permassalah tersebut, dilakukan pemodelan dengan metode *classification*\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **Identifikasi Masalah** \n", "\n", "Diberikan data `Credit_Card_Default` yang bersumber dari *Google Cloud Platform*. Analisis dan buat model dengan menggunakan *Classification Regression* untuk menganalisa masalah gagal bayar *default_payment_next_month* dengan memperhatikan aspek:\n", "\n", "- Coeficient pada metode *logistic regression*\n", "\n", "- Fungsi parameter kernel pada metode *Support Vector Machine* (SVM)\n", "\n", "- Pemilihan K yang optimal pada metode *K-Nearest Neighbor* (KNN)\n", "\n", "- Parameter `Accuracy`, `Precision`, `Recall`, `F1 Score`\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ***Import Libraries***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pickle\n", "import json\n", "from feature_engine.outliers import Winsorizer\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.preprocessing import RobustScaler\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.pipeline import Pipeline\n", "from sklearn.ensemble import VotingClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay, f1_score, accuracy_score, precision_score, recall_score\n", "from scipy import stats\n", "from scipy.stats.mstats import winsorize\n", "from statsmodels.stats.outliers_influence import variance_inflation_factor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ***Data Loading***\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ***Syntax SQL Biqquery***" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "# SELECT limit_balance, CAST(sex AS INT64) as sex, CAST(education_level AS INT64) as education_level, CAST(marital_status AS INT64) as marital_status, age, pay_0, pay_2, pay_3, pay_4, CAST(pay_5 AS FLOAT64) as pay_5, CAST(pay_6 AS FLOAT64) as pay_6, bill_amt_1, bill_amt_2, bill_amt_3, bill_amt_4, bill_amt_5, bill_amt_6, pay_amt_1, pay_amt_2, pay_amt_3, pay_amt_4, pay_amt_5, pay_amt_6, CAST(default_payment_next_month AS INT64) as default_payment_next_month\n", "# FROM bigquery-public-data.ml_datasets.credit_card_default\n", "# LIMIT 20000;" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "### **Membuka Data CSV**" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# Membuka Data CSV\n", "df = pd.read_csv('credit_card_default.csv', na_values=['N/A', 'NA', 'NaN'], index_col=False)" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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limit_balancesexeducation_levelmarital_statusagepay_0pay_2pay_3pay_4pay_5pay_6bill_amt_1bill_amt_2bill_amt_3bill_amt_4bill_amt_5bill_amt_6pay_amt_1pay_amt_2pay_amt_3pay_amt_4pay_amt_5pay_amt_6default_payment_next_month
0800001615400000061454618086229029296262101764325452208133622325423481
1200000141490000004922149599509425014650235489841689216425003480250030000
2200002622200000019568194201553514345000464110199000150001
3260000242330000001845722815270862782130767298905000500011375000108550000
415000014232000-1001599196868616119215046414337514641140191468961574364600470956000
53000002423200000-154053652356474765150-45070015235149113030200014000
6130000111450000005818059134611566237763832650992886290821292354236622910
7200000111580000001924611959701222141246471269211291677822441744464597467746980
8500000111390000001335981673781711061745001374062049755420946074603522420744075090
9230000111480000001608791617971651071055081081011100947000660737734290416420000
\n", "
" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_0 pay_2 \\\n", "0 80000 1 6 1 54 0 0 \n", "1 200000 1 4 1 49 0 0 \n", "2 20000 2 6 2 22 0 0 \n", "3 260000 2 4 2 33 0 0 \n", "4 150000 1 4 2 32 0 0 \n", "5 300000 2 4 2 32 0 0 \n", "6 130000 1 1 1 45 0 0 \n", "7 200000 1 1 1 58 0 0 \n", "8 500000 1 1 1 39 0 0 \n", "9 230000 1 1 1 48 0 0 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 bill_amt_4 \\\n", "0 0 0 0 0 61454 61808 62290 29296 \n", "1 0 0 0 0 49221 49599 50942 50146 \n", "2 0 0 0 0 19568 19420 15535 1434 \n", "3 0 0 0 0 18457 22815 27086 27821 \n", "4 0 -1 0 0 159919 68686 161192 150464 \n", "5 0 0 0 -1 54053 65235 64747 65150 \n", "6 0 0 0 0 58180 59134 61156 62377 \n", "7 0 0 0 0 192461 195970 122214 124647 \n", "8 0 0 0 0 133598 167378 171106 174500 \n", "9 0 0 0 0 160879 161797 165107 105508 \n", "\n", " bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 pay_amt_4 \\\n", "0 26210 17643 2545 2208 1336 2232 \n", "1 50235 48984 1689 2164 2500 3480 \n", "2 500 0 4641 1019 900 0 \n", "3 30767 29890 5000 5000 1137 5000 \n", "4 143375 146411 4019 146896 157436 4600 \n", "5 -450 700 15235 1491 1303 0 \n", "6 63832 65099 2886 2908 2129 2354 \n", "7 126921 129167 7822 4417 4446 4597 \n", "8 137406 204975 54209 4607 4603 5224 \n", "9 108101 110094 7000 6607 3773 4290 \n", "\n", " pay_amt_5 pay_amt_6 default_payment_next_month \n", "0 542 348 1 \n", "1 2500 3000 0 \n", "2 1500 0 1 \n", "3 1085 5000 0 \n", "4 4709 5600 0 \n", "5 2000 1400 0 \n", "6 2366 2291 0 \n", "7 4677 4698 0 \n", "8 207440 7509 0 \n", "9 4164 2000 0 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan 10 Data Teratas\n", "pd.options.display.max_columns = None\n", "df.head(10)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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limit_balancesexeducation_levelmarital_statusagepay_0pay_2pay_3pay_4pay_5pay_6bill_amt_1bill_amt_2bill_amt_3bill_amt_4bill_amt_5bill_amt_6pay_amt_1pay_amt_2pay_amt_3pay_amt_4pay_amt_5pay_amt_6default_payment_next_month
295536000022226-1-1-1-1-2-219684592500000463250000000
29561000001314000-1-1-2-2128787702237700020002377400000000
295730000231481-1-1-2-2-2-1001000000200000000
29588000023139-1-1-1-1-2-1528050005000005000500050000500050004700
29592000013226-1-1-1-2-2-29678000001560000000
29608000023228-1-1-1-2-2-24280280000002800000000
29615000023151-1-1-1-1-2-27523005880000300588000001
296245000022138-2-2-2-2-2-23903903903903903903907803903903903901
29635000022144-2-2-2-2-2-21473390390390390039039039039007800
2964290000221391-2-2-2-2-2-709540390318439039010000800318439039066170
\n", "
" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_0 pay_2 \\\n", "2955 360000 2 2 2 26 -1 -1 \n", "2956 100000 1 3 1 40 0 0 \n", "2957 30000 2 3 1 48 1 -1 \n", "2958 80000 2 3 1 39 -1 -1 \n", "2959 20000 1 3 2 26 -1 -1 \n", "2960 80000 2 3 2 28 -1 -1 \n", "2961 50000 2 3 1 51 -1 -1 \n", "2962 450000 2 2 1 38 -2 -2 \n", "2963 50000 2 2 1 44 -2 -2 \n", "2964 290000 2 2 1 39 1 -2 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 \\\n", "2955 -1 -1 -2 -2 1968 459 2500 \n", "2956 -1 -1 -2 -2 12878 7702 2377 \n", "2957 -1 -2 -2 -2 -100 100 0 \n", "2958 -1 -1 -2 -1 5280 5000 5000 \n", "2959 -1 -2 -2 -2 96 780 0 \n", "2960 -1 -2 -2 -2 4280 2800 0 \n", "2961 -1 -1 -2 -2 752 300 5880 \n", "2962 -2 -2 -2 -2 390 390 390 \n", "2963 -2 -2 -2 -2 1473 390 390 \n", "2964 -2 -2 -2 -2 -70 9540 390 \n", "\n", " bill_amt_4 bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 \\\n", "2955 0 0 0 463 2500 0 \n", "2956 0 0 0 2000 2377 40000 \n", "2957 0 0 0 200 0 0 \n", "2958 0 0 5000 5000 5000 0 \n", "2959 0 0 0 1560 0 0 \n", "2960 0 0 0 2800 0 0 \n", "2961 0 0 0 300 5880 0 \n", "2962 390 390 390 390 780 390 \n", "2963 390 390 0 390 390 390 \n", "2964 3184 390 390 10000 800 3184 \n", "\n", " pay_amt_4 pay_amt_5 pay_amt_6 default_payment_next_month \n", "2955 0 0 0 0 \n", "2956 0 0 0 0 \n", "2957 0 0 0 0 \n", "2958 5000 5000 470 0 \n", "2959 0 0 0 0 \n", "2960 0 0 0 0 \n", "2961 0 0 0 1 \n", "2962 390 390 390 1 \n", "2963 390 0 780 0 \n", "2964 390 390 6617 0 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan 10 Data Terbawah\n", "df.tail(10)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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limit_balancesexeducation_levelmarital_statusagepay_0pay_2pay_3pay_4pay_5pay_6bill_amt_1bill_amt_2bill_amt_3bill_amt_4bill_amt_5bill_amt_6pay_amt_1pay_amt_2pay_amt_3pay_amt_4pay_amt_5pay_amt_6default_payment_next_month
1001500001123300000078038631765280734199320612368250001011650006000300070000
10117000011239000000171160187853171095137986141319139463130006154150006000700050000
1022900001124000000013552713441414060713193313560613290680001000050006000470050000
103360000111360000002829131055337883649141179458233000300030005000500050000
104500000111480000001075951096051134091141911172851203885000557141525000500050000
1052800001123000000010074010405510638011259611423511723250004000800050005000180000
1062300001123000000010187497302973839748796588952218000501650005000500052000
107230000112320000004473447178295823842642500435311012020000100005000500050000
108180000112320000001543741530181463961365311385021366657500700050005000600051370
109470000112330000007208368136690367146667538704005031550050005000400050000
11016000011228000000157921144659154012151403115731113635600025409300005000500045000
11117000011141000000149941689127274176149844749240032006000500010000100007800
112400000112340000002691224967138101823025050102091001660001000010000500040000
1133800001123300000070591767158008081162895719662710000500050001000010000100000
114370000112300000003339302807272857052957472501582559561300011000150001000010000120000
\n", "
" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_0 pay_2 \\\n", "100 150000 1 1 2 33 0 0 \n", "101 170000 1 1 2 39 0 0 \n", "102 290000 1 1 2 40 0 0 \n", "103 360000 1 1 1 36 0 0 \n", "104 500000 1 1 1 48 0 0 \n", "105 280000 1 1 2 30 0 0 \n", "106 230000 1 1 2 30 0 0 \n", "107 230000 1 1 2 32 0 0 \n", "108 180000 1 1 2 32 0 0 \n", "109 470000 1 1 2 33 0 0 \n", "110 160000 1 1 2 28 0 0 \n", "111 170000 1 1 1 41 0 0 \n", "112 400000 1 1 2 34 0 0 \n", "113 380000 1 1 2 33 0 0 \n", "114 370000 1 1 2 30 0 0 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 \\\n", "100 0 0 0 0 78038 63176 52807 \n", "101 0 0 0 0 171160 187853 171095 \n", "102 0 0 0 0 135527 134414 140607 \n", "103 0 0 0 0 28291 31055 33788 \n", "104 0 0 0 0 107595 109605 113409 \n", "105 0 0 0 0 100740 104055 106380 \n", "106 0 0 0 0 101874 97302 97383 \n", "107 0 0 0 0 44734 47178 29582 \n", "108 0 0 0 0 154374 153018 146396 \n", "109 0 0 0 0 72083 68136 69036 \n", "110 0 0 0 0 157921 144659 154012 \n", "111 0 0 0 0 149941 68912 72741 \n", "112 0 0 0 0 26912 24967 13810 \n", "113 0 0 0 0 70591 76715 80080 \n", "114 0 0 0 0 333930 280727 285705 \n", "\n", " bill_amt_4 bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 \\\n", "100 34199 32061 23682 5000 10116 5000 \n", "101 137986 141319 139463 13000 6154 15000 \n", "102 131933 135606 132906 8000 10000 5000 \n", "103 36491 41179 45823 3000 3000 3000 \n", "104 114191 117285 120388 5000 5571 4152 \n", "105 112596 114235 117232 5000 4000 8000 \n", "106 97487 96588 95221 8000 5016 5000 \n", "107 38426 42500 43531 10120 20000 10000 \n", "108 136531 138502 136665 7500 7000 5000 \n", "109 71466 67538 70400 5031 5500 5000 \n", "110 151403 115731 113635 6000 25409 30000 \n", "111 76149 84474 92400 3200 6000 5000 \n", "112 18230 25050 10209 10016 6000 10000 \n", "113 81162 89571 96627 10000 5000 5000 \n", "114 295747 250158 255956 13000 11000 15000 \n", "\n", " pay_amt_4 pay_amt_5 pay_amt_6 default_payment_next_month \n", "100 6000 3000 7000 0 \n", "101 6000 7000 5000 0 \n", "102 6000 4700 5000 0 \n", "103 5000 5000 5000 0 \n", "104 5000 5000 5000 0 \n", "105 5000 5000 18000 0 \n", "106 5000 5000 5200 0 \n", "107 5000 5000 5000 0 \n", "108 5000 6000 5137 0 \n", "109 5000 4000 5000 0 \n", "110 5000 5000 4500 0 \n", "111 10000 10000 780 0 \n", "112 10000 5000 4000 0 \n", "113 10000 10000 10000 0 \n", "114 10000 10000 12000 0 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilan Data Pada Rentang Tertentu \n", "df.iloc[100:115]" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['limit_balance', 'sex', 'education_level', 'marital_status', 'age',\n", " 'pay_0', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6', 'bill_amt_1',\n", " 'bill_amt_2', 'bill_amt_3', 'bill_amt_4', 'bill_amt_5', 'bill_amt_6',\n", " 'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6', 'default_payment_next_month'],\n", " dtype='object')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Kolom pada Data\n", "df.columns" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2965, 24)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Jumlah Baris dan Kolom\n", "df.shape" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 2965 entries, 0 to 2964\n", "Data columns (total 24 columns):\n", " # Column Non-Null Count Dtype\n", "--- ------ -------------- -----\n", " 0 limit_balance 2965 non-null int64\n", " 1 sex 2965 non-null int64\n", " 2 education_level 2965 non-null int64\n", " 3 marital_status 2965 non-null int64\n", " 4 age 2965 non-null int64\n", " 5 pay_0 2965 non-null int64\n", " 6 pay_2 2965 non-null int64\n", " 7 pay_3 2965 non-null int64\n", " 8 pay_4 2965 non-null int64\n", " 9 pay_5 2965 non-null int64\n", " 10 pay_6 2965 non-null int64\n", " 11 bill_amt_1 2965 non-null int64\n", " 12 bill_amt_2 2965 non-null int64\n", " 13 bill_amt_3 2965 non-null int64\n", " 14 bill_amt_4 2965 non-null int64\n", " 15 bill_amt_5 2965 non-null int64\n", " 16 bill_amt_6 2965 non-null int64\n", " 17 pay_amt_1 2965 non-null int64\n", " 18 pay_amt_2 2965 non-null int64\n", " 19 pay_amt_3 2965 non-null int64\n", " 20 pay_amt_4 2965 non-null int64\n", " 21 pay_amt_5 2965 non-null int64\n", " 22 pay_amt_6 2965 non-null int64\n", " 23 default_payment_next_month 2965 non-null int64\n", "dtypes: int64(24)\n", "memory usage: 556.1 KB\n" ] } ], "source": [ "# Menampilkan informasi data\n", "df.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Insight* :\n", "\n", "Adapun keterangan untuk masing masing kolom yakni:\n", "\n", "Kolom | Keterangan\n", "--- | ---\n", "limit_balance | Jumlah kredit yang diberikan dalam Dolar NT (termasuk kredit individu dan keluarga)\n", "sex | Jenis Kelamin (1 = male, 2 = female)\n", "education_level | Tingkat Pendidikan (1 = graduate school, 2 = university, 3 = high school, 4 = others, 5 = unknown, 6 = unknown)\n", "marital_status | Status Pernikahan (1 = married, 2 = single, 3 = others)\n", "age | Umur\n", "pay_0 | Status pelunasan bulan September 2005 (-1=bayar lunas, 1=keterlambatan pembayaran selama satu bulan, 2=keterlambatan pembayaran selama dua bulan, ... 8=keterlambatan pembayaran selama delapan bulan, 9=keterlambatan pembayaran selama sembilan bulan ke atas)\n", "pay_2 | Status pelunasan bulan Agustus 2005 (-1=bayar lunas, 1=keterlambatan pembayaran selama satu bulan, 2=keterlambatan pembayaran selama dua bulan, ... 8=keterlambatan pembayaran selama delapan bulan, 9=keterlambatan pembayaran selama sembilan bulan ke atas)\n", "pay_3 | Status pelunasan bulan Juli 2005 (-1=bayar lunas, 1=keterlambatan pembayaran selama satu bulan, 2=keterlambatan pembayaran selama dua bulan, ... 8=keterlambatan pembayaran selama delapan bulan, 9=keterlambatan pembayaran selama sembilan bulan ke atas)\n", "pay_4 | Status pelunasan bulan Juni 2005 (-1=bayar lunas, 1=keterlambatan pembayaran selama satu bulan, 2=keterlambatan pembayaran selama dua bulan, ... 8=keterlambatan pembayaran selama delapan bulan, 9=keterlambatan pembayaran selama sembilan bulan ke atas)\n", "pay_5 | Status pelunasan bulan Mei 2005 (-1=bayar lunas, 1=keterlambatan pembayaran selama satu bulan, 2=keterlambatan pembayaran selama dua bulan, ... 8=keterlambatan pembayaran selama delapan bulan, 9=keterlambatan pembayaran selama sembilan bulan ke atas)\n", "pay_6 | Status pelunasan bulan April 2005 (-1=bayar lunas, 1=keterlambatan pembayaran selama satu bulan, 2=keterlambatan pembayaran selama dua bulan, ... 8=keterlambatan pembayaran selama delapan bulan, 9=keterlambatan pembayaran selama sembilan bulan ke atas)\n", "bill_amt_1 | Jumlah tagihan bulan September 2005 (Dolar NT)\n", "bill_amt_2 | Jumlah tagihan bulan Agustus 2005 (Dolar NT)\n", "bill_amt_3 | Jumlah tagihan bulan Juli 2005 (Dolar NT)\n", "bill_amt_4 | Jumlah tagihan bulan Juni 2005 (Dolar NT)\n", "bill_amt_5 | Jumlah tagihan bulan Mei 2005 (Dolar NT)\n", "bill_amt_6 | Jumlah tagihan bulan April 2005 (Dolar NT)\n", "pay_amt_1 | Jumlah pembayaran sebelumnya pada bulan September 2005 (Dolar NT)\n", "pay_amt_2 | Jumlah pembayaran sebelumnya pada bulan Agustus 2005 (Dolar NT)\n", "pay_amt_3 | Jumlah pembayaran sebelumnya pada bulan Juli 2005 (Dolar NT)\n", "pay_amt_4 | Jumlah pembayaran sebelumnya pada bulan Juni 2005 (Dolar NT)\n", "pay_amt_5 | Jumlah pembayaran sebelumnya pada bulan Mei 2005 (Dolar NT)\n", "pay_amt_6 | Jumlah pembayaran sebelumnya pada bulan April 2005 (Dolar NT)\n", "default_payment_next_month | Gagal membayar tagihan (1 = yes, 0 = no)\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# Mengganti Nama Kolom\n", "df.rename(columns={'pay_0': 'pay_1'}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# Mengganti Isi kolom ke dalam Kolom Lain\n", "df['education_level'] = df['education_level'].replace(to_replace=[0, 5, 6], value=4)\n", "df['marital_status'] = df['marital_status'].replace(to_replace=0, value=3)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Duplikat\n", "df.duplicated().sum()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "limit_balance 0\n", "sex 0\n", "education_level 0\n", "marital_status 0\n", "age 0\n", "pay_1 0\n", "pay_2 0\n", "pay_3 0\n", "pay_4 0\n", "pay_5 0\n", "pay_6 0\n", "bill_amt_1 0\n", "bill_amt_2 0\n", "bill_amt_3 0\n", "bill_amt_4 0\n", "bill_amt_5 0\n", "bill_amt_6 0\n", "pay_amt_1 0\n", "pay_amt_2 0\n", "pay_amt_3 0\n", "pay_amt_4 0\n", "pay_amt_5 0\n", "pay_amt_6 0\n", "default_payment_next_month 0\n", "dtype: int64" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Nilai Missing Value\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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limit_balancesexeducation_levelmarital_statusagepay_1pay_2pay_3pay_4pay_5pay_6bill_amt_1bill_amt_2bill_amt_3bill_amt_4bill_amt_5bill_amt_6pay_amt_1pay_amt_2pay_amt_3pay_amt_4pay_amt_5pay_amt_6default_payment_next_month
2806200000211341-2-2-2-2-20000000000000
2815200000211341-2-2-2-2-20000000000000
\n", "
" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_1 pay_2 \\\n", "2806 200000 2 1 1 34 1 -2 \n", "2815 200000 2 1 1 34 1 -2 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 \\\n", "2806 -2 -2 -2 -2 0 0 0 \n", "2815 -2 -2 -2 -2 0 0 0 \n", "\n", " bill_amt_4 bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 \\\n", "2806 0 0 0 0 0 0 \n", "2815 0 0 0 0 0 0 \n", "\n", " pay_amt_4 pay_amt_5 pay_amt_6 default_payment_next_month \n", "2806 0 0 0 0 \n", "2815 0 0 0 0 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Melakukan Permeriksaan Terhadap Data Duplikasi\n", "duplicated_data = df.duplicated(keep=False)\n", "duplicate_rows = df[duplicated_data]\n", "duplicate_rows" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Pada dataframe `credit_card_default.csv` dapat ditemukan:\n", "\n", "- Terdapat 2965 baris dan 24 kolom \n", "\n", "- Ditemukan data yang mengalami duplikasi dan ditemukan 1 data yang mengalami duplikasi. Namun data ini tidak dihapus dengan pertimbangan nasabah pada data ini kebetulan mengalami situasi hal yang sama dari semua aspek \n", "\n", "- Tidak ditemukan data yang mengalami *missing value*\n", "\n", "- Pada kolom `pay_0` akan dilakukan perubahan nama menjadi `pay_1` dikarenakan untuk menyesuaikan dengan kolom `bill_amt_1` dan `pay_amt_1`\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ***Exploratory Data Analysis***\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Visualisasi Data Sedehana**\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melihat Hubungan Jenis Kelamin dengan Status Pembayaran\n", "sns.barplot(x='sex', y='default_payment_next_month', data=df)\n", "\n", "# Menambah Title dan Label\n", "plt.title('Hubungan Jenis Kelamin dengan Gagal Bayar')\n", "plt.xlabel('Jenis Kelamin')\n", "plt.ylabel('Gagal Bayar')\n", "\n", "# Menampilkan Plot\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Ketika jenis kelamin dan gagal bayar diplot dalam tabel ditemukan bahwa nasabah pria cenderung mengalami kemungkinan untuk gagal bayar dibandingkan wanita \n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melihat Tingkat Pendidikan dengan Gagal Bayar\n", "sns.barplot(x='education_level', y='default_payment_next_month', data=df)\n", "\n", "# Menambah Title dan Label\n", "plt.title('Hubungan Tingkat Pendidikan dengan Gagal Bayar')\n", "plt.xlabel('Tingkat Pendidikan')\n", "plt.ylabel('Gagal Bayar')\n", "\n", "# Menampilkan Plot\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Pada grafik tingkat pendidikan dan gagal bayar ditemukan nasabah yang tidak diketahui tingkat pendidikannya (pada kolom 6) memiliki tingkat kemungkinan gagal bayar dibandingkan dengan tingkat pendidikan lainnya \n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melihat Status Pernikahan dengan Gagal Bayar\n", "sns.barplot(x='marital_status', y='default_payment_next_month', data=df)\n", "\n", "# Menambah Title dan Label\n", "plt.title('Hubungan Status Pernikahan dengan Gagal Bayar')\n", "plt.xlabel('Status Pernikahan')\n", "plt.ylabel('Gagal Bayar')\n", "\n", "# Menampilkan Plot\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Status pernikahan juga mempengaruhi tingkat kemungkinan untuk gagal membayar, pada grafik ditunjukkan untuk mereka yang menikah memiliki potensi tinggi untuk kemungkinan mengalami gagal membayar tagihan dibanding lainnya\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melihat Status Pernikahan dengan Gagal Bayar\n", "sns.barplot(x='pay_1', y='default_payment_next_month', data=df)\n", "\n", "# Menambah Title dan Label\n", "plt.title('Hubungan Status Pelunasan (September) dengan Gagal Bayar')\n", "plt.xlabel('Status Pelunasan (September)')\n", "plt.ylabel('Gagal Bayar')\n", "\n", "# Menampilkan Plot\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Untuk status pelunasan pada bulan September diketahui terjadi keterlambatan pembayaran hingga mencapai 7 bulan. Keterlambatan ini sungguh fantasis \n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melihat Status Pernikahan dengan Gagal Bayar\n", "sns.barplot(x='pay_2', y='default_payment_next_month', data=df)\n", "\n", "# Menambah Title dan Label\n", "plt.title('Hubungan Status Pelunasan (Agustus) dengan Gagal Bayar')\n", "plt.xlabel('Status Pelunasan (Agustus)')\n", "plt.ylabel('Gagal Bayar')\n", "\n", "# Menampilkan Plot\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Tidak terlalu jauh berbeda dengan bulan September diketahui terjadi keterlambatan pembayaran hingga mencapai 6 bulan untuk bulan Agustus \n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melihat Status Pernikahan dengan Gagal Bayar\n", "sns.barplot(x='pay_3', y='default_payment_next_month', data=df)\n", "\n", "# Menambah Title dan Label\n", "plt.title('Hubungan Status Pelunasan (Juli) dengan Gagal Bayar')\n", "plt.xlabel('Status Pelunasan (Juli)')\n", "plt.ylabel('Gagal Bayar')\n", "\n", "# Menampilkan Plot\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Pada grafik status penjualan pada bulan Juli cukup menarik karena ditemukan nilai yang sama pada keterlambatan 5 bulan dan keterlambatan 7 bulan\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melihat Status Pernikahan dengan Gagal Bayar\n", "sns.barplot(x='pay_4', y='default_payment_next_month', data=df)\n", "\n", "# Menambah Title dan Label\n", "plt.title('Hubungan Status Pelunasan (Juni) dengan Gagal Bayar')\n", "plt.xlabel('Status Pelunasan (Juni)')\n", "plt.ylabel('Gagal Bayar')\n", "\n", "# Menampilkan Plot\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Tidak jauh berbeda dengan grafik status penjualan pada bulan Agustus, ditemukan nilai keterlambatan selama 6 bulan untuk bulan Juni\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melihat Status Pernikahan dengan Gagal Bayar\n", "sns.barplot(x='pay_5', y='default_payment_next_month', data=df)\n", "\n", "# Menambah Title dan Label\n", "plt.title('Hubungan Status Pelunasan (Mei) dengan Gagal Bayar')\n", "plt.xlabel('Status Pelunasan (Mei)')\n", "plt.ylabel('Gagal Bayar')\n", "\n", "# Menampilkan Plot\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Tidak jauh berbeda dengan grafik status penjualan pada bulan Juli, ditemukan nilai keterlambatan yang sama untuk 5 bulan dan 6 bulan untuk bulan Mei\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melihat Status Pernikahan dengan Gagal Bayar\n", "sns.barplot(x='pay_6', y='default_payment_next_month', data=df)\n", "\n", "# Menambah Title dan Label\n", "plt.title('Hubungan Status Pelunasan (April) dengan Gagal Bayar')\n", "plt.xlabel('Status Pelunasan (April)')\n", "plt.ylabel('Gagal Bayar')\n", "\n", "# Menampilkan Plot\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Status pelunasan yang mengalami keterlambatan bayar selama 4 bulan ditemukan sebagai data tertinggi untuk bulan April\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Cek Statistika Deskriptif**\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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limit_balancesexeducation_levelmarital_statusagepay_1pay_2pay_3pay_4pay_5pay_6bill_amt_1bill_amt_2bill_amt_3bill_amt_4bill_amt_5bill_amt_6pay_amt_1pay_amt_2pay_amt_3pay_amt_4pay_amt_5pay_amt_6default_payment_next_month
count2965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002965.0000002.965000e+032965.0000002965.0000002965.0000002965.0000002965.000000
mean163369.3086001.6077571.8414841.56391235.1932550.005059-0.122428-0.141653-0.185160-0.225295-0.25463752118.30522850649.15312048239.75750444089.68330540956.08060739773.0725136348.9028676.272494e+035150.4971334561.3760544913.2866785382.7015180.214165
std125030.4154720.4883330.7391810.5218379.1094391.1143951.1807841.1836301.1783221.1590031.16730572328.67054170785.00158868145.71074561907.45405658271.90475157303.48898120885.7353362.887967e+0414287.07998213281.49959916734.34077817275.9530290.410311
min10000.0000001.0000001.0000001.00000021.000000-2.000000-2.000000-2.000000-2.000000-2.000000-2.000000-11545.000000-67526.000000-25443.000000-46627.000000-46627.000000-73895.0000000.0000000.000000e+000.0000000.0000000.0000000.0000000.000000
25%50000.0000001.0000001.0000001.00000028.000000-1.000000-1.000000-1.000000-1.000000-1.000000-1.0000003958.0000003390.0000003302.0000002582.0000001958.0000001430.0000001013.0000009.900000e+02477.000000313.000000323.000000173.0000000.000000
50%140000.0000002.0000002.0000002.00000034.0000000.0000000.0000000.0000000.0000000.0000000.00000024257.00000023111.00000021520.00000019894.00000018814.00000018508.0000002234.0000002.175000e+031994.0000001600.0000001646.0000001615.0000000.000000
75%230000.0000002.0000002.0000002.00000041.0000000.0000000.0000000.0000000.0000000.0000000.00000069852.00000067827.00000063023.00000058622.00000053373.00000052287.0000005087.0000005.000000e+034500.0000004000.0000004021.0000004081.0000000.000000
max800000.0000002.0000004.0000003.00000069.0000008.0000007.0000007.0000008.0000007.0000007.000000613860.000000512650.000000578971.000000488808.000000441981.000000436172.000000493358.0000001.227082e+06199209.000000202076.000000388071.000000403500.0000001.000000
\n", "
" ], "text/plain": [ " limit_balance sex education_level marital_status \\\n", "count 2965.000000 2965.000000 2965.000000 2965.000000 \n", "mean 163369.308600 1.607757 1.841484 1.563912 \n", "std 125030.415472 0.488333 0.739181 0.521837 \n", "min 10000.000000 1.000000 1.000000 1.000000 \n", "25% 50000.000000 1.000000 1.000000 1.000000 \n", "50% 140000.000000 2.000000 2.000000 2.000000 \n", "75% 230000.000000 2.000000 2.000000 2.000000 \n", "max 800000.000000 2.000000 4.000000 3.000000 \n", "\n", " age pay_1 pay_2 pay_3 pay_4 \\\n", "count 2965.000000 2965.000000 2965.000000 2965.000000 2965.000000 \n", "mean 35.193255 0.005059 -0.122428 -0.141653 -0.185160 \n", "std 9.109439 1.114395 1.180784 1.183630 1.178322 \n", "min 21.000000 -2.000000 -2.000000 -2.000000 -2.000000 \n", "25% 28.000000 -1.000000 -1.000000 -1.000000 -1.000000 \n", "50% 34.000000 0.000000 0.000000 0.000000 0.000000 \n", "75% 41.000000 0.000000 0.000000 0.000000 0.000000 \n", "max 69.000000 8.000000 7.000000 7.000000 8.000000 \n", "\n", " pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 \\\n", "count 2965.000000 2965.000000 2965.000000 2965.000000 2965.000000 \n", "mean -0.225295 -0.254637 52118.305228 50649.153120 48239.757504 \n", "std 1.159003 1.167305 72328.670541 70785.001588 68145.710745 \n", "min -2.000000 -2.000000 -11545.000000 -67526.000000 -25443.000000 \n", "25% -1.000000 -1.000000 3958.000000 3390.000000 3302.000000 \n", "50% 0.000000 0.000000 24257.000000 23111.000000 21520.000000 \n", "75% 0.000000 0.000000 69852.000000 67827.000000 63023.000000 \n", "max 7.000000 7.000000 613860.000000 512650.000000 578971.000000 \n", "\n", " bill_amt_4 bill_amt_5 bill_amt_6 pay_amt_1 \\\n", "count 2965.000000 2965.000000 2965.000000 2965.000000 \n", "mean 44089.683305 40956.080607 39773.072513 6348.902867 \n", "std 61907.454056 58271.904751 57303.488981 20885.735336 \n", "min -46627.000000 -46627.000000 -73895.000000 0.000000 \n", "25% 2582.000000 1958.000000 1430.000000 1013.000000 \n", "50% 19894.000000 18814.000000 18508.000000 2234.000000 \n", "75% 58622.000000 53373.000000 52287.000000 5087.000000 \n", "max 488808.000000 441981.000000 436172.000000 493358.000000 \n", "\n", " pay_amt_2 pay_amt_3 pay_amt_4 pay_amt_5 \\\n", "count 2.965000e+03 2965.000000 2965.000000 2965.000000 \n", "mean 6.272494e+03 5150.497133 4561.376054 4913.286678 \n", "std 2.887967e+04 14287.079982 13281.499599 16734.340778 \n", "min 0.000000e+00 0.000000 0.000000 0.000000 \n", "25% 9.900000e+02 477.000000 313.000000 323.000000 \n", "50% 2.175000e+03 1994.000000 1600.000000 1646.000000 \n", "75% 5.000000e+03 4500.000000 4000.000000 4021.000000 \n", "max 1.227082e+06 199209.000000 202076.000000 388071.000000 \n", "\n", " pay_amt_6 default_payment_next_month \n", "count 2965.000000 2965.000000 \n", "mean 5382.701518 0.214165 \n", "std 17275.953029 0.410311 \n", "min 0.000000 0.000000 \n", "25% 173.000000 0.000000 \n", "50% 1615.000000 0.000000 \n", "75% 4081.000000 0.000000 \n", "max 403500.000000 1.000000 " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Melihat Informasi Statistika\n", "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Untuk analisa statistka deskriptif, dilakukan analisa secara sekilas perbandingan antara nilai median dan mean. Ternyata perbandingan ini cukup variatif karena ditemukan adanya nilai median yang lebih tinggi dari mean, nilai mean lebih tinggi dari median hingga data terdistribusi normal. Hal ini menandakan ada skewness dan bernilai positif maupun negatif walau masih ada ditemukan juga data terdistribusi normal\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Menampilkan Boxplot Untuk Setiap Kolom**\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Membuat Fungsi Boxplot\n", "def boxplot_for_all_columns(df):\n", "\n", " # Filter hanya kolom numerik\n", " numeric_columns = df.select_dtypes(include=['int', 'float']).columns\n", " \n", " # Mengatur ukuran plot\n", " plt.figure(figsize=(20, 15))\n", "\n", " # Loop melalui setiap kolom numerik dan membuat box plot\n", " for i, column in enumerate(numeric_columns, 1):\n", " plt.subplot(7, 8, i)\n", " sns.boxplot(y=df[column])\n", " plt.title(column)\n", "\n", " plt.tight_layout()\n", " plt.show()\n", "\n", "# Contoh pemanggilan fungsi\n", "boxplot_for_all_columns(df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Bedasarkan analisa statistika deskriptif yang dicari sebelumnya untuk melihat data mean dan data median untuk melihat *skewness*, dilakukan pemodelan dalam bentuk gambar untuk membuktikan adanya outlier. Dari boxplot, ditemukan ternyata semua kolom memiliki outlier kecuali kolom `sex` dan `marital status`. Adapun alasan ditemukanya data outlier karena untuk setiap nasabah memiliki banyak latar belakang dan kesanggupan pembayaran untuk pelunasan yang berbeda-beda\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Melihat Status Pembayaran** \n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Menampilkan diagram batang\n", "sns.countplot(x='default_payment_next_month', data=df)\n", "\n", "# Menambahkan label sumbu dan judul\n", "plt.xlabel('Status Pembayaran')\n", "plt.ylabel('Count')\n", "plt.title('Kemungkinan Nasabah Mengalami Kegagalan Pembayaran')\n", "\n", "# Menampilkan plot\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Setelah dilakukan visualisasi pada kolom status pembayaran, ditemukan lebih banyak nasabah yang memiliki kemungkinan untuk gagal membayar dibandingkan berhasil membayar tagihan. Data ini akan menjadi pertimbangan untuk pemodelan nantinya \n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ***Feature Engineering***\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Memisahkan nilai X dan y** \n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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limit_balancesexeducation_levelmarital_statusagepay_1pay_2pay_3pay_4pay_5pay_6bill_amt_1bill_amt_2bill_amt_3bill_amt_4bill_amt_5bill_amt_6pay_amt_1pay_amt_2pay_amt_3pay_amt_4pay_amt_5pay_amt_6
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120000014149000000492214959950942501465023548984168921642500348025003000
\n", "
" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_1 pay_2 \\\n", "0 80000 1 4 1 54 0 0 \n", "1 200000 1 4 1 49 0 0 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 bill_amt_4 \\\n", "0 0 0 0 0 61454 61808 62290 29296 \n", "1 0 0 0 0 49221 49599 50942 50146 \n", "\n", " bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 pay_amt_4 \\\n", "0 26210 17643 2545 2208 1336 2232 \n", "1 50235 48984 1689 2164 2500 3480 \n", "\n", " pay_amt_5 pay_amt_6 \n", "0 542 348 \n", "1 2500 3000 " ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Memisahkan `X` and `y`\n", "\n", "X = df.drop(['default_payment_next_month'], axis=1)\n", "y = df['default_payment_next_month']\n", "X.head(2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Dilakukan pemisahan pada dataframe untuk mendapatkan nilai *feature* (X) dan target / label (y) yang pada langkah berikutnya akan dilakukan split data \n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ***Splitting* Dataset**\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_Train size : (2223, 23)\n", "X_Test size : (742, 23)\n", "y_Train size : (2223,)\n", "y_Test size : (742,)\n" ] } ], "source": [ "# Splitting Dataset\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)\n", "\n", "print('X_Train size : ', X_train.shape)\n", "print('X_Test size : ', X_test.shape)\n", "print('y_Train size : ', y_train.shape)\n", "print('y_Test size : ', y_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "*Splitting* dataset telah dilakukan dengan proporsi data train / test = 75 / 25 (dalam satuan persen). Adapun digunakan random state yang digunakan bernilai 42\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### **Mendapatkan Nilai Kategorik dan Numerik** \n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Numerical Columns: ['limit_balance', 'sex', 'education_level', 'marital_status', 'age', 'pay_1', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6', 'bill_amt_1', 'bill_amt_2', 'bill_amt_3', 'bill_amt_4', 'bill_amt_5', 'bill_amt_6', 'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', 'pay_amt_6']\n", "Categorical Columns: []\n" ] } ], "source": [ "# Mendapat nilai kategorik dan nilai numerik\n", "\n", "cat_columns = X_train.select_dtypes(include=['object']).columns.tolist()\n", "num_columns = X_train.select_dtypes(include = np.number).columns.tolist()\n", "\n", "print('Numerical Columns: ', num_columns)\n", "print('Categorical Columns: ', cat_columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Dilakukan pemisahan antara kolom kategori dan kolom numerik pada data X_train untuk setiap kolomnya. Ternyata ditemukan pada kolom numerik ditemukan setiap *feature* dari dataframe. Melakukan pemisahan ini juga erat kaitannya untuk *model inference* \n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ***Outlier Handling***\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### **Menampilkan Persentase Outlier**" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def cek_skewness(data):\n", " skewness_data = pd.DataFrame({'Skewness': data.skew()})\n", " return skewness_data" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "# Melihat Skewness \n", "X_train_skew = cek_skewness(X_train)\n", "X_test_skew = cek_skewness(X_test)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "# Fungsi Menghitung Persentase Outlier\n", "def calculate_outlier_percentage(data):\n", " q1 = np.percentile(data, 25)\n", " q3 = np.percentile(data, 75)\n", " iqr = q3 - q1\n", " lower_bound = q1 - 1.5 * iqr\n", " upper_bound = q3 + 1.5 * iqr\n", " outliers = data[(data < lower_bound) | (data > upper_bound)]\n", " outlier_percentage = (len(outliers) / len(data)) * 100\n", " return \"{:.2f}%\".format(outlier_percentage)\n", "\n", "# Fungsi Melihat Hasil Outlier di Setiap Kolom (Data Dictionary)\n", "def calculate_outlier_percentage_all_columns(data):\n", " outlier_percentage_dict = {}\n", " for column in data.columns:\n", " outlier_percentage = calculate_outlier_percentage(data[column])\n", " outlier_percentage_dict[column] = outlier_percentage\n", " return outlier_percentage_dict" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Train_Outlier_Percentage_BeforeTest_Outlier_Percentage_BeforeX_train Skewness_BeforeX_test Skewness_Before
limit_balance0.31%2.29%0.9733121.086581
sex0.00%0.00%-0.488376-0.306005
education_level1.44%1.48%0.4580990.532583
marital_status0.00%0.00%0.032011-0.014498
age1.08%0.94%0.7548150.825919
pay_19.99%11.32%0.7529411.306659
pay_213.45%16.44%0.8140010.958053
pay_313.27%15.23%0.9097060.985246
pay_411.70%13.21%1.0247851.282378
pay_510.75%10.65%0.9720061.302975
pay_611.29%10.51%0.8973591.219875
bill_amt_17.65%7.28%2.5479602.307875
bill_amt_27.92%7.41%2.5011112.342765
bill_amt_38.28%6.06%2.6353282.391354
bill_amt_47.42%5.53%2.4249992.515232
bill_amt_58.05%7.68%2.4065042.386317
bill_amt_67.87%7.41%2.4432792.404137
pay_amt_19.49%8.76%12.62997810.241498
pay_amt_210.17%7.95%27.1878257.176087
pay_amt_39.13%8.36%7.7955648.591743
pay_amt_49.67%8.09%8.5004568.292223
pay_amt_59.81%8.76%11.57247910.845104
pay_amt_610.21%7.55%10.1836347.240740
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" ], "text/plain": [ " Train_Outlier_Percentage_Before \\\n", "limit_balance 0.31% \n", "sex 0.00% \n", "education_level 1.44% \n", "marital_status 0.00% \n", "age 1.08% \n", "pay_1 9.99% \n", "pay_2 13.45% \n", "pay_3 13.27% \n", "pay_4 11.70% \n", "pay_5 10.75% \n", "pay_6 11.29% \n", "bill_amt_1 7.65% \n", "bill_amt_2 7.92% \n", "bill_amt_3 8.28% \n", "bill_amt_4 7.42% \n", "bill_amt_5 8.05% \n", "bill_amt_6 7.87% \n", "pay_amt_1 9.49% \n", "pay_amt_2 10.17% \n", "pay_amt_3 9.13% \n", "pay_amt_4 9.67% \n", "pay_amt_5 9.81% \n", "pay_amt_6 10.21% \n", "\n", " Test_Outlier_Percentage_Before X_train Skewness_Before \\\n", "limit_balance 2.29% 0.973312 \n", "sex 0.00% -0.488376 \n", "education_level 1.48% 0.458099 \n", "marital_status 0.00% 0.032011 \n", "age 0.94% 0.754815 \n", "pay_1 11.32% 0.752941 \n", "pay_2 16.44% 0.814001 \n", "pay_3 15.23% 0.909706 \n", "pay_4 13.21% 1.024785 \n", "pay_5 10.65% 0.972006 \n", "pay_6 10.51% 0.897359 \n", "bill_amt_1 7.28% 2.547960 \n", "bill_amt_2 7.41% 2.501111 \n", "bill_amt_3 6.06% 2.635328 \n", "bill_amt_4 5.53% 2.424999 \n", "bill_amt_5 7.68% 2.406504 \n", "bill_amt_6 7.41% 2.443279 \n", "pay_amt_1 8.76% 12.629978 \n", "pay_amt_2 7.95% 27.187825 \n", "pay_amt_3 8.36% 7.795564 \n", "pay_amt_4 8.09% 8.500456 \n", "pay_amt_5 8.76% 11.572479 \n", "pay_amt_6 7.55% 10.183634 \n", "\n", " X_test Skewness_Before \n", "limit_balance 1.086581 \n", "sex -0.306005 \n", "education_level 0.532583 \n", "marital_status -0.014498 \n", "age 0.825919 \n", "pay_1 1.306659 \n", "pay_2 0.958053 \n", "pay_3 0.985246 \n", "pay_4 1.282378 \n", "pay_5 1.302975 \n", "pay_6 1.219875 \n", "bill_amt_1 2.307875 \n", "bill_amt_2 2.342765 \n", "bill_amt_3 2.391354 \n", "bill_amt_4 2.515232 \n", "bill_amt_5 2.386317 \n", "bill_amt_6 2.404137 \n", "pay_amt_1 10.241498 \n", "pay_amt_2 7.176087 \n", "pay_amt_3 8.591743 \n", "pay_amt_4 8.292223 \n", "pay_amt_5 10.845104 \n", "pay_amt_6 7.240740 " ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Persentase Outlier\n", "X_train_numerik_persentase = calculate_outlier_percentage_all_columns(X_train)\n", "X_test_numerik_persentase = calculate_outlier_percentage_all_columns(X_test)\n", "\n", "# Membuat DataFrame dari hasil perhitungan\n", "X_train_numerik_df = pd.DataFrame.from_dict(X_train_numerik_persentase, orient='index', columns=['Train_Outlier_Percentage_Before'])\n", "X_test_numerik_df = pd.DataFrame.from_dict(X_test_numerik_persentase, orient='index', columns=['Test_Outlier_Percentage_Before'])\n", "X_train_numerik_skew = X_train_skew.rename(columns={'Skewness': 'X_train Skewness_Before'})\n", "X_test_numerik_skew = X_test_skew.rename(columns={'Skewness': 'X_test Skewness_Before'})\n", "\n", "# Menggabungkan kedua DataFrame\n", "merged_df = pd.concat([X_train_numerik_df, X_test_numerik_df, X_train_numerik_skew, X_test_numerik_skew], axis=1)\n", "\n", "# Menampilkan DataFrame hasil penggabungan\n", "merged_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Setelah dilihat nilai persentase pada outlier `X_train_numerik` dengan `X_test_numerik` ditemukan bahwa nilainya berbeda namun tidak jauh berbeda. Namun masih banyak kolom yang memiliki nilai outlier lebih besar dari 5 % dan jumlah skewness tidak berada di antara 0,5 dan 1 atau -1 dan 0,5 . Hal ini akan diatasi dengan menggunakan metode *capping / sensoring*\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ***Capping/Sensoring* Data**" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "def winsoriser(df):\n", " # Inisialisasi Winsorizer untuk kolom saat ini\n", " winsorizer = Winsorizer(\n", " capping_method='iqr',\n", " tail='both',\n", " fold=1.5\n", " )\n", " winsorizer2 = Winsorizer(\n", " capping_method='iqr',\n", " tail='both',\n", " fold=3\n", " )\n", " for column in df.columns:\n", " # Melakukan censoring pada data train (df)\n", " if (abs(df[column].skew()) <= 1) & (abs(df[column].skew()) >= 0.5):\n", " df[column] = winsorizer.fit_transform(df[[column]])\n", " else:\n", " df[column] = winsorizer2.fit_transform(df[[column]])\n", " return df" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "# Winsorizer Data\n", "X_train_new = winsoriser(X_train)\n", "X_test_new = winsoriser(X_test)" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "# Melihat Skewness \n", "X_train_skew = cek_skewness(X_train_new)\n", "X_test_skew = cek_skewness(X_test_new)" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Train_Outlier_Percentage_AfterTest_Outlier_Percentage_AfterX_train Skewness_AfterX_test Skewness_After
limit_balance0.00%2.29%1.0760961.076096
sex0.00%0.00%-0.306005-0.306005
education_level1.44%0.00%0.3747350.374735
marital_status0.00%0.00%-0.014498-0.014498
age0.00%0.00%0.7292040.729204
pay_10.00%11.32%0.4464820.446482
pay_20.00%0.00%-0.023649-0.023649
pay_30.00%0.00%-0.038643-0.038643
pay_411.70%13.21%0.4903560.490356
pay_50.00%10.65%0.4559030.455903
pay_60.00%10.51%0.4400130.440013
bill_amt_17.65%7.28%1.7203911.720391
bill_amt_27.92%7.41%1.7206841.720684
bill_amt_38.28%6.06%1.8244211.824421
bill_amt_47.42%5.53%1.8138401.813840
bill_amt_58.05%7.68%1.6876781.687678
bill_amt_67.87%7.41%1.6673811.667381
pay_amt_19.49%8.76%1.7021981.702198
pay_amt_210.17%7.95%1.7173671.717367
pay_amt_39.13%8.36%1.8767941.876794
pay_amt_49.67%8.09%1.8619801.861980
pay_amt_59.81%8.76%1.8430911.843091
pay_amt_610.21%7.55%1.9344241.934424
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" ], "text/plain": [ " Train_Outlier_Percentage_After Test_Outlier_Percentage_After \\\n", "limit_balance 0.00% 2.29% \n", "sex 0.00% 0.00% \n", "education_level 1.44% 0.00% \n", "marital_status 0.00% 0.00% \n", "age 0.00% 0.00% \n", "pay_1 0.00% 11.32% \n", "pay_2 0.00% 0.00% \n", "pay_3 0.00% 0.00% \n", "pay_4 11.70% 13.21% \n", "pay_5 0.00% 10.65% \n", "pay_6 0.00% 10.51% \n", "bill_amt_1 7.65% 7.28% \n", "bill_amt_2 7.92% 7.41% \n", "bill_amt_3 8.28% 6.06% \n", "bill_amt_4 7.42% 5.53% \n", "bill_amt_5 8.05% 7.68% \n", "bill_amt_6 7.87% 7.41% \n", "pay_amt_1 9.49% 8.76% \n", "pay_amt_2 10.17% 7.95% \n", "pay_amt_3 9.13% 8.36% \n", "pay_amt_4 9.67% 8.09% \n", "pay_amt_5 9.81% 8.76% \n", "pay_amt_6 10.21% 7.55% \n", "\n", " X_train Skewness_After X_test Skewness_After \n", "limit_balance 1.076096 1.076096 \n", "sex -0.306005 -0.306005 \n", "education_level 0.374735 0.374735 \n", "marital_status -0.014498 -0.014498 \n", "age 0.729204 0.729204 \n", "pay_1 0.446482 0.446482 \n", "pay_2 -0.023649 -0.023649 \n", "pay_3 -0.038643 -0.038643 \n", "pay_4 0.490356 0.490356 \n", "pay_5 0.455903 0.455903 \n", "pay_6 0.440013 0.440013 \n", "bill_amt_1 1.720391 1.720391 \n", "bill_amt_2 1.720684 1.720684 \n", "bill_amt_3 1.824421 1.824421 \n", "bill_amt_4 1.813840 1.813840 \n", "bill_amt_5 1.687678 1.687678 \n", "bill_amt_6 1.667381 1.667381 \n", "pay_amt_1 1.702198 1.702198 \n", "pay_amt_2 1.717367 1.717367 \n", "pay_amt_3 1.876794 1.876794 \n", "pay_amt_4 1.861980 1.861980 \n", "pay_amt_5 1.843091 1.843091 \n", "pay_amt_6 1.934424 1.934424 " ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Persentase Outlier\n", "X_train_numerik_winsorizer_persentase = calculate_outlier_percentage_all_columns(X_train_new)\n", "X_test_numerik_winsorizer_persentase = calculate_outlier_percentage_all_columns(X_test_new)\n", "\n", "# Membuat DataFrame dari hasil perhitungan\n", "X_train_numerik_winsorizer_df = pd.DataFrame.from_dict(X_train_numerik_winsorizer_persentase, orient='index', columns=['Train_Outlier_Percentage_After'])\n", "X_test_numerik_winsorizer_df = pd.DataFrame.from_dict(X_test_numerik_winsorizer_persentase, orient='index', columns=['Test_Outlier_Percentage_After'])\n", "X_train_numerik_winsorizer_skew = X_test_skew.rename(columns={'Skewness': 'X_train Skewness_After'})\n", "X_test_numerik_winsorizer_skew = X_test_skew.rename(columns={'Skewness': 'X_test Skewness_After'})\n", "\n", "# Menggabungkan kedua DataFrame\n", "merged_df1 = pd.concat([X_train_numerik_winsorizer_df, X_test_numerik_winsorizer_df, X_train_numerik_winsorizer_skew, X_test_numerik_winsorizer_skew], axis=1)\n", "\n", "# Menampilkan DataFrame hasil penggabungan\n", "merged_df1" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Train_Outlier_Percentage_BeforeTest_Outlier_Percentage_BeforeX_train Skewness_BeforeX_test Skewness_BeforeTrain_Outlier_Percentage_AfterTest_Outlier_Percentage_AfterX_train Skewness_AfterX_test Skewness_After
limit_balance0.31%2.29%0.9733121.0865810.00%2.29%1.0760961.076096
sex0.00%0.00%-0.488376-0.3060050.00%0.00%-0.306005-0.306005
education_level1.44%1.48%0.4580990.5325831.44%0.00%0.3747350.374735
marital_status0.00%0.00%0.032011-0.0144980.00%0.00%-0.014498-0.014498
age1.08%0.94%0.7548150.8259190.00%0.00%0.7292040.729204
pay_19.99%11.32%0.7529411.3066590.00%11.32%0.4464820.446482
pay_213.45%16.44%0.8140010.9580530.00%0.00%-0.023649-0.023649
pay_313.27%15.23%0.9097060.9852460.00%0.00%-0.038643-0.038643
pay_411.70%13.21%1.0247851.28237811.70%13.21%0.4903560.490356
pay_510.75%10.65%0.9720061.3029750.00%10.65%0.4559030.455903
pay_611.29%10.51%0.8973591.2198750.00%10.51%0.4400130.440013
bill_amt_17.65%7.28%2.5479602.3078757.65%7.28%1.7203911.720391
bill_amt_27.92%7.41%2.5011112.3427657.92%7.41%1.7206841.720684
bill_amt_38.28%6.06%2.6353282.3913548.28%6.06%1.8244211.824421
bill_amt_47.42%5.53%2.4249992.5152327.42%5.53%1.8138401.813840
bill_amt_58.05%7.68%2.4065042.3863178.05%7.68%1.6876781.687678
bill_amt_67.87%7.41%2.4432792.4041377.87%7.41%1.6673811.667381
pay_amt_19.49%8.76%12.62997810.2414989.49%8.76%1.7021981.702198
pay_amt_210.17%7.95%27.1878257.17608710.17%7.95%1.7173671.717367
pay_amt_39.13%8.36%7.7955648.5917439.13%8.36%1.8767941.876794
pay_amt_49.67%8.09%8.5004568.2922239.67%8.09%1.8619801.861980
pay_amt_59.81%8.76%11.57247910.8451049.81%8.76%1.8430911.843091
pay_amt_610.21%7.55%10.1836347.24074010.21%7.55%1.9344241.934424
\n", "
" ], "text/plain": [ " Train_Outlier_Percentage_Before \\\n", "limit_balance 0.31% \n", "sex 0.00% \n", "education_level 1.44% \n", "marital_status 0.00% \n", "age 1.08% \n", "pay_1 9.99% \n", "pay_2 13.45% \n", "pay_3 13.27% \n", "pay_4 11.70% \n", "pay_5 10.75% \n", "pay_6 11.29% \n", "bill_amt_1 7.65% \n", "bill_amt_2 7.92% \n", "bill_amt_3 8.28% \n", "bill_amt_4 7.42% \n", "bill_amt_5 8.05% \n", "bill_amt_6 7.87% \n", "pay_amt_1 9.49% \n", "pay_amt_2 10.17% \n", "pay_amt_3 9.13% \n", "pay_amt_4 9.67% \n", "pay_amt_5 9.81% \n", "pay_amt_6 10.21% \n", "\n", " Test_Outlier_Percentage_Before X_train Skewness_Before \\\n", "limit_balance 2.29% 0.973312 \n", "sex 0.00% -0.488376 \n", "education_level 1.48% 0.458099 \n", "marital_status 0.00% 0.032011 \n", "age 0.94% 0.754815 \n", "pay_1 11.32% 0.752941 \n", "pay_2 16.44% 0.814001 \n", "pay_3 15.23% 0.909706 \n", "pay_4 13.21% 1.024785 \n", "pay_5 10.65% 0.972006 \n", "pay_6 10.51% 0.897359 \n", "bill_amt_1 7.28% 2.547960 \n", "bill_amt_2 7.41% 2.501111 \n", "bill_amt_3 6.06% 2.635328 \n", "bill_amt_4 5.53% 2.424999 \n", "bill_amt_5 7.68% 2.406504 \n", "bill_amt_6 7.41% 2.443279 \n", "pay_amt_1 8.76% 12.629978 \n", "pay_amt_2 7.95% 27.187825 \n", "pay_amt_3 8.36% 7.795564 \n", "pay_amt_4 8.09% 8.500456 \n", "pay_amt_5 8.76% 11.572479 \n", "pay_amt_6 7.55% 10.183634 \n", "\n", " X_test Skewness_Before Train_Outlier_Percentage_After \\\n", "limit_balance 1.086581 0.00% \n", "sex -0.306005 0.00% \n", "education_level 0.532583 1.44% \n", "marital_status -0.014498 0.00% \n", "age 0.825919 0.00% \n", "pay_1 1.306659 0.00% \n", "pay_2 0.958053 0.00% \n", "pay_3 0.985246 0.00% \n", "pay_4 1.282378 11.70% \n", "pay_5 1.302975 0.00% \n", "pay_6 1.219875 0.00% \n", "bill_amt_1 2.307875 7.65% \n", "bill_amt_2 2.342765 7.92% \n", "bill_amt_3 2.391354 8.28% \n", "bill_amt_4 2.515232 7.42% \n", "bill_amt_5 2.386317 8.05% \n", "bill_amt_6 2.404137 7.87% \n", "pay_amt_1 10.241498 9.49% \n", "pay_amt_2 7.176087 10.17% \n", "pay_amt_3 8.591743 9.13% \n", "pay_amt_4 8.292223 9.67% \n", "pay_amt_5 10.845104 9.81% \n", "pay_amt_6 7.240740 10.21% \n", "\n", " Test_Outlier_Percentage_After X_train Skewness_After \\\n", "limit_balance 2.29% 1.076096 \n", "sex 0.00% -0.306005 \n", "education_level 0.00% 0.374735 \n", "marital_status 0.00% -0.014498 \n", "age 0.00% 0.729204 \n", "pay_1 11.32% 0.446482 \n", "pay_2 0.00% -0.023649 \n", "pay_3 0.00% -0.038643 \n", "pay_4 13.21% 0.490356 \n", "pay_5 10.65% 0.455903 \n", "pay_6 10.51% 0.440013 \n", "bill_amt_1 7.28% 1.720391 \n", "bill_amt_2 7.41% 1.720684 \n", "bill_amt_3 6.06% 1.824421 \n", "bill_amt_4 5.53% 1.813840 \n", "bill_amt_5 7.68% 1.687678 \n", "bill_amt_6 7.41% 1.667381 \n", "pay_amt_1 8.76% 1.702198 \n", "pay_amt_2 7.95% 1.717367 \n", "pay_amt_3 8.36% 1.876794 \n", "pay_amt_4 8.09% 1.861980 \n", "pay_amt_5 8.76% 1.843091 \n", "pay_amt_6 7.55% 1.934424 \n", "\n", " X_test Skewness_After \n", "limit_balance 1.076096 \n", "sex -0.306005 \n", "education_level 0.374735 \n", "marital_status -0.014498 \n", "age 0.729204 \n", "pay_1 0.446482 \n", "pay_2 -0.023649 \n", "pay_3 -0.038643 \n", "pay_4 0.490356 \n", "pay_5 0.455903 \n", "pay_6 0.440013 \n", "bill_amt_1 1.720391 \n", "bill_amt_2 1.720684 \n", "bill_amt_3 1.824421 \n", "bill_amt_4 1.813840 \n", "bill_amt_5 1.687678 \n", "bill_amt_6 1.667381 \n", "pay_amt_1 1.702198 \n", "pay_amt_2 1.717367 \n", "pay_amt_3 1.876794 \n", "pay_amt_4 1.861980 \n", "pay_amt_5 1.843091 \n", "pay_amt_6 1.934424 " ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Merge DataFrame \n", "gabungan = pd.concat([merged_df, merged_df1], axis=1)\n", "gabungan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Setelah dilakukan *capping / sensoring* pada data `X_train_numerik` dan `X_test_numerik`, telah diperoleh hasil outlier yang telah berkurang walau tidak secara keseluruhan. Namun, pada skew telah berhasil menurunkan nilai hingga mencapai sekitar 2 sehingga bisa dikatakan data ini sudah bisa dikatakan terdistribusi normal. \n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ***Scalling***" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "# Menentukan Jenis Scalling \n", "scaler = RobustScaler()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Pada permodelan ini akan digunakan scalling berupa Robust Scaler. Alasan pemilihan *scale* ini adalah karena ketahanannya terhadap outlier. Hal ini erat kaitannya dengan nilai data yang masih memiliku outlier. *Scale* ini nantinya akan digunakan pada model dengan bantuan *pipeline*\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ***Model Definition***\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ***Hyperparameter Tuning***\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ***Logistic Regression***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "# Pipeline Logistic Regression\n", "pipe_hyperparameter_logistic_regression = Pipeline([\n", " ('scaler', scaler),\n", " ('logistic_classification', LogisticRegression(solver='liblinear', max_iter=1000))\n", "])\n", "\n", "# Tuning Paramameter\n", "param_grid = {\n", " 'logistic_classification__C': [0.001, 0.01, 0.1, 1, 10, 100], \n", " 'logistic_classification__penalty': ['l1', 'l2']\n", "}\n", "\n", "# Model GridSearchCV\n", "grid_logisctic_regression = GridSearchCV(pipe_hyperparameter_logistic_regression, param_grid=param_grid, cv=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Model *Logistic Regression* yang telah dibentuk sebelumnya akan dilakukan percobaan dengan menggunakan *hyperparameter tuning*, adapun param_grid atau parameter yang diinginkan optimalisasinya adalah nilai c dan nilai pinalty seperti Lasso dan Ridge\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ***K-Nearest Neighbors (KNN)***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "# Pipeline KNN classifier\n", "pipe_hyperparameter_KNN = Pipeline([\n", " ('scaler', scaler),\n", " ('knn', KNeighborsClassifier())\n", "])\n", "\n", "# Tuning Paramameter\n", "param_grid = {\n", " 'knn__n_neighbors': [3, 5, 7, 9],\n", " 'knn__weights': ['uniform', 'distance'],\n", " 'knn__metric': ['euclidean', 'manhattan']\n", "}\n", "\n", "# Model GridSearchCV\n", "grid_KNN = GridSearchCV(pipe_hyperparameter_KNN, param_grid=param_grid, cv=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Model *KNN classifier* yang telah dibentuk sebelumnya akan dilakukan percobaan dengan menggunakan *hyperparameter tuning*, adapun param_grid atau parameter yang diinginkan optimalisasinya adalah nilai n_neighbors, weights dan metric seperti euclidean atau manhattan\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### ***Support Vector Machine (SVM)***\n", "\n", "---\n" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "# Pipeline SVC classifier\n", "pipe_hyperparameter_SVC = Pipeline([\n", " ('scaler', scaler),\n", " ('svc', SVC())\n", "])\n", "\n", "# Tuning Paramameter\n", "param_grid = {\n", " 'svc__C': [0.1, 1, 10, 100],\n", " 'svc__gamma': [0.001, 0.01, 0.1, 1, 10, 100]\n", "}\n", "\n", "# Model GridSearchCV\n", "grid_SVC = GridSearchCV(pipe_hyperparameter_SVC, param_grid=param_grid, cv=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Model *SVC classifier* yang telah dibentuk sebelumnya akan dilakukan percobaan dengan menggunakan *hyperparameter tuning*, adapun param_grid atau parameter yang diinginkan optimalisasinya adalah nilai C dan Gamma \n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ***Model Training***\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ***Logistic Regression***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scaler', RobustScaler()),\n",
       "                                       ('logistic_classification',\n",
       "                                        LogisticRegression(max_iter=1000,\n",
       "                                                           solver='liblinear'))]),\n",
       "             param_grid={'logistic_classification__C': [0.001, 0.01, 0.1, 1, 10,\n",
       "                                                        100],\n",
       "                         'logistic_classification__penalty': ['l1', 'l2']})
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" ], "text/plain": [ "GridSearchCV(cv=5,\n", " estimator=Pipeline(steps=[('scaler', RobustScaler()),\n", " ('logistic_classification',\n", " LogisticRegression(max_iter=1000,\n", " solver='liblinear'))]),\n", " param_grid={'logistic_classification__C': [0.001, 0.01, 0.1, 1, 10,\n", " 100],\n", " 'logistic_classification__penalty': ['l1', 'l2']})" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Fit pada Logistic Regression\n", "grid_logisctic_regression.fit(X_train_new, y_train)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'logistic_classification__C': 0.1, 'logistic_classification__penalty': 'l1'}\n", "Best Score: 0.81826703107602\n", "Test Accuracy: 0.8342318059299192\n" ] } ], "source": [ "# Mendapatakan Parameter Terbaik dan Score Terbaik\n", "best_params = grid_logisctic_regression.best_params_\n", "best_score = grid_logisctic_regression.best_score_\n", "print(\"Best Parameters:\", best_params)\n", "print(\"Best Score:\", best_score)\n", "\n", "# Evaluasi Model dengan Paramter Terbaik \n", "best_model = grid_logisctic_regression.best_estimator_\n", "y_pred = best_model.predict(X_test_new)\n", "accuracy = accuracy_score(y_test, y_pred)\n", "print(\"Test Accuracy:\", accuracy)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficient : [[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 -3.52617975e-03\n", " 7.53910056e-02 6.55105102e-01 0.00000000e+00 6.20468567e-02\n", " 2.56343587e-01 0.00000000e+00 8.55477698e-02 -2.36895260e-04\n", " 0.00000000e+00 0.00000000e+00 0.00000000e+00 0.00000000e+00\n", " 0.00000000e+00 -4.69822441e-02 -2.07389407e-01 0.00000000e+00\n", " -3.97777192e-02 -1.00373238e-01 -1.02178376e-01]]\n", "Intercept : [-1.14911363]\n" ] } ], "source": [ "# Akses Model dengan Grid Search\n", "best_logistic_regression_model = grid_logisctic_regression.best_estimator_.named_steps['logistic_classification']\n", "\n", "# Mendapatkan Parameter Terbaik\n", "print('Coefficient : ', best_logistic_regression_model.coef_)\n", "print('Intercept : ', best_logistic_regression_model.intercept_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "1. Data pelatihan yang baik diperoleh angka 81,82 % sedangkan untuk akurasi model dengan data uji diperoleh 83,42 %. Nilai pada data ini tidak terlalu jauh berbeda dan bisa dikatakan ini adalah pemodelan yang baik\n", "\n", "2. Untuk koefisien sendiri bisa didapatkan *justifikasi*:\n", " \n", " - Koefisien memiliki nilai non pada beberapa fitur, menunjukkan bahwa fitur-fitur ini mempengaruhi prediksi kelas target.\n", "\n", " - Koefisien yang memiliki nilai positif yang berarti menunjukkan kontribusi positif terhadap kelas target\n", "\n", " - Koefisien yang memiliki nilai negatif yang berarti menunjukkan kontribusi negatif terhadap kelas target\n", "\n", " - Nilai-nilai koefisien yang mendekati nol menunjukkan bahwa fitur-fitur ini memiliki kontribusi yang sangat kecil terhadap prediksi kelas target.\n", "\n", " - Intersep memiliki nilai -1.14917358, menunjukkan nilai prediksi yang diharapkan ketika semua fitur memiliki nilai nol.\n", "\n", "3. *Hyperparameter yang terbaik adalah dengan menggunakan nilai C sebesar 0.1 dan Lasso Regression (L1)\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ***K-Nearest Neighbors (KNN)***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scaler', RobustScaler()),\n",
       "                                       ('knn', KNeighborsClassifier())]),\n",
       "             param_grid={'knn__metric': ['euclidean', 'manhattan'],\n",
       "                         'knn__n_neighbors': [3, 5, 7, 9],\n",
       "                         'knn__weights': ['uniform', 'distance']})
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" ], "text/plain": [ "GridSearchCV(cv=5,\n", " estimator=Pipeline(steps=[('scaler', RobustScaler()),\n", " ('knn', KNeighborsClassifier())]),\n", " param_grid={'knn__metric': ['euclidean', 'manhattan'],\n", " 'knn__n_neighbors': [3, 5, 7, 9],\n", " 'knn__weights': ['uniform', 'distance']})" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_KNN.fit(X_train_new, y_train)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'knn__metric': 'manhattan', 'knn__n_neighbors': 9, 'knn__weights': 'uniform'}\n", "Best Score: 0.8142210750075918\n", "Test Accuracy: 0.8301886792452831\n" ] } ], "source": [ "# Get the best parameters and best score\n", "best_params = grid_KNN.best_params_\n", "best_score = grid_KNN.best_score_\n", "print(\"Best Parameters:\", best_params)\n", "print(\"Best Score:\", best_score)\n", "\n", "# Evaluate the model with best parameters\n", "best_model = grid_KNN.best_estimator_\n", "y_pred = best_model.predict(X_test)\n", "accuracy = accuracy_score(y_test, y_pred)\n", "print(\"Test Accuracy:\", accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "1. Data pelatihan yang baik diperoleh angka 81,42 % sedangkan untuk akurasi model dengan data uji diperoleh 83,01 %. Nilai pada data ini tidak terlalu jauh berbeda dan bisa dikatakan ini adalah pemodelan yang baik\n", "\n", "2. *Hyperparameter* yang terbaik adalah dengan menggunakan nilai N_neighbors sebesar 9 dengan weighs uniform dan metric manhattan\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### ***Support Vector Machine (SVM)***\n", "\n", "---\n" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scaler', RobustScaler()),\n",
       "                                       ('svc', SVC())]),\n",
       "             param_grid={'svc__C': [0.1, 1, 10, 100],\n",
       "                         'svc__gamma': [0.001, 0.01, 0.1, 1, 10, 100]})
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" ], "text/plain": [ "GridSearchCV(cv=5,\n", " estimator=Pipeline(steps=[('scaler', RobustScaler()),\n", " ('svc', SVC())]),\n", " param_grid={'svc__C': [0.1, 1, 10, 100],\n", " 'svc__gamma': [0.001, 0.01, 0.1, 1, 10, 100]})" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_SVC.fit(X_train_new, y_train)" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'svc__C': 10, 'svc__gamma': 0.01}\n", "Best Score: 0.8326632250227755\n", "Test Accuracy: 0.8369272237196765\n" ] } ], "source": [ "# Get the best parameters and best score\n", "best_params = grid_SVC.best_params_\n", "best_score = grid_SVC.best_score_\n", "print(\"Best Parameters:\", best_params)\n", "print(\"Best Score:\", best_score)\n", "\n", "# Evaluate the model with best parameters\n", "best_model = grid_SVC.best_estimator_\n", "y_pred = best_model.predict(X_test)\n", "accuracy = accuracy_score(y_test, y_pred)\n", "print(\"Test Accuracy:\", accuracy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "1. Data pelatihan yang baik diperoleh angka 83,26 % sedangkan untuk akurasi model dengan data uji diperoleh 83,69 %. Nilai pada data ini tidak terlalu jauh berbeda dan bisa dikatakan ini adalah pemodelan yang baik\n", "\n", "2. *Hyperparameter* yang terbaik adalah dengan menggunakan nilai C sebesar 10 dengan gamma sebesar 0.01\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ***Model Evaluation***\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ***Logistic Regression***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "# Memeriksa Kinerja Model\n", "y_pred_train1 = grid_logisctic_regression.predict(X_train_new)\n", "y_pred_test1 = grid_logisctic_regression.predict(X_test_new)" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train\n", " precision recall f1-score support\n", "\n", " 0 0.83 0.98 0.89 1740\n", " 1 0.75 0.25 0.38 483\n", "\n", " accuracy 0.82 2223\n", " macro avg 0.79 0.62 0.64 2223\n", "weighted avg 0.81 0.82 0.78 2223\n", "\n", "\n", "Confusion Matrix : \n", " \n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Menampilkan Classification Report\n", "print('Train')\n", "print(classification_report(y_train, y_pred_train1))\n", "print('')\n", "print('Confusion Matrix : \\n', ConfusionMatrixDisplay.from_estimator(grid_logisctic_regression, X_train_new, y_train, cmap='Reds'))" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "F1 Score - All - Cross Validation : [0.359447 0.39285714 0.4109589 ]\n", "F1 Score - Mean - Cross Validation : 0.3877543505250089\n", "F1 Score - Std - Cross Validation : 0.021336944716407097\n", "F1 Score - Range of Test-Set : 0.3664174058086018 - 0.40909129524141596\n" ] } ], "source": [ "# Menampilkan Cross Validation \n", "f1_train_cross_val = cross_val_score(grid_logisctic_regression,\n", " X_train_new,\n", " y_train,\n", " cv=3,\n", " scoring=\"f1\")\n", "\n", "print('F1 Score - All - Cross Validation : ', f1_train_cross_val)\n", "print('F1 Score - Mean - Cross Validation : ', f1_train_cross_val.mean())\n", "print('F1 Score - Std - Cross Validation : ', f1_train_cross_val.std())\n", "print('F1 Score - Range of Test-Set : ', (f1_train_cross_val.mean()-f1_train_cross_val.std()) , '-', (f1_train_cross_val.mean()+f1_train_cross_val.std()))" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test\n", " precision recall f1-score support\n", "\n", " 0 0.85 0.95 0.90 590\n", " 1 0.67 0.37 0.48 152\n", "\n", " accuracy 0.83 742\n", " macro avg 0.76 0.66 0.69 742\n", "weighted avg 0.82 0.83 0.81 742\n", "\n", "\n", "Confusion Matrix : \n", " \n" ] }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Menampilkan Classification Report\n", "print('Test')\n", "print(classification_report(y_test, y_pred_test1))\n", "print('')\n", "print('Confusion Matrix : \\n', ConfusionMatrixDisplay.from_estimator(grid_logisctic_regression, X_test_new, y_test, cmap='Reds'))" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Logistic Regression Hyperparameter
train - precision0.754601
train - recall0.254658
train - accuracy0.820063
train - f1_score0.380805
test - precision0.674699
test - recall0.368421
test - accuracy_score0.834232
test - f1_score0.476596
\n", "
" ], "text/plain": [ " Logistic Regression Hyperparameter\n", "train - precision 0.754601\n", "train - recall 0.254658\n", "train - accuracy 0.820063\n", "train - f1_score 0.380805\n", "test - precision 0.674699\n", "test - recall 0.368421\n", "test - accuracy_score 0.834232\n", "test - f1_score 0.476596" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Hasil Klasifikasi\n", "all_reports = {}\n", "def performance_report(all_reports, y_train, y_pred_train1, y_test, y_pred_test1, name):\n", " score_reports = {\n", " 'train - precision' : precision_score(y_train, y_pred_train1),\n", " 'train - recall' : recall_score(y_train, y_pred_train1),\n", " 'train - accuracy' : accuracy_score(y_train, y_pred_train1),\n", " 'train - f1_score' : f1_score(y_train, y_pred_train1),\n", " 'test - precision' : precision_score(y_test, y_pred_test1),\n", " 'test - recall' : recall_score(y_test, y_pred_test1),\n", " 'test - accuracy_score' : accuracy_score(y_test, y_pred_test1),\n", " 'test - f1_score' : f1_score(y_test, y_pred_test1),\n", " }\n", " all_reports[name] = score_reports\n", " return all_reports\n", "\n", "all_reports = performance_report(all_reports, y_train, y_pred_train1, y_test, y_pred_test1, 'Logistic Regression Hyperparameter')\n", "logistic_regression_reports = pd.DataFrame(all_reports)\n", "logistic_regression_reports" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "1. Pada data *training* dapat dilihat seberapa baik model bekerja dalam memprediksi kelas 1 (\"gagal membayar\") dari data uji yang ada. Dalam contoh ini, meskipun akurasi secara keseluruhan tinggi, recall untuk kelas 1 (gagal membayar) relatif rendah, menunjukkan bahwa model mungkin perlu diperbaiki untuk lebih baik dalam menangkap kasus-kasus gagal membayar.\n", "\n", "2. Diperoleh nilai Std - Cross Validation: 0.0213. Sehingga dapat dikatakan model ini memiki performa stabil\n", "\n", "3. Dari laporoan pada data *test*, dapat dilihat bahwa model memiliki precision yang baik untuk memprediksi kelas 1 (gagal membayar), tetapi recallnya agak rendah, yang berarti model mungkin perlu diperbaiki untuk lebih baik dalam menangkap kasus-kasus gagal membayar yang sebenarnya.\n", "\n", "4. Pemodelan dengan menggunakan metode Logistic Regression bisa dikatakan baik walau perlu ditingkatkan lagi performa pemodelnnya\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ***K-Nearest Neighbors (KNN)***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# Memeriksa Kinerja Model\n", "y_pred_train2 = grid_KNN.predict(X_train_new)\n", "y_pred_test2 = grid_KNN.predict(X_test_new)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train\n", " precision recall f1-score support\n", "\n", " 0 0.84 0.96 0.90 1740\n", " 1 0.73 0.35 0.47 483\n", "\n", " accuracy 0.83 2223\n", " macro avg 0.79 0.66 0.69 2223\n", "weighted avg 0.82 0.83 0.81 2223\n", "\n", "\n", "Confusion Matrix : \n", " \n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Menampilkan Classification Report\n", "print('Train')\n", "print(classification_report(y_train, y_pred_train2))\n", "print('')\n", "print('Confusion Matrix : \\n', ConfusionMatrixDisplay.from_estimator(grid_KNN, X_train_new, y_train, cmap='Reds'))" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "F1 Score - All - Cross Validation : [0.37974684 0.47148289 0.41255605]\n", "F1 Score - Mean - Cross Validation : 0.42126192632951254\n", "F1 Score - Std - Cross Validation : 0.0379536566216756\n", "F1 Score - Range of Test-Set : 0.38330826970783693 - 0.45921558295118814\n" ] } ], "source": [ "# Menampilkan Cross Validation \n", "f1_train_cross_val = cross_val_score(grid_KNN,\n", " X_train_new,\n", " y_train,\n", " cv=3,\n", " scoring=\"f1\")\n", "\n", "print('F1 Score - All - Cross Validation : ', f1_train_cross_val)\n", "print('F1 Score - Mean - Cross Validation : ', f1_train_cross_val.mean())\n", "print('F1 Score - Std - Cross Validation : ', f1_train_cross_val.std())\n", "print('F1 Score - Range of Test-Set : ', (f1_train_cross_val.mean()-f1_train_cross_val.std()) , '-', (f1_train_cross_val.mean()+f1_train_cross_val.std()))" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test\n", " precision recall f1-score support\n", "\n", " 0 0.85 0.95 0.90 590\n", " 1 0.66 0.35 0.46 152\n", "\n", " accuracy 0.83 742\n", " macro avg 0.76 0.65 0.68 742\n", "weighted avg 0.81 0.83 0.81 742\n", "\n", "\n", "Confusion Matrix : \n", " \n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Menampilkan Classification Report\n", "print('Test')\n", "print(classification_report(y_test, y_pred_test2))\n", "print('')\n", "print('Confusion Matrix : \\n', ConfusionMatrixDisplay.from_estimator(grid_KNN, X_test_new, y_test, cmap='Reds'))" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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KNN Hyperparameter
train - precision0.729614
train - recall0.351967
train - accuracy0.830859
train - f1_score0.474860
test - precision0.662500
test - recall0.348684
test - accuracy_score0.830189
test - f1_score0.456897
\n", "
" ], "text/plain": [ " KNN Hyperparameter\n", "train - precision 0.729614\n", "train - recall 0.351967\n", "train - accuracy 0.830859\n", "train - f1_score 0.474860\n", "test - precision 0.662500\n", "test - recall 0.348684\n", "test - accuracy_score 0.830189\n", "test - f1_score 0.456897" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Hasil Klasifikasi\n", "all_reports = {}\n", "def performance_report(all_reports, y_train, y_pred_train2, y_test, y_pred_test2, name):\n", " score_reports = {\n", " 'train - precision' : precision_score(y_train, y_pred_train2),\n", " 'train - recall' : recall_score(y_train, y_pred_train2),\n", " 'train - accuracy' : accuracy_score(y_train, y_pred_train2),\n", " 'train - f1_score' : f1_score(y_train, y_pred_train2),\n", " 'test - precision' : precision_score(y_test, y_pred_test2),\n", " 'test - recall' : recall_score(y_test, y_pred_test2),\n", " 'test - accuracy_score' : accuracy_score(y_test, y_pred_test2),\n", " 'test - f1_score' : f1_score(y_test, y_pred_test2),\n", " }\n", " all_reports[name] = score_reports\n", " return all_reports\n", "\n", "all_reports = performance_report(all_reports, y_train, y_pred_train2, y_test, y_pred_test2, 'KNN Hyperparameter')\n", "KNN_reports = pd.DataFrame(all_reports)\n", "KNN_reports" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Menampilkan Nilai F1 Score dari k = 1 to k = 100\n", "max_k = 100\n", "\n", "train_acc = []\n", "test_acc = []\n", "\n", "for loop in range (1, max_k+1):\n", " knn = KNeighborsClassifier(n_neighbors = loop)\n", " knn.fit(X_train_new, y_train)\n", "\n", " y_pred_train_knn = knn.predict(X_train_new)\n", " y_pred_test_knn = knn.predict(X_test_new)\n", "\n", " train_acc.append(f1_score(y_train, y_pred_train_knn, average='weighted'))\n", " test_acc.append(f1_score(y_test, y_pred_test_knn, average='weighted'))\n", "\n", "# Visualisasi Akurasi Nilai K\n", "plt.figure(figsize=(20,5))\n", "plt.title('Effect of Value k on Accuracy - Euclidean Distance')\n", "plt.plot(range(1, max_k+1), train_acc, label='Train Accuracy')\n", "plt.plot(range(1, max_k+1), test_acc, label='Test accuracy')\n", "\n", "plt.legend()\n", "plt.xlabel('Number of k')\n", "plt.ylabel('F1 Score')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "1. Pada data *training* dapat dilihat seberapa baik model memiliki precision yang baik untuk kelas 0 (tidak gagal membayar), namun recall yang rendah untuk kelas 1 (gagal membayar), yang berarti model mungkin perlu ditingkatkan kemampuannya dalam menangkap kasus-kasus gagal membayar yang sebenarnya.\n", "\n", "2. Diperoleh nilai Std - Cross Validation: 0.038. Sehingga dapat dikatakan model ini memiki performa stabil\n", "\n", "3. Dari laporoan pada data *test*, dapat dilihat bahwa model memiliki precision yang baik untuk kelas 0 (tidak gagal membayar), namun recall yang rendah untuk kelas 1 (gagal membayar), yang berarti model mungkin perlu ditingkatkan kemampuannya dalam menangkap kasus-kasus gagal membayar yang sebenarnya.\n", "\n", "4. Dilakukan uji coba pemodelan untuk mencari nilai *Hyperparameter* untuk nilai K. Adapun nilai K yang dicari titik optimalnya akan dilakukan uji coba dari nilai 1 hingga 100 sehingg ditemukan angka 9 seperti yang ditemukan sebelumnya\n", "\n", "5. Pemodelan dengan menggunakan metode *K-Nearest Neighbors* (KNN) bisa dikatakan baik walau perlu ditingkatkan lagi performa pemodelnnya\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### ***Support Vector Machine (SVM)***\n", "\n", "---\n" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "# Memeriksa Kinerja Model\n", "y_pred_train3 = grid_SVC.predict(X_train_new)\n", "y_pred_test3 = grid_SVC.predict(X_test_new)" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train\n", " precision recall f1-score support\n", "\n", " 0 0.85 0.97 0.91 1740\n", " 1 0.76 0.39 0.52 483\n", "\n", " accuracy 0.84 2223\n", " macro avg 0.81 0.68 0.71 2223\n", "weighted avg 0.83 0.84 0.82 2223\n", "\n", "\n", "Confusion Matrix : \n", " \n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Menampilkan Classification Report\n", "print('Train')\n", "print(classification_report(y_train, y_pred_train3))\n", "print('')\n", "print('Confusion Matrix : \\n', ConfusionMatrixDisplay.from_estimator(grid_KNN, X_train_new, y_train, cmap='Reds'))" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "F1 Score - All - Cross Validation : [0.44255319 0.47736626 0.49180328]\n", "F1 Score - Mean - Cross Validation : 0.47057424177397306\n", "F1 Score - Std - Cross Validation : 0.020671902936040043\n", "F1 Score - Range of Test-Set : 0.449902338837933 - 0.4912461447100131\n" ] } ], "source": [ "# Menampilkan Cross Validation \n", "f1_train_cross_val = cross_val_score(grid_SVC,\n", " X_train_new,\n", " y_train,\n", " cv=3,\n", " scoring=\"f1\")\n", "\n", "print('F1 Score - All - Cross Validation : ', f1_train_cross_val)\n", "print('F1 Score - Mean - Cross Validation : ', f1_train_cross_val.mean())\n", "print('F1 Score - Std - Cross Validation : ', f1_train_cross_val.std())\n", "print('F1 Score - Range of Test-Set : ', (f1_train_cross_val.mean()-f1_train_cross_val.std()) , '-', (f1_train_cross_val.mean()+f1_train_cross_val.std()))" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test\n", " precision recall f1-score support\n", "\n", " 0 0.86 0.94 0.90 590\n", " 1 0.66 0.42 0.51 152\n", "\n", " accuracy 0.84 742\n", " macro avg 0.76 0.68 0.71 742\n", "weighted avg 0.82 0.84 0.82 742\n", "\n", "\n", "Confusion Matrix : \n", " \n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Menampilkan Classification Report\n", "print('Test')\n", "print(classification_report(y_test, y_pred_test3))\n", "print('')\n", "print('Confusion Matrix : \\n', ConfusionMatrixDisplay.from_estimator(grid_KNN, X_test_new, y_test, cmap='Reds'))" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SVC Hyperparameter
train - precision0.763052
train - recall0.393375
train - accuracy0.841655
train - f1_score0.519126
test - precision0.659794
test - recall0.421053
test - accuracy_score0.836927
test - f1_score0.514056
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" ], "text/plain": [ " SVC Hyperparameter\n", "train - precision 0.763052\n", "train - recall 0.393375\n", "train - accuracy 0.841655\n", "train - f1_score 0.519126\n", "test - precision 0.659794\n", "test - recall 0.421053\n", "test - accuracy_score 0.836927\n", "test - f1_score 0.514056" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Hasil Klasifikasi\n", "\n", "all_reports = {}\n", "def performance_report(all_reports, y_train, y_pred_train3, y_test, y_pred_test3, name):\n", " score_reports = {\n", " 'train - precision' : precision_score(y_train, y_pred_train3),\n", " 'train - recall' : recall_score(y_train, y_pred_train3),\n", " 'train - accuracy' : accuracy_score(y_train, y_pred_train3),\n", " 'train - f1_score' : f1_score(y_train, y_pred_train3),\n", " 'test - precision' : precision_score(y_test, y_pred_test3),\n", " 'test - recall' : recall_score(y_test, y_pred_test3),\n", " 'test - accuracy_score' : accuracy_score(y_test, y_pred_test3),\n", " 'test - f1_score' : f1_score(y_test, y_pred_test3),\n", " }\n", " all_reports[name] = score_reports\n", " return all_reports\n", "\n", "all_reports = performance_report(all_reports, y_train, y_pred_train3, y_test, y_pred_test3, 'SVC Hyperparameter')\n", "SVC_reports = pd.DataFrame(all_reports)\n", "SVC_reports" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "1. Pada data *training* dapat dilihat seberapa baik model memiliki precision yang baik untuk kelas 0 (tidak gagal membayar), namun recall yang rendah untuk kelas 1 (gagal membayar), yang berarti model mungkin perlu ditingkatkan kemampuannya dalam menangkap kasus-kasus gagal membayar yang sebenarnya.\n", "\n", "2. Diperoleh nilai Std - Cross Validation: 0.020. Sehingga dapat dikatakan model ini memiki performa stabil\n", "\n", "3. Dari laporoan pada data *test*, dapat dilihat bahwa model memiliki precision yang baik untuk kelas 0 (tidak gagal membayar), namun recall yang rendah untuk kelas 1 (gagal membayar), yang berarti model mungkin perlu ditingkatkan kemampuannya dalam menangkap kasus-kasus gagal membayar yang sebenarnya.\n", "\n", "4. Pemodelan dengan menggunakan metode *Support Vector Machine* (SVM) bisa dikatakan baik walau perlu ditingkatkan lagi performa pemodelnnya\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### ***Model Comparison***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "# Melakukan Fit pada RobustScaler\n", "scaler.fit(X_train_new)\n", "\n", "# Mendefenisikan Model dan Metric\n", "models = {\n", " 'Logistic Regression':grid_logisctic_regression,\n", " 'K-Nearest Neighbors (KNN)': grid_KNN,\n", " 'Support Vector Machine (SVM)': grid_SVC,\n", "}\n", "\n", "metrics = {\n", " 'Accuracy': accuracy_score,\n", " 'F1-Score': f1_score,\n", "}\n", "\n", "# Melakukan Fit Setiap Model Training Data\n", "for model_name, model in models.items():\n", " model.fit(X_train_new, y_train)\n", "\n", "# Membuat Dataframe Untuk Hasil\n", "df_model = pd.DataFrame(columns=models.keys(), index=['Accuracy', 'F1-Score'])\n", "\n", "# Evaluasi Model Setiap Metriks\n", "for metric in metrics.keys():\n", " for model_name, model in models.items():\n", " df_model.loc[metric, model_name] = metrics[metric](y_test, model.predict(X_test_new))\n" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Logistic RegressionK-Nearest Neighbors (KNN)Support Vector Machine (SVM)
Accuracy0.8342320.8301890.836927
F1-Score0.4765960.4568970.514056
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" ], "text/plain": [ " Logistic Regression K-Nearest Neighbors (KNN) \\\n", "Accuracy 0.834232 0.830189 \n", "F1-Score 0.476596 0.456897 \n", "\n", " Support Vector Machine (SVM) \n", "Accuracy 0.836927 \n", "F1-Score 0.514056 " ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Hasil Perbandingan Tiap Model\n", "df_model" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: Logistic Regression\n", "Accuracy: 0.8342318059299192\n", "Precision: 0.6746987951807228\n", "Recall: 0.3684210526315789\n", "F1 Score: 0.4765957446808511\n", "\n", "Model: K-Nearest Neighbors\n", "Accuracy: 0.8301886792452831\n", "Precision: 0.6625\n", "Recall: 0.34868421052631576\n", "F1 Score: 0.45689655172413796\n", "\n", "Model: Support Vector Machine (SVM)\n", "Accuracy: 0.8369272237196765\n", "Precision: 0.6597938144329897\n", "Recall: 0.42105263157894735\n", "F1 Score: 0.5140562248995983\n", "\n", "The best model is: Support Vector Machine (SVM)\n", "With performance metrics:\n", "Accuracy: 0.8369272237196765\n", "Precision: 0.6597938144329897\n", "Recall: 0.42105263157894735\n", "F1 Score: 0.5140562248995983\n", "And parameters:\n", "{'svc__C': 10, 'svc__gamma': 0.01}\n" ] } ], "source": [ "# Membuat List untuk Menyimpan Model\n", "models_performance = []\n", "\n", "# Defenisi Metriks Evaluation\n", "evaluation_metrics = {\n", " 'Accuracy': accuracy_score,\n", " 'Precision': precision_score,\n", " 'Recall': recall_score,\n", " 'F1 Score': f1_score\n", "}\n", "\n", "# Evaluasi Model\n", "models = {\n", " 'Logistic Regression': grid_logisctic_regression,\n", " 'K-Nearest Neighbors': grid_KNN,\n", " 'Support Vector Machine (SVM)': grid_SVC\n", "}\n", "\n", "# Iterate Tiap Model\n", "for model_name, model in models.items():\n", " # Fit model\n", " model.fit(X_train_new, y_train) # Assuming X_train and y_train are your training data\n", " \n", " # Prediksi\n", " y_pred = model.predict(X_test_new) # Assuming X_test is your test data\n", " \n", " # Kalkulasi Performa Metriks\n", " performance = {}\n", " for metric_name, metric_func in evaluation_metrics.items():\n", " performance[metric_name] = metric_func(y_test, y_pred)\n", " \n", " # Mernambah Nama Model dan Performa ke dalam List\n", " models_performance.append((model_name, performance))\n", "\n", "# Menampilkan Hasil Performa Metriks Setiap Model\n", "for model_name, performance in models_performance:\n", " print(f\"Model: {model_name}\")\n", " for metric_name, metric_value in performance.items():\n", " print(f\"{metric_name}: {metric_value}\")\n", " print()\n", "\n", "# Memilih Model Terbaik Untuk Setiap Rata-Rata Performa Setiap Metriks \n", "best_model = max(models_performance, key=lambda x: sum(x[1].values()))\n", "\n", "# Menyimpan Pipeline yang telah di Hyperparameter Tuning\n", "best_pipe = models[best_model[0]]\n", "\n", "# Menampilkan Hasil Performa Model\n", "print(f\"The best model is: {best_model[0]}\")\n", "print(\"With performance metrics:\")\n", "for metric_name, metric_value in best_model[1].items():\n", " print(f\"{metric_name}: {metric_value}\")\n", "print(\"And parameters:\")\n", "print(models[best_model[0]].best_params_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Pada sesi ini dilakukan perbandingan untuk setiap Accuracy, Precision, Recall, F1 Score untuk setiap Model dan diperoleh model dengan metode Support Vector Machine (SVM) adalah yang terbaik dengan *Hyperparameter* ('svc__C': 10, 'svc__gamma': 0.01)\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## ***Model Saving***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [], "source": [ "# Menyimpan File\n", "with open('best_pipe.pkl', 'wb') as file_1: # wb = write binary\n", " pickle.dump(best_pipe, file_1)\n", "with open('num_col.txt', 'w') as file_2: # wb = write binary\n", " json.dump(num_columns,file_2)\n", "with open('cat_col.txt', 'w') as file_3: # wb = write binary\n", " json.dump(cat_columns,file_3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "## ***Model Inference***\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### **Membuka File**\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 80, "metadata": {}, "outputs": [], "source": [ "# Loading File\n", "with open('best_pipe.pkl', 'rb') as file_1: # wb = write binary\n", " best_pipe = pickle.load(file_1)\n", "with open('num_col.txt', 'r') as file_2: # wb = write binary\n", " num_col = json.load(file_2)\n", "with open('cat_col.txt', 'r') as file_3: # wb = write binary\n", " cat_col = json.load(file_3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "#### **Membuat & Memprediksi Data *Inference***\n", "\n", "---" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [], "source": [ "# Membuat Data Inference\n", "df_inf = {\n", " 'limit_balance': [80000, 20000],\n", " 'sex': [1, 0],\n", " 'education_level': [2, 3],\n", " 'marital_status': [1, 1],\n", " 'age': [42, 53],\n", " 'pay_1': [2, 3],\n", " 'pay_2': [1, 3],\n", " 'pay_3': [5, 4],\n", " 'pay_4': [3, 3],\n", " 'pay_5': [2, 2],\n", " 'pay_6': [-1, 5],\n", " 'bill_amt_1': [56133, 34444],\n", " 'bill_amt_2': [44332, 53422],\n", " 'bill_amt_3': [60434, 60333],\n", " 'bill_amt_4': [29523, 34563],\n", " 'bill_amt_5': [31145, 32355],\n", " 'bill_amt_6': [41231, 35465],\n", " 'pay_amt_1': [3323, 1231],\n", " 'pay_amt_2': [4343, 4321],\n", " 'pay_amt_3': [2300, 313],\n", " 'pay_amt_4': [1313, 1231],\n", " 'pay_amt_5': [1444, 5435], \n", " 'pay_amt_6': [534, 435],\n", "}\n", "\n", "df_inf = pd.DataFrame(df_inf)" ] }, { "cell_type": "code", "execution_count": 82, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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limit_balancesexeducation_levelmarital_statusagepay_1pay_2pay_3pay_4pay_5pay_6bill_amt_1bill_amt_2bill_amt_3bill_amt_4bill_amt_5bill_amt_6pay_amt_1pay_amt_2pay_amt_3pay_amt_4pay_amt_5pay_amt_6
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" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_1 pay_2 \\\n", "0 80000 1 2 1 42 2 1 \n", "1 20000 0 3 1 53 3 3 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 bill_amt_4 \\\n", "0 5 3 2 -1 56133 44332 60434 29523 \n", "1 4 3 2 5 34444 53422 60333 34563 \n", "\n", " bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 pay_amt_4 \\\n", "0 31145 41231 3323 4343 2300 1313 \n", "1 32355 35465 1231 4321 313 1231 \n", "\n", " pay_amt_5 pay_amt_6 \n", "0 1444 534 \n", "1 5435 435 " ] }, "execution_count": 82, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Menampilkan Data Inference\n", "df_inf" ] }, { "cell_type": "code", "execution_count": 83, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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limit_balancesexeducation_levelmarital_statusagepay_1pay_2pay_3pay_4pay_5pay_6bill_amt_1bill_amt_2bill_amt_3bill_amt_4bill_amt_5bill_amt_6pay_amt_1pay_amt_2pay_amt_3pay_amt_4pay_amt_5pay_amt_6
0800001214221532-156133443326043429523311454123133234343230013131444534
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" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_1 pay_2 \\\n", "0 80000 1 2 1 42 2 1 \n", "1 20000 0 3 1 53 3 3 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 bill_amt_4 \\\n", "0 5 3 2 -1 56133 44332 60434 29523 \n", "1 4 3 2 5 34444 53422 60333 34563 \n", "\n", " bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 pay_amt_4 \\\n", "0 31145 41231 3323 4343 2300 1313 \n", "1 32355 35465 1231 4321 313 1231 \n", "\n", " pay_amt_5 pay_amt_6 \n", "0 1444 534 \n", "1 5435 435 " ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Memisahkan Kategori Kolom Kategori dan Numerical\n", "df_inf_num = df_inf[num_columns]\n", "df_inf_cat = df_inf[cat_columns]\n", "df_inf_num" ] }, { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Rating: [1 1]\n", "Rating: 1\n" ] } ], "source": [ "# Memprediksikan Menggunakan Model Terbaik\n", "y_pred_inf = best_pipe.predict(df_inf)\n", "print('Rating:',y_pred_inf) #before\n", "print('Rating:',round(y_pred_inf[0],3)) #after" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "***Insight:***\n", "\n", "Telah berhasil dilakukan *inference* pada suatu data random. Dari hasil data random ditemukan bahwa nasabah mengalami kegagalan dalam membayar \n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## **Kesimpulan**\n", "\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Setelah dilakukan pemodelan pada kasus `credit card default`, diperoleh kesimpulan sebagai berikut:\n", "\n", "- Setiap *Coeficient* pada metode *logistic regression* mewakili bagaimana setiap fitur berkontribusi terhadap prediksi kelas target. Pada model ini bervariasi seperti memiliki nilai positif, negatif dan nilai 0. Nilai positif menunjukkan bahwa fitur tersebut berkontribusi positif terhadap kelas target, sementara nilai negatif menunjukkan kontribusi negatif. Sedangkan nilai 0 menunjukkan bahwa fitur-fitur ini memiliki kontribusi yang sangat kecil terhadap prediksi kelas target. Sehingga untuk optimasi model ini harus mengurangi nilai 0 semaksimal mungkin\n", "\n", "- Kernel digunakan dalam SVM untuk mentransformasi data masukan ke dalam ruang dimensi yang lebih tinggi sehingga data dapat dipisahkan secara linear di ruang yang lebih tinggi, bahkan jika data aslinya tidak dapat dipisahkan secara linear di ruang dimensi rendah. Pada pemodelan ini tidak dilakukan *Hyperparamter Tuning* pada metode Kernel dikarenakan alasan biaya komputasi yang tinggi\n", "\n", "- Pemilihan K yang optimal pada metode *K-Nearest Neighbor* (KNN) dianjurkan untuk meningkatkan akurasu dari pada prediksi data baru. Pada model ini dilakukan *Hyperparameter Tuning* untuk menemukan nilai K yang terbaik yaitu 9. Selain itu telah dilakukan plot terhadap nilai K dari 1 - 100 yang dimana hasilnya prediksi akan memburuk seiring meningkatnya jumlah K\n", "\n", "- Parameter `Accuracy`, `Precision`, `Recall`, `F1 Score` digunakan untuk melihat hasil klasifikasi untuk model.\n", " \n", " - Accuracy ketika data balance \n", "\n", " - Recall untuk kasus *false negatif* sekecil mungkin\n", " \n", " - Precision untuk kasus *false positif* sekecil mungkin\n", " \n", " - F1 score kalo sama pentingnya antara Recall dan Precision\n", "\n", " Untuk Pemodelan ini sendiri memiliki nilai `Accuracy` yang tinggi namun `F1 Score` rendah. Oleh karena itu, perlu dilakukan analisa kembali untuk meningkatkan keakuratan pemodelan " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 2 }