{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "\n", "Nama : Badriah Nursakinah\n", "\n", "Batch : BSD_005\n", "\n", "Dataset : P1G5_Set_1_badriah_nursakinah.csv\n", "\n", "Studi Kasus : Memprediksi `default_payment_next_month` dengan model Classification pada dataset yang sudah di simpan pada format csv\n", "\n", "Link Hugging : https://huggingface.co/spaces/nursakinahbadriah/predict_credit_card_default/tree/main\n", "\n", "---\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Perkenalan\n", "\n", "Problem Statement : Sebagai Data Scientiest saya akan mencoba memprediksi `default_payment_next_month` dengan model Classification, dimana untuk model inference merupakan kesimpulan akhir yang menjawab proses pengolahan data dengan menggunakan model Classification\n", "\n", "\n", "Adapun poin-poin yang akan dianalisa dengan mengunakan metode `Explore Data Analysis` diantaranya sebagai berikut :\n", "\n", " - Objective 1 : Memprediksi limit_balance untuk `Age`, `Count`, `Marital_Status`, `Education_Level`\n", " - Objective 2 : Memprediksi usia yang paling banyak membayar\n", " - Objective 3 : Memprediksi jumlah pengguna kartu kredit berdasarkan jenis kelamin\n", " - Objective 4 : Memprediksi pengguna kartu kredit berdsarkan status pernikahan\n", " - Objective 5 : Memprediksi pengguna kartu kredit berdasarkan usia\n", " - Objective 6 : Memprediksi limit_limi_balance pada setiap individu\n", " - Objective 7 : Memprediksi hubungan antara umur dengan limit_balance" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Query SQL\n", "\n", "Merupakan hasil proses penyimpanan data yang sudah diolah pada Query SQL dengan menggunaka google platform, dimana data yang sudah pada Query SQL akan disimpan pada format csv. Dimana data yang berformat csv ini nantinya akan digunakan sebagai dataset guna diperlukan pada proses selanjutnya.\n", "\n", "`'SELECT limit_balance,\n", " CAST(sex AS INT) AS sex,\n", " CAST(education_level AS INT) AS education_level,\n", " CAST(marital_status AS INT) AS marital_status,\n", " age,\n", " pay_0,\n", " pay_2,\n", " pay_3,\n", " pay_4,\n", " CAST(pay_5 AS FLOAT) AS pay_5,\n", " CAST(pay_6 AS FLOAT) AS pay_6,\n", " bill_amt_1,\n", " bill_amt_2,\n", " bill_amt_3,\n", " bill_amt_4,\n", " bill_amt_5,\n", " bill_amt_6,\n", " pay_amt_1,\n", " pay_amt_2,\n", " pay_amt_3,\n", " pay_amt_4,\n", " pay_amt_5,\n", " pay_amt_6,\n", " CAST(default_payment_next_month AS INT) AS default_payment_next_month\n", "FROM\n", " credit_card_default.ml_datasets\n", "LIMIT 9945`\n", "'''" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Import Libraries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Berikut merupakan beberapa libraries yang digunakan sebagai alat pendukung dalam proses analisa ini dan machine learning framework yang digunakan adalah `Scikit-Learn`" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "\n", "import pandas as pd\n", "import numpy as np\n", "from scipy.stats import skew\n", "from sklearn.preprocessing import OneHotEncoder\n", "import phik\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import classification_report, accuracy_score\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.naive_bayes import GaussianNB\n", "from phik.report import plot_correlation_matrix\n", "from sklearn.ensemble import AdaBoostClassifier\n", "from sklearn.compose import ColumnTransformer\n", "from sklearn.pipeline import make_pipeline, Pipeline\n", "from feature_engine.outliers import Winsorizer\n", "from sklearn.model_selection import cross_val_score, KFold, GridSearchCV\n", "from sklearn.model_selection import GridSearchCV\n", "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "from sklearn.model_selection import StratifiedKFold\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.metrics import f1_score\n", "from sklearn.metrics import confusion_matrix\n", "from sklearn.metrics import roc_auc_score, ConfusionMatrixDisplay\n", "import pickle\n", "import json" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Loading Data\n", "\n", "Referensi data yang digunakan bersumber dari proses Query SQL dimana dataset ini akan diolah dan diproses sebagai bahan acuan guna memprediksi `default_payment_next_month` dengan model Classification pada dataset yang sudah di simpan pada format csv.\n", "\n", "Adapun tujuan dari proses ini adalah untuk mengimpor atau memuat data dari sumber eksternal ke dalam program atau lingkungan pemrograman tertentu, dimana proses ini merupakan langkah awal dalam analisis data dan pemrosesan data lebih lanjut" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "... | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2.965000e+03 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "2965.000000 | \n", "
mean | \n", "163369.308600 | \n", "1.607757 | \n", "1.849578 | \n", "1.559865 | \n", "35.193255 | \n", "0.005059 | \n", "-0.122428 | \n", "-0.141653 | \n", "-0.185160 | \n", "-0.225295 | \n", "... | \n", "44089.683305 | \n", "40956.080607 | \n", "39773.072513 | \n", "6348.902867 | \n", "6.272494e+03 | \n", "5150.497133 | \n", "4561.376054 | \n", "4913.286678 | \n", "5382.701518 | \n", "0.214165 | \n", "
std | \n", "125030.415472 | \n", "0.488333 | \n", "0.778184 | \n", "0.522317 | \n", "9.109439 | \n", "1.114395 | \n", "1.180784 | \n", "1.183630 | \n", "1.178322 | \n", "1.159003 | \n", "... | \n", "61907.454056 | \n", "58271.904751 | \n", "57303.488981 | \n", "20885.735336 | \n", "2.887967e+04 | \n", "14287.079982 | \n", "13281.499599 | \n", "16734.340778 | \n", "17275.953029 | \n", "0.410311 | \n", "
min | \n", "10000.000000 | \n", "1.000000 | \n", "0.000000 | \n", "0.000000 | \n", "21.000000 | \n", "-2.000000 | \n", "-2.000000 | \n", "-2.000000 | \n", "-2.000000 | \n", "-2.000000 | \n", "... | \n", "-46627.000000 | \n", "-46627.000000 | \n", "-73895.000000 | \n", "0.000000 | \n", "0.000000e+00 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "
25% | \n", "50000.000000 | \n", "1.000000 | \n", "1.000000 | \n", "1.000000 | \n", "28.000000 | \n", "-1.000000 | \n", "-1.000000 | \n", "-1.000000 | \n", "-1.000000 | \n", "-1.000000 | \n", "... | \n", "2582.000000 | \n", "1958.000000 | \n", "1430.000000 | \n", "1013.000000 | \n", "9.900000e+02 | \n", "477.000000 | \n", "313.000000 | \n", "323.000000 | \n", "173.000000 | \n", "0.000000 | \n", "
50% | \n", "140000.000000 | \n", "2.000000 | \n", "2.000000 | \n", "2.000000 | \n", "34.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "... | \n", "19894.000000 | \n", "18814.000000 | \n", "18508.000000 | \n", "2234.000000 | \n", "2.175000e+03 | \n", "1994.000000 | \n", "1600.000000 | \n", "1646.000000 | \n", "1615.000000 | \n", "0.000000 | \n", "
75% | \n", "230000.000000 | \n", "2.000000 | \n", "2.000000 | \n", "2.000000 | \n", "41.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "... | \n", "58622.000000 | \n", "53373.000000 | \n", "52287.000000 | \n", "5087.000000 | \n", "5.000000e+03 | \n", "4500.000000 | \n", "4000.000000 | \n", "4021.000000 | \n", "4081.000000 | \n", "0.000000 | \n", "
max | \n", "800000.000000 | \n", "2.000000 | \n", "6.000000 | \n", "3.000000 | \n", "69.000000 | \n", "8.000000 | \n", "7.000000 | \n", "7.000000 | \n", "8.000000 | \n", "7.000000 | \n", "... | \n", "488808.000000 | \n", "441981.000000 | \n", "436172.000000 | \n", "493358.000000 | \n", "1.227082e+06 | \n", "199209.000000 | \n", "202076.000000 | \n", "388071.000000 | \n", "403500.000000 | \n", "1.000000 | \n", "
8 rows × 24 columns
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---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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\n", "\n", " | limit_balance | \n", "sex | \n", "education_level | \n", "marital_status | \n", "age | \n", "pay_1 | \n", "pay_2 | \n", "pay_3 | \n", "pay_4 | \n", "pay_5 | \n", "... | \n", "bill_amt_4 | \n", "bill_amt_5 | \n", "bill_amt_6 | \n", "pay_amt_1 | \n", "pay_amt_2 | \n", "pay_amt_3 | \n", "pay_amt_4 | \n", "pay_amt_5 | \n", "pay_amt_6 | \n", "default_payment_next_month | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "... | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2.964000e+03 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "2964.000000 | \n", "
mean | \n", "163356.950067 | \n", "1.607625 | \n", "1.849865 | \n", "1.560054 | \n", "35.193657 | \n", "0.004723 | \n", "-0.121795 | \n", "-0.141026 | \n", "-0.184548 | \n", "-0.224696 | \n", "... | \n", "44104.558367 | \n", "40969.898448 | \n", "39786.491228 | \n", "6351.044872 | \n", "6.274611e+03 | \n", "5152.234818 | \n", "4562.914980 | \n", "4914.944332 | \n", "5384.517544 | \n", "0.214238 | \n", "
std | \n", "125049.701016 | \n", "0.488362 | \n", "0.778159 | \n", "0.522304 | \n", "9.110949 | \n", "1.114434 | \n", "1.180479 | \n", "1.183337 | \n", "1.178049 | \n", "1.158739 | \n", "... | \n", "61912.600106 | \n", "58276.878626 | \n", "57308.498646 | \n", "20888.933729 | \n", "2.888432e+04 | \n", "14289.177313 | \n", "13283.476236 | \n", "16736.920951 | \n", "17278.585006 | \n", "0.410362 | \n", "
min | \n", "10000.000000 | \n", "1.000000 | \n", "0.000000 | \n", "0.000000 | \n", "21.000000 | \n", "-2.000000 | \n", "-2.000000 | \n", "-2.000000 | \n", "-2.000000 | \n", "-2.000000 | \n", "... | \n", "-46627.000000 | \n", "-46627.000000 | \n", "-73895.000000 | \n", "0.000000 | \n", "0.000000e+00 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "
25% | \n", "50000.000000 | \n", "1.000000 | \n", "1.000000 | \n", "1.000000 | \n", "28.000000 | \n", "-1.000000 | \n", "-1.000000 | \n", "-1.000000 | \n", "-1.000000 | \n", "-1.000000 | \n", "... | \n", "2585.000000 | \n", "1974.500000 | \n", "1433.000000 | \n", "1013.000000 | \n", "9.975000e+02 | \n", "479.250000 | \n", "313.750000 | \n", "325.250000 | \n", "175.250000 | \n", "0.000000 | \n", "
50% | \n", "140000.000000 | \n", "2.000000 | \n", "2.000000 | \n", "2.000000 | \n", "34.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "... | \n", "19895.000000 | \n", "18819.500000 | \n", "18511.500000 | \n", "2238.000000 | \n", "2.182500e+03 | \n", "1997.000000 | \n", "1600.000000 | \n", "1646.500000 | \n", "1626.500000 | \n", "0.000000 | \n", "
75% | \n", "230000.000000 | \n", "2.000000 | \n", "2.000000 | \n", "2.000000 | \n", "41.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "... | \n", "58638.500000 | \n", "53414.500000 | \n", "52297.000000 | \n", "5089.500000 | \n", "5.000000e+03 | \n", "4500.000000 | \n", "4000.250000 | \n", "4021.000000 | \n", "4081.750000 | \n", "0.000000 | \n", "
max | \n", "800000.000000 | \n", "2.000000 | \n", "6.000000 | \n", "3.000000 | \n", "69.000000 | \n", "8.000000 | \n", "7.000000 | \n", "7.000000 | \n", "8.000000 | \n", "7.000000 | \n", "... | \n", "488808.000000 | \n", "441981.000000 | \n", "436172.000000 | \n", "493358.000000 | \n", "1.227082e+06 | \n", "199209.000000 | \n", "202076.000000 | \n", "388071.000000 | \n", "403500.000000 | \n", "1.000000 | \n", "
8 rows × 24 columns
\n", "\n", " | limit_balance | \n", "sex | \n", "education_level | \n", "marital_status | \n", "age | \n", "pay_1 | \n", "pay_2 | \n", "pay_3 | \n", "pay_4 | \n", "pay_5 | \n", "... | \n", "bill_amt_5 | \n", "bill_amt_6 | \n", "pay_amt_1 | \n", "pay_amt_2 | \n", "pay_amt_3 | \n", "pay_amt_4 | \n", "pay_amt_5 | \n", "pay_amt_6 | \n", "default_payment_next_month | \n", "Klasifikasi | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "80000.0 | \n", "1 | \n", "6 | \n", "1 | \n", "54.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "... | \n", "26210.0 | \n", "17643.0 | \n", "2545.0 | \n", "2208.0 | \n", "1336.0 | \n", "2232.0 | \n", "542.0 | \n", "348.0 | \n", "1 | \n", "Lansia | \n", "
1 | \n", "200000.0 | \n", "1 | \n", "4 | \n", "1 | \n", "49.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "... | \n", "50235.0 | \n", "48984.0 | \n", "1689.0 | \n", "2164.0 | \n", "2500.0 | \n", "3480.0 | \n", "2500.0 | \n", "3000.0 | \n", "0 | \n", "Lansia | \n", "
2 | \n", "20000.0 | \n", "2 | \n", "6 | \n", "2 | \n", "22.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "... | \n", "500.0 | \n", "0.0 | \n", "4641.0 | \n", "1019.0 | \n", "900.0 | \n", "0.0 | \n", "1500.0 | \n", "0.0 | \n", "1 | \n", "Remaja | \n", "
3 | \n", "260000.0 | \n", "2 | \n", "4 | \n", "2 | \n", "33.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "... | \n", "30767.0 | \n", "29890.0 | \n", "5000.0 | \n", "5000.0 | \n", "1137.0 | \n", "5000.0 | \n", "1085.0 | \n", "5000.0 | \n", "0 | \n", "Dewasa | \n", "
4 | \n", "150000.0 | \n", "1 | \n", "4 | \n", "2 | \n", "32.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "-1.0 | \n", "0 | \n", "... | \n", "143375.0 | \n", "146411.0 | \n", "4019.0 | \n", "146896.0 | \n", "157436.0 | \n", "4600.0 | \n", "4709.0 | \n", "5600.0 | \n", "0 | \n", "Dewasa | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
2960 | \n", "80000.0 | \n", "2 | \n", "3 | \n", "2 | \n", "28.0 | \n", "-1.0 | \n", "-1.0 | \n", "-1.0 | \n", "-2.0 | \n", "-2 | \n", "... | \n", "0.0 | \n", "0.0 | \n", "2800.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "Dewasa | \n", "
2961 | \n", "50000.0 | \n", "2 | \n", "3 | \n", "1 | \n", "51.0 | \n", "-1.0 | \n", "-1.0 | \n", "-1.0 | \n", "-1.0 | \n", "-2 | \n", "... | \n", "0.0 | \n", "0.0 | \n", "300.0 | \n", "5880.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "1 | \n", "Lansia | \n", "
2962 | \n", "450000.0 | \n", "2 | \n", "2 | \n", "1 | \n", "38.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2 | \n", "... | \n", "390.0 | \n", "390.0 | \n", "390.0 | \n", "780.0 | \n", "390.0 | \n", "390.0 | \n", "390.0 | \n", "390.0 | \n", "1 | \n", "Dewasa | \n", "
2963 | \n", "50000.0 | \n", "2 | \n", "2 | \n", "1 | \n", "44.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2 | \n", "... | \n", "390.0 | \n", "0.0 | \n", "390.0 | \n", "390.0 | \n", "390.0 | \n", "390.0 | \n", "0.0 | \n", "780.0 | \n", "0 | \n", "Dewasa | \n", "
2964 | \n", "290000.0 | \n", "2 | \n", "2 | \n", "1 | \n", "39.0 | \n", "1.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2 | \n", "... | \n", "390.0 | \n", "390.0 | \n", "10000.0 | \n", "800.0 | \n", "3184.0 | \n", "390.0 | \n", "390.0 | \n", "6617.0 | \n", "0 | \n", "Dewasa | \n", "
2964 rows × 25 columns
\n", "\n", " | count | \n", "
---|---|
Klasifikasi | \n", "\n", " |
Dewasa | \n", "2136 | \n", "
Kanak-Kanak | \n", "119 | \n", "
Lansia | \n", "438 | \n", "
Remaja | \n", "271 | \n", "
\n", " | sex | \n", "education_level | \n", "marital_status | \n", "pay_1 | \n", "pay_2 | \n", "pay_3 | \n", "pay_4 | \n", "pay_5 | \n", "pay_6 | \n", "
---|---|---|---|---|---|---|---|---|---|
1968 | \n", "2 | \n", "2 | \n", "1 | \n", "-2.0 | \n", "-2.0 | \n", "-1.0 | \n", "2.0 | \n", "2 | \n", "-2 | \n", "
2725 | \n", "1 | \n", "2 | \n", "2 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "-2.0 | \n", "-2 | \n", "-2 | \n", "
443 | \n", "1 | \n", "2 | \n", "1 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "
825 | \n", "2 | \n", "2 | \n", "1 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "
664 | \n", "2 | \n", "2 | \n", "1 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
2604 | \n", "2 | \n", "1 | \n", "2 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2 | \n", "-2 | \n", "
1478 | \n", "2 | \n", "3 | \n", "1 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "
617 | \n", "2 | \n", "2 | \n", "1 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "
1993 | \n", "2 | \n", "3 | \n", "1 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "3 | \n", "2 | \n", "
1897 | \n", "2 | \n", "2 | \n", "1 | \n", "1.0 | \n", "3.0 | \n", "2.0 | \n", "2.0 | \n", "2 | \n", "0 | \n", "
2370 rows × 9 columns
\n", "\n", " | limit_balance | \n", "pay_1 | \n", "pay_2 | \n", "pay_3 | \n", "pay_4 | \n", "pay_5 | \n", "pay_6 | \n", "pay_amt_1 | \n", "pay_amt_2 | \n", "pay_amt_3 | \n", "pay_amt_4 | \n", "pay_amt_5 | \n", "pay_amt_6 | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1968 | \n", "80000.0 | \n", "-2.0 | \n", "-2.0 | \n", "-1.0 | \n", "2.0 | \n", "2 | \n", "-2 | \n", "0.000 | \n", "11049.375 | \n", "0.0 | \n", "0.000 | \n", "0.0 | \n", "0.0 | \n", "
2725 | \n", "180000.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "-2.0 | \n", "-2 | \n", "-2 | \n", "1110.000 | \n", "0.000 | \n", "0.0 | \n", "0.000 | \n", "0.0 | \n", "0.0 | \n", "
443 | \n", "310000.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "11171.625 | \n", "11049.375 | \n", "2500.0 | \n", "9527.875 | \n", "3000.0 | \n", "4377.0 | \n", "
825 | \n", "270000.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "2500.000 | \n", "2000.000 | \n", "2000.0 | \n", "2000.000 | \n", "2000.0 | \n", "2000.0 | \n", "
664 | \n", "380000.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "9000.000 | \n", "9000.000 | \n", "10068.0 | \n", "8768.000 | \n", "9087.0 | \n", "9133.0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
2604 | \n", "270000.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2 | \n", "-2 | \n", "592.000 | \n", "1593.000 | \n", "913.0 | \n", "1471.000 | \n", "1302.0 | \n", "7107.0 | \n", "
1478 | \n", "30000.0 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "3280.000 | \n", "0.000 | \n", "1511.0 | \n", "609.000 | \n", "500.0 | \n", "800.0 | \n", "
617 | \n", "160000.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0 | \n", "0 | \n", "8450.000 | \n", "5289.000 | \n", "4022.0 | \n", "4195.000 | \n", "3986.0 | \n", "3958.0 | \n", "
1993 | \n", "30000.0 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "3 | \n", "2 | \n", "1700.000 | \n", "1200.000 | \n", "1600.0 | \n", "0.000 | \n", "1500.0 | \n", "0.0 | \n", "
1897 | \n", "10000.0 | \n", "1.0 | \n", "3.0 | \n", "2.0 | \n", "2.0 | \n", "2 | \n", "0 | \n", "0.000 | \n", "0.000 | \n", "3000.0 | \n", "0.000 | \n", "500.0 | \n", "1000.0 | \n", "
2370 rows × 13 columns
\n", "ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1', 'pay_amt_2',\n", " 'pay_amt_3', 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1', 'pay_amt_2',\n", " 'pay_amt_3', 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])
['limit_balance', 'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', 'pay_amt_6']
StandardScaler()
passthrough
\n", " | scaling__limit_balance | \n", "scaling__pay_amt_1 | \n", "scaling__pay_amt_2 | \n", "scaling__pay_amt_3 | \n", "scaling__pay_amt_4 | \n", "scaling__pay_amt_5 | \n", "scaling__pay_amt_6 | \n", "remainder__pay_1 | \n", "remainder__pay_2 | \n", "remainder__pay_3 | \n", "remainder__pay_4 | \n", "remainder__pay_5 | \n", "remainder__pay_6 | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "-0.686097 | \n", "-1.028015 | \n", "2.133638 | \n", "-0.917534 | \n", "-0.907005 | \n", "-0.908620 | \n", "-0.881127 | \n", "-2.0 | \n", "-2.0 | \n", "-1.0 | \n", "2.0 | \n", "2.0 | \n", "-2.0 | \n", "
1 | \n", "0.138932 | \n", "-0.713036 | \n", "-1.007524 | \n", "-0.917534 | \n", "-0.907005 | \n", "-0.908620 | \n", "-0.881127 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "
2 | \n", "1.211471 | \n", "2.142103 | \n", "2.133638 | \n", "-0.184365 | \n", "2.288891 | \n", "0.103903 | \n", "0.504560 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
3 | \n", "0.881459 | \n", "-0.318602 | \n", "-0.438955 | \n", "-0.330999 | \n", "-0.236153 | \n", "-0.233605 | \n", "-0.247959 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
4 | \n", "1.788992 | \n", "1.525872 | \n", "1.551033 | \n", "2.035084 | \n", "2.034009 | \n", "2.158312 | \n", "2.010233 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
2365 | \n", "0.881459 | \n", "-0.860026 | \n", "-0.554659 | \n", "-0.649781 | \n", "-0.413593 | \n", "-0.469185 | \n", "1.368834 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "-2.0 | \n", "
2366 | \n", "-1.098612 | \n", "-0.097265 | \n", "-1.007524 | \n", "-0.474407 | \n", "-0.702730 | \n", "-0.739866 | \n", "-0.627860 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
2367 | \n", "-0.026074 | \n", "1.369801 | \n", "0.496055 | \n", "0.261988 | \n", "0.500107 | \n", "0.436685 | \n", "0.371912 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "0.0 | \n", "
2368 | \n", "-1.098612 | \n", "-0.545614 | \n", "-0.666383 | \n", "-0.448306 | \n", "-0.907005 | \n", "-0.402359 | \n", "-0.881127 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "3.0 | \n", "2.0 | \n", "
2369 | \n", "-1.263618 | \n", "-1.028015 | \n", "-1.007524 | \n", "-0.037732 | \n", "-0.907005 | \n", "-0.739866 | \n", "-0.564543 | \n", "1.0 | \n", "3.0 | \n", "2.0 | \n", "2.0 | \n", "2.0 | \n", "0.0 | \n", "
2370 rows × 13 columns
\n", "Pipeline(steps=[('preprocessing',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1',\n", " 'pay_amt_2', 'pay_amt_3',\n", " 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])),\n", " ('SVM', SVC())])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('preprocessing',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1',\n", " 'pay_amt_2', 'pay_amt_3',\n", " 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])),\n", " ('SVM', SVC())])
ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1', 'pay_amt_2',\n", " 'pay_amt_3', 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])
['limit_balance', 'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', 'pay_amt_6']
StandardScaler()
['pay_1', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6']
passthrough
SVC()
GridSearchCV(cv=5,\n", " estimator=Pipeline(steps=[('preprocessing',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling',\n", " StandardScaler(),\n", " ['limit_balance',\n", " 'pay_amt_1',\n", " 'pay_amt_2',\n", " 'pay_amt_3',\n", " 'pay_amt_4',\n", " 'pay_amt_5',\n", " 'pay_amt_6'])])),\n", " ('SVM', SVC())]),\n", " param_grid={'SVM__C': [0.001, 0.01, 0.1, 1, 10, 500],\n", " 'SVM__gamma': [0.001, 0.01, 0.1, 1, 10, 500, 'auto']},\n", " scoring='f1')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
GridSearchCV(cv=5,\n", " estimator=Pipeline(steps=[('preprocessing',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling',\n", " StandardScaler(),\n", " ['limit_balance',\n", " 'pay_amt_1',\n", " 'pay_amt_2',\n", " 'pay_amt_3',\n", " 'pay_amt_4',\n", " 'pay_amt_5',\n", " 'pay_amt_6'])])),\n", " ('SVM', SVC())]),\n", " param_grid={'SVM__C': [0.001, 0.01, 0.1, 1, 10, 500],\n", " 'SVM__gamma': [0.001, 0.01, 0.1, 1, 10, 500, 'auto']},\n", " scoring='f1')
Pipeline(steps=[('preprocessing',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1',\n", " 'pay_amt_2', 'pay_amt_3',\n", " 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])),\n", " ('SVM', SVC())])
ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1', 'pay_amt_2',\n", " 'pay_amt_3', 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])
['limit_balance', 'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', 'pay_amt_6']
StandardScaler()
['pay_1', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6']
passthrough
SVC()
Pipeline(steps=[('preprocessing',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1',\n", " 'pay_amt_2', 'pay_amt_3',\n", " 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])),\n", " ('SVM', SVC(C=1, gamma='auto'))])In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
Pipeline(steps=[('preprocessing',\n", " ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1',\n", " 'pay_amt_2', 'pay_amt_3',\n", " 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])),\n", " ('SVM', SVC(C=1, gamma='auto'))])
ColumnTransformer(remainder='passthrough',\n", " transformers=[('scaling', StandardScaler(),\n", " ['limit_balance', 'pay_amt_1', 'pay_amt_2',\n", " 'pay_amt_3', 'pay_amt_4', 'pay_amt_5',\n", " 'pay_amt_6'])])
['limit_balance', 'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', 'pay_amt_6']
StandardScaler()
['pay_1', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6']
passthrough
SVC(C=1, gamma='auto')