{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "```text\n", "=================================================\n", "\n", "GRADED CHALLENGE 5\n", "\n", "Nama : Ahmad Naufal Budianto\n", "Batch : RMT-31\n", "\n", "=================================================\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Program ini dibuat untuk memprediksi `default_payment_next_month` dengan metode klasifikasi menggunakan tiga tipe pemodelan (Logistic Regression, SVM, dan KNN). Dataset yang saya pergunakan merupakan data dari bigquery `bigquery-public-data.ml_datasets.credit_card_default`. Dalam pengolahan data saya melakukan EDA, Feature Engineering, Pipeline Model (pelatihan dan evaluasi), Model Saving, dan Model Inference; yang kemudian akan saya lakukan deployment menggunakan `streamlit`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Query SQL" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pada tahap awal saya menggunakan Google Cloud environment (Google Collab) untuk mengambil data bigquery, dengan hanya mengambil kolom-kolom `limit_balance, sex, education_level, marital_status, age, pay_0, 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, default_payment_next_month`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bvMeowGGBkRs", "outputId": "8e785c9f-9bf8-443f-8ffb-94c3b7675846" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Authenticated\n" ] } ], "source": [ "from google.colab import auth\n", "from google.cloud import bigquery\n", "from google.colab import files\n", "\n", "auth.authenticate_user()\n", "print('Authenticated')\n", "\n", "project_id = \"hacktiv-421408\"\n", "client = bigquery.Client(project=project_id)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "1wbWfJaYCoah" }, "outputs": [], "source": [ "df = client.query('''\n", "SELECT\n", " limit_balance,\n", " CAST(sex AS INT64) AS sex,\n", " CAST(education_level AS INT64) AS education_level,\n", " CAST(marital_status AS INT64) AS marital_status,\n", " age,\n", " pay_0,\n", " pay_2,\n", " pay_3,\n", " pay_4,\n", " CAST(pay_5 AS FLOAT64) AS pay_5,\n", " CAST(pay_6 AS FLOAT64) 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 INT64) AS default_payment_next_month\n", "FROM\n", " `bigquery-public-data.ml_datasets.credit_card_default`\n", "LIMIT 61876;\n", "''').to_dataframe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Kemudian mengubah tipe data pada kolom-kolom `sex`, `education_level`, `marital_status`, `pay_5`, `pay_6`, dan `default_payment_next_month` menjadi tipe data numerik lalu menyimpan query ke dalam dataframe `df` dan diunduh menjadi csv `P1G5_Set_1_naufal.csv`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "id": "tWL01zYmIwa9", "outputId": "8fce8395-a50d-4242-dad5-9a62557a0fa4" }, "outputs": [ { "data": { "application/javascript": "\n async function download(id, filename, size) {\n if (!google.colab.kernel.accessAllowed) {\n return;\n }\n const div = document.createElement('div');\n const label = document.createElement('label');\n label.textContent = `Downloading \"${filename}\": `;\n div.appendChild(label);\n const progress = document.createElement('progress');\n progress.max = size;\n div.appendChild(progress);\n document.body.appendChild(div);\n\n const buffers = [];\n let downloaded = 0;\n\n const channel = await google.colab.kernel.comms.open(id);\n // Send a message to notify the kernel that we're ready.\n channel.send({})\n\n for await (const message of channel.messages) {\n // Send a message to notify the kernel that we're ready.\n channel.send({})\n if (message.buffers) {\n for (const buffer of message.buffers) {\n buffers.push(buffer);\n downloaded += buffer.byteLength;\n progress.value = downloaded;\n }\n }\n }\n const blob = new Blob(buffers, {type: 'application/binary'});\n const a = document.createElement('a');\n a.href = window.URL.createObjectURL(blob);\n a.download = filename;\n div.appendChild(a);\n a.click();\n div.remove();\n }\n ", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/javascript": "download(\"download_e335f945-bb38-4eb2-90a5-5e8347c7e948\", \"P1G5_Set_1_naufal.csv\", 386113)", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "df.to_csv('P1G5_Set_1_naufal.csv', index=False)\n", "files.download('P1G5_Set_1_naufal.csv')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 412 }, "id": "kWxX7lixMoA_", "outputId": "ccc4e5aa-bfde-4a4f-bd0e-b48340fe06cd" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df" }, "text/html": [ "\n", "
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2965 rows × 24 columns

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" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_0 pay_2 \\\n", "0 80000.0 1 6 1 54.0 0.0 0.0 \n", "1 200000.0 1 4 1 49.0 0.0 0.0 \n", "2 20000.0 2 6 2 22.0 0.0 0.0 \n", "3 260000.0 2 4 2 33.0 0.0 0.0 \n", "4 150000.0 1 4 2 32.0 0.0 0.0 \n", "... ... ... ... ... ... ... ... \n", "2960 80000.0 2 3 2 28.0 -1.0 -1.0 \n", "2961 50000.0 2 3 1 51.0 -1.0 -1.0 \n", "2962 450000.0 2 2 1 38.0 -2.0 -2.0 \n", "2963 50000.0 2 2 1 44.0 -2.0 -2.0 \n", "2964 290000.0 2 2 1 39.0 1.0 -2.0 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 \\\n", "0 0.0 0.0 0.0 0.0 61454.0 61808.0 62290.0 \n", "1 0.0 0.0 0.0 0.0 49221.0 49599.0 50942.0 \n", "2 0.0 0.0 0.0 0.0 19568.0 19420.0 15535.0 \n", "3 0.0 0.0 0.0 0.0 18457.0 22815.0 27086.0 \n", "4 0.0 -1.0 0.0 0.0 159919.0 68686.0 161192.0 \n", "... ... ... ... ... ... ... ... \n", "2960 -1.0 -2.0 -2.0 -2.0 4280.0 2800.0 0.0 \n", "2961 -1.0 -1.0 -2.0 -2.0 752.0 300.0 5880.0 \n", "2962 -2.0 -2.0 -2.0 -2.0 390.0 390.0 390.0 \n", "2963 -2.0 -2.0 -2.0 -2.0 1473.0 390.0 390.0 \n", "2964 -2.0 -2.0 -2.0 -2.0 -70.0 9540.0 390.0 \n", "\n", " bill_amt_4 bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 \\\n", "0 29296.0 26210.0 17643.0 2545.0 2208.0 1336.0 \n", "1 50146.0 50235.0 48984.0 1689.0 2164.0 2500.0 \n", "2 1434.0 500.0 0.0 4641.0 1019.0 900.0 \n", "3 27821.0 30767.0 29890.0 5000.0 5000.0 1137.0 \n", "4 150464.0 143375.0 146411.0 4019.0 146896.0 157436.0 \n", "... ... ... ... ... ... ... \n", "2960 0.0 0.0 0.0 2800.0 0.0 0.0 \n", "2961 0.0 0.0 0.0 300.0 5880.0 0.0 \n", "2962 390.0 390.0 390.0 390.0 780.0 390.0 \n", "2963 390.0 390.0 0.0 390.0 390.0 390.0 \n", "2964 3184.0 390.0 390.0 10000.0 800.0 3184.0 \n", "\n", " pay_amt_4 pay_amt_5 pay_amt_6 default_payment_next_month \n", "0 2232.0 542.0 348.0 1 \n", "1 3480.0 2500.0 3000.0 0 \n", "2 0.0 1500.0 0.0 1 \n", "3 5000.0 1085.0 5000.0 0 \n", "4 4600.0 4709.0 5600.0 0 \n", "... ... ... ... ... \n", "2960 0.0 0.0 0.0 0 \n", "2961 0.0 0.0 0.0 1 \n", "2962 390.0 390.0 390.0 1 \n", "2963 390.0 0.0 780.0 0 \n", "2964 390.0 390.0 6617.0 0 \n", "\n", "[2965 rows x 24 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Melakukan perintah untuk menampilkan semua kolom dari dataset.\n", "pd.set_option('display.max_columns', 500)\n", "\n", "# Melakukan data loading dari csv dataset yang akan digunakan.\n", "df = pd.read_csv(\"/Users/Naufal's/Desktop/Hacktiv8 Tugas/p1-ftds031-rmt-g5-naufalbudianto28/P1G5_Set_1_naufal.csv\")\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 3, "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 float64\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 float64\n", " 5 pay_0 2965 non-null float64\n", " 6 pay_2 2965 non-null float64\n", " 7 pay_3 2965 non-null float64\n", " 8 pay_4 2965 non-null float64\n", " 9 pay_5 2965 non-null float64\n", " 10 pay_6 2965 non-null float64\n", " 11 bill_amt_1 2965 non-null float64\n", " 12 bill_amt_2 2965 non-null float64\n", " 13 bill_amt_3 2965 non-null float64\n", " 14 bill_amt_4 2965 non-null float64\n", " 15 bill_amt_5 2965 non-null float64\n", " 16 bill_amt_6 2965 non-null float64\n", " 17 pay_amt_1 2965 non-null float64\n", " 18 pay_amt_2 2965 non-null float64\n", " 19 pay_amt_3 2965 non-null float64\n", " 20 pay_amt_4 2965 non-null float64\n", " 21 pay_amt_5 2965 non-null float64\n", " 22 pay_amt_6 2965 non-null float64\n", " 23 default_payment_next_month 2965 non-null int64 \n", "dtypes: float64(20), int64(4)\n", "memory usage: 556.1 KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 4, "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
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
mean163369.3086001.6077571.8495781.55986535.1932550.005059-0.122428-0.141653-0.185160-0.225295-0.25463752118.30522850649.15312048239.75750444089.68330540956.08060739773.0725136348.9028676.272494e+035150.4971334561.3760544913.2866785382.7015180.214165
std125030.4154720.4883330.7781840.5223179.1094391.1143951.1807841.1836301.1783221.1590031.16730572328.67054170785.00158868145.71074561907.45405658271.90475157303.48898120885.7353362.887967e+0414287.07998213281.49959916734.34077817275.9530290.410311
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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
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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
max800000.0000002.0000006.0000003.00000069.0000008.0000007.0000007.0000008.0000007.0000007.000000613860.000000512650.000000578971.000000488808.000000441981.000000436172.000000493358.0000001.227082e+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 1.849578 1.559865 \n", "std 125030.415472 0.488333 0.778184 0.522317 \n", "min 10000.000000 1.000000 0.000000 0.000000 \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 6.000000 3.000000 \n", "\n", " age pay_0 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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data telah berhasil ter-*loading* dan kemudian saya melakukan cek duplikasi data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Jumlah data terduplikasi: 1\n", " limit_balance sex education_level marital_status age pay_0 pay_2 \\\n", "2815 200000.0 2 1 1 34.0 1.0 -2.0 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 \\\n", "2815 -2.0 -2.0 -2.0 -2.0 0.0 0.0 0.0 \n", "\n", " bill_amt_4 bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 \\\n", "2815 0.0 0.0 0.0 0.0 0.0 0.0 \n", "\n", " pay_amt_4 pay_amt_5 pay_amt_6 default_payment_next_month \n", "2815 0.0 0.0 0.0 0 \n" ] } ], "source": [ "print(\"Jumlah data terduplikasi: \", df.duplicated().sum())\n", "print(df[df.duplicated()])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Jumlah data terduplikasi: 0\n" ] } ], "source": [ "df = df.drop_duplicates()\n", "print(\"Jumlah data terduplikasi: \", df.duplicated().sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data yang terduplikasi sudah berhasil dihapus." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# EDA" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pada tahap ini saya akan melakukan eksplorasi data menggunakan visualisasi sederhana dan sebagainya." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Sex Distribution\n", "\n", "Melihat distribusi `sex` pada dataset dengan visualisasi *pie chart*." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Melakukan perintah untuk membuat dua plot.\n", "plt.figure(figsize=(16, 8))\n", "\n", "# Membuat grafik pie chart untuk jenis kelamin pada dataset.\n", "sex_pie = df.groupby('sex').size()\n", "\n", "# Plotting the pie chart\n", "plt.pie(sex_pie, labels=sex_pie.index, autopct='%1.1f%%', startangle=140)\n", "plt.title('Sex Distribution')\n", "plt.axis('equal')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Penjelasan angka `1` pada dataset adalah **Laki-laki** dan `2` adalah **Perempuan**. Sehingga hasil dari data visualisasi di atas menunjukkan bahwa pada dataset ini jumlah Perempuan (61%) lebih banyak daripada Laki-laki (39%)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Age Distribution\n", "\n", "Melihat visualiasi *bar chart* terhadap distribusi `umur` pada dataset." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 6))\n", "\n", "# Membuat bar chart untuk distribusi umur pada dataset.\n", "age_bar = df['age'].value_counts().sort_index().plot(kind='bar', color='skyblue')\n", "\n", "plt.title('Age Distribution')\n", "plt.xlabel('Age')\n", "plt.ylabel('Count')\n", "plt.xticks(rotation=45)\n", "\n", "# Menampilkan nilai tiap bar.\n", "for p in age_bar.patches:\n", " age_bar.annotate(str(p.get_height()), (p.get_x() + p.get_width() / 2., p.get_height()),\n", " ha='center', va='center', xytext=(0, 5), textcoords='offset points')\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Terlihat dari hasil visualisasi data, umur pada dataset memiliki variasi yang beragam dengan umur paling muda yaitu `21 tahun `dan umur paling senior yaitu `69 tahun`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Limit Balance Distribution\n", "\n", "Melihat distribusi pada kolom `limit_balance` yang merupakan jumlah limit dari credit pengguna dengan menggunakan histogram." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Nilai minimal: 10000.0\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 6))\n", "\n", "sns.histplot(df['limit_balance'], kde=True, bins=30)\n", "plt.title('Histogram of Limit Balance')\n", "\n", "print('Nilai minimal: ', df['limit_balance'].min())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Sekilas terlihat dari hasil visualisasi data, kolom `limit_balance` memiliki distribusi `positively skewed` dan memiliki rentang variasi data yang cukup luas ($10,000 hingga $800,000 )." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic Data Correlation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pada kolom-kolom informasi dasar pengguna, saya akan mencoba untuk melakukan analisa korelasi Spearman terhadap kolom klasifikasi `default_payment_next_month`.\n", "\n", "Meskipun pada kolom `sex`, `education_level`, dan `marital_status` saya anggap adalah **hasil dari encoding**, akan tetapi karena sudah berbentuk numerik saya tetap menggunakan Spearman." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data = df[['limit_balance', 'sex', 'education_level', 'marital_status', 'age', 'default_payment_next_month']]\n", "plt.figure(figsize=(10,8))\n", "# Menggunakan metode spearman dikarenakan asumsi data masih memiliki outliers.\n", "sns.heatmap(data.corr(method='spearman'), annot=True, cmap='coolwarm', fmt=\".2f\")\n", "plt.title(\"Basic Data - Spearman Correlation\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Meskipun menunjukkan bahwa semua kolom informasi dasar pengguna memiliki korelasi dengan pembayaran bulan depan, namun kebanyakan mendekati nilai 0%. Maka jika saya menentukan batas korelasi di atas 10%, maka dari hasil visualisasi di atas hanya kolom `limit_balance` yang memiliki korelasi dengan kolom `default_payment_next_month`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Status Repayment Correlation\n", "\n", "Menghitung korelasi Spearman kolom-kolom repayment terhadap kolom pembayaran bulan depan `default_payment_next_month`." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pay = df[['pay_0', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6', 'default_payment_next_month']]\n", "plt.figure(figsize=(10,8))\n", "sns.heatmap(pay.corr(method='spearman'), annot=True, cmap='coolwarm', fmt=\".2f\")\n", "plt.title(\"Status Repayment - Spearman Correlation\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dapat disimpulkan dari data di atas bahwa semua kolom **status repayment** memiliki korelasi dengan kolom pembayaran bulan depan `default_payment_next_month`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bill Statement Correlation\n", "\n", "Melakukan analisa korelasi Spearman pada kolom-kolom bill statement terhadap kolom pembayaran bulan depan `default_payment_next_month`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bill = df[['bill_amt_1', 'bill_amt_2', 'bill_amt_3', 'bill_amt_4', 'bill_amt_5', 'bill_amt_6', 'default_payment_next_month']]\n", "plt.figure(figsize=(10,8))\n", "sns.heatmap(bill.corr(method='spearman'), annot=True, cmap='coolwarm', fmt=\".2f\")\n", "plt.title(\"Bill Statement - Spearman Correlation\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hasil analisa korelasi kolom-kolom bill statement menunjukkan korelasi yang sangat kecil (mendekati 0%) terhadap `default_payment_next_month`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Previous Payment Correlation\n", "\n", "Uji korelasi Spearman pada kolom-kolom pembayaran sebelumnya terhadap kolom pembayaran bulan depan `default_payment_next_month`." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "payamt = df[['pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5', 'pay_amt_6', 'default_payment_next_month']]\n", "plt.figure(figsize=(10,8))\n", "sns.heatmap(pay.corr(method='spearman'), annot=True, cmap='coolwarm', fmt=\".2f\")\n", "plt.title(\"Previous Payment - Spearman Correlation\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Terlihat dari hasil uji korelasi Spearman pada kolom-kolom pembayaran sebelumnya, seluruh kolom memiliki korelasi dengan kolom `default_payment_next_month`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Feature Engineering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature Selection\n", "\n", "Dari hasil uji korelasi pada tahap EDA, didapatkan bahwa kolom `sex`, `education_level`, `marital_status`, `age`, dan kolom-kolom bill statement (`bill_amt_1`, `bill_amt_2`, `bill_amt_3`, `bill_amt_4`, `bill_amt_5`, `bill_amt_6`) memiliki korelasi yang sangat lemah (mendekati 0). Akan tetapi saya akan tetap memasukkan kolom-kolom tersebut untuk dilakukan pemodelan." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Inference Saving" ] }, { "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
49950000.012232.00.00.00.00.00.00.048536.041045.041532.018646.019183.019214.03030.02124.0646.0828.01097.0612.00
103130000.022235.02.02.00.00.00.00.025368.024662.026001.027012.027241.027812.00.01739.01750.0975.01010.03226.01
1546360000.011232.0-1.0-1.0-1.0-1.00.00.027.0185.00.0617.0617.00.0185.00.0617.00.00.00.00
1496100000.021230.00.00.0-2.0-1.00.00.041150.00.00.074550.075731.075975.00.00.074550.03000.03000.03500.00
916170000.022224.00.00.00.00.00.00.064142.054347.055677.059096.050536.015851.03000.03000.05000.010000.05000.06000.00
\n", "
" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_0 pay_2 \\\n", "499 50000.0 1 2 2 32.0 0.0 0.0 \n", "1031 30000.0 2 2 2 35.0 2.0 2.0 \n", "1546 360000.0 1 1 2 32.0 -1.0 -1.0 \n", "1496 100000.0 2 1 2 30.0 0.0 0.0 \n", "916 170000.0 2 2 2 24.0 0.0 0.0 \n", "\n", " pay_3 pay_4 pay_5 pay_6 bill_amt_1 bill_amt_2 bill_amt_3 \\\n", "499 0.0 0.0 0.0 0.0 48536.0 41045.0 41532.0 \n", "1031 0.0 0.0 0.0 0.0 25368.0 24662.0 26001.0 \n", "1546 -1.0 -1.0 0.0 0.0 27.0 185.0 0.0 \n", "1496 -2.0 -1.0 0.0 0.0 41150.0 0.0 0.0 \n", "916 0.0 0.0 0.0 0.0 64142.0 54347.0 55677.0 \n", "\n", " bill_amt_4 bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 \\\n", "499 18646.0 19183.0 19214.0 3030.0 2124.0 646.0 \n", "1031 27012.0 27241.0 27812.0 0.0 1739.0 1750.0 \n", "1546 617.0 617.0 0.0 185.0 0.0 617.0 \n", "1496 74550.0 75731.0 75975.0 0.0 0.0 74550.0 \n", "916 59096.0 50536.0 15851.0 3000.0 3000.0 5000.0 \n", "\n", " pay_amt_4 pay_amt_5 pay_amt_6 default_payment_next_month \n", "499 828.0 1097.0 612.0 0 \n", "1031 975.0 1010.0 3226.0 1 \n", "1546 0.0 0.0 0.0 0 \n", "1496 3000.0 3000.0 3500.0 0 \n", "916 10000.0 5000.0 6000.0 0 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Mengambil sample data yang nanti akan digunakan pada tahap inference.\n", "data_inf = df.sample(28, random_state=3)\n", "\n", "# Menghapus index sample data dari dataset utama.\n", "df = df.drop(data_inf.index)\n", "\n", "# Menunjukkan sample data yang akan digunakan pada tahap inference.\n", "data_inf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Split\n", "\n", "Melakukan pemisahan data antara feature (X) dan target klasifikasi (y), serta pemisahan data train dan data test. " ] }, { "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_6
080000.016154.00.00.00.00.00.00.061454.061808.062290.029296.026210.017643.02545.02208.01336.02232.0542.0348.0
1200000.014149.00.00.00.00.00.00.049221.049599.050942.050146.050235.048984.01689.02164.02500.03480.02500.03000.0
220000.026222.00.00.00.00.00.00.019568.019420.015535.01434.0500.00.04641.01019.0900.00.01500.00.0
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\n", "
" ], "text/plain": [ " limit_balance sex education_level marital_status age pay_0 pay_2 \\\n", "0 80000.0 1 6 1 54.0 0.0 0.0 \n", "1 200000.0 1 4 1 49.0 0.0 0.0 \n", "2 20000.0 2 6 2 22.0 0.0 0.0 \n", "3 260000.0 2 4 2 33.0 0.0 0.0 \n", "4 150000.0 1 4 2 32.0 0.0 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 0.0 0.0 61454.0 61808.0 62290.0 29296.0 \n", "1 0.0 0.0 0.0 0.0 49221.0 49599.0 50942.0 50146.0 \n", "2 0.0 0.0 0.0 0.0 19568.0 19420.0 15535.0 1434.0 \n", "3 0.0 0.0 0.0 0.0 18457.0 22815.0 27086.0 27821.0 \n", "4 0.0 -1.0 0.0 0.0 159919.0 68686.0 161192.0 150464.0 \n", "\n", " bill_amt_5 bill_amt_6 pay_amt_1 pay_amt_2 pay_amt_3 pay_amt_4 \\\n", "0 26210.0 17643.0 2545.0 2208.0 1336.0 2232.0 \n", "1 50235.0 48984.0 1689.0 2164.0 2500.0 3480.0 \n", "2 500.0 0.0 4641.0 1019.0 900.0 0.0 \n", "3 30767.0 29890.0 5000.0 5000.0 1137.0 5000.0 \n", "4 143375.0 146411.0 4019.0 146896.0 157436.0 4600.0 \n", "\n", " pay_amt_5 pay_amt_6 \n", "0 542.0 348.0 \n", "1 2500.0 3000.0 \n", "2 1500.0 0.0 \n", "3 1085.0 5000.0 \n", "4 4709.0 5600.0 " ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X = df.drop(['default_payment_next_month'], axis = 1)\n", "y = df['default_payment_next_month']\n", "X.head()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train Size: (2055, 23)\n", "Test Size: (881, 23)\n" ] } ], "source": [ "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 28)\n", "\n", "print('Train Size: ', X_train.shape)\n", "print('Test Size: ', X_test.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data train dan data test sudah berhasil terpisahkan terhadap masing-masing feature dan target klasifikasi-nya." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Outliers Handling" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Skewness\n", "\n", "Melakukan pengecekan nilai skewness pada tiap-tiap kolom data train (X_train)." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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KolomSkewness
0limit_balance1.047687
1sex-0.458628
2education_level0.812951
3marital_status-0.021846
4age0.741800
5pay_00.894320
6pay_20.816794
7pay_30.894227
8pay_41.088285
9pay_51.043413
10pay_60.976049
11bill_amt_12.533318
12bill_amt_22.454935
13bill_amt_32.461382
14bill_amt_42.407399
15bill_amt_52.395429
16bill_amt_62.408168
17pay_amt_110.606102
18pay_amt_27.322532
19pay_amt_38.403017
20pay_amt_48.799217
21pay_amt_511.860252
22pay_amt_610.475013
\n", "
" ], "text/plain": [ " Kolom Skewness\n", "0 limit_balance 1.047687\n", "1 sex -0.458628\n", "2 education_level 0.812951\n", "3 marital_status -0.021846\n", "4 age 0.741800\n", "5 pay_0 0.894320\n", "6 pay_2 0.816794\n", "7 pay_3 0.894227\n", "8 pay_4 1.088285\n", "9 pay_5 1.043413\n", "10 pay_6 0.976049\n", "11 bill_amt_1 2.533318\n", "12 bill_amt_2 2.454935\n", "13 bill_amt_3 2.461382\n", "14 bill_amt_4 2.407399\n", "15 bill_amt_5 2.395429\n", "16 bill_amt_6 2.408168\n", "17 pay_amt_1 10.606102\n", "18 pay_amt_2 7.322532\n", "19 pay_amt_3 8.403017\n", "20 pay_amt_4 8.799217\n", "21 pay_amt_5 11.860252\n", "22 pay_amt_6 10.475013" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kolom = list(X_train.columns)\n", "\n", "list= []\n", "\n", "for col in kolom:\n", " list.append([col, X_train[col].skew()])\n", "\n", "pd.DataFrame(columns=['Kolom', 'Skewness'], data= list)" ] }, { "cell_type": "code", "execution_count": 18, "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'],\n", " dtype='object')" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "X_train.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Terlihat dari hasil analisa skewness, kolom-kolom `'limit_balance', 'pay_0', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6'` memiliki skewness moderat (|0,5| < skewness <= |1|). Pada kolom-kolom bill statement `'bill_amt_1', 'bill_amt_2', 'bill_amt_3', 'bill_amt_4', 'bill_amt_5', 'bill_amt_6'` dan kolom-kolom previous payment `'pay_amt_1', 'pay_amt_2', 'pay_amt_3', 'pay_amt_4', 'pay_amt_5','pay_amt_6'` memiliki extreme skewness (skewness > |1|)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Capping\n", "\n", "Saya **hanya** melakukan capping pada data `X_train` yang memiliki nilai skewness extreme pada kolom-kolom *bill statement* dan kolom-kolom *previous payment*, terlebih pada kolom-kolom **repayment status** ('pay_0', 'pay_2', 'pay_3', 'pay_4', 'pay_5', 'pay_6') saya asumsikan sebagai kolom hasil encoding." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Train Size: (2055, 23)\n", "Train Capped Size: (2055, 23)\n" ] } ], "source": [ "# Melakukan capping dengan metode iqr pada kolom-kolom skewness > |1| dan menggunakan threshold = 3.\n", "winsoriser = Winsorizer(capping_method='iqr',\n", " tail='both',\n", " fold=3,\n", " variables=['bill_amt_1','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', 'pay_amt_6'],\n", " missing_values='ignore')\n", "\n", "# Saya hanya melakukan capping pada X_train dan tidak pada X_test, dengan alasan ingin melihat seberapa jauh model dapat menangani outliers nanti di saat melakukan data test.\n", "X_train_capped = winsoriser.fit_transform(X_train)\n", "\n", "print('Train Size: ', X_train.shape)\n", "print('Train Capped Size: ', X_train_capped.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dataset `X_train` telah berhasil dilakukan capping dan disimapn ke dalam fungsi `X_train_capped`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Pipeline [Model Definition and Training]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pada tahap ini saya mencoba untuk melakukan proses pemodelan dan pelatihan model *Machine Learning* menggunakan pipeline. Dimana metode pemodelan yang akan saya gunakan adalah `Linear Regression`, `SVM`, dan `KNN` dengan melakukan **hyperparameter**. Serta pada tahap normalisasi (scaling) saya menggunakan metode `MinMaxScaler` dengan asumsi data masih terdapat outliers." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic Regression [Pipeline]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/miniconda3/envs/kernelenv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n", "/opt/miniconda3/envs/kernelenv/lib/python3.12/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "
GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n",
              "                                       ('logisticregression',\n",
              "                                        LogisticRegression())]),\n",
              "             param_grid={'logisticregression__C': [0.1, 0.5, 1.0, 3.0, 5.0],\n",
              "                         'logisticregression__max_iter': [100, 200, 300],\n",
              "                         'logisticregression__solver': ['liblinear', 'saga']})
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" ], "text/plain": [ "GridSearchCV(cv=5,\n", " estimator=Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n", " ('logisticregression',\n", " LogisticRegression())]),\n", " param_grid={'logisticregression__C': [0.1, 0.5, 1.0, 3.0, 5.0],\n", " 'logisticregression__max_iter': [100, 200, 300],\n", " 'logisticregression__solver': ['liblinear', 'saga']})" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Membuat pipeline dengan normalisasi MinMaxScaler dan pemodelan LogisticRegression.\n", "pipe_lr = make_pipeline(MinMaxScaler(), LogisticRegression())\n", "\n", "# Menentukan parameter hyperparameter pada LogisticRegression.\n", "lr_param_grid = {\n", " 'logisticregression__C': [0.1, 0.5, 1.0, 3.0, 5.0],\n", " 'logisticregression__solver': ['liblinear', 'saga'],\n", " 'logisticregression__max_iter': [100, 200, 300]\n", "}\n", "\n", "# Mencari parameter LogisticRegression terbaik menggunakan metode GridSearch.\n", "grid_search_lr = GridSearchCV(pipe_lr, lr_param_grid, cv=5)\n", "\n", "# Melakukan fit pemodelan pada data train.\n", "grid_lr_fit = grid_search_lr.fit(X_train, y_train)\n", "grid_lr_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SVM [Pipeline]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n",
              "                                       ('svc', SVC())]),\n",
              "             param_grid={'svc__C': [0.1, 0.5, 1.0, 3.0, 5.0],\n",
              "                         'svc__gamma': ['scale', 'auto'],\n",
              "                         'svc__kernel': ['linear', 'poly', 'rbf', 'sigmoid']})
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" ], "text/plain": [ "GridSearchCV(cv=5,\n", " estimator=Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n", " ('svc', SVC())]),\n", " param_grid={'svc__C': [0.1, 0.5, 1.0, 3.0, 5.0],\n", " 'svc__gamma': ['scale', 'auto'],\n", " 'svc__kernel': ['linear', 'poly', 'rbf', 'sigmoid']})" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Membuat pipeline dengan normalisasi MinMaxScaler dan pemodelan SVC.\n", "pipe_svm = make_pipeline(MinMaxScaler(), SVC())\n", "\n", "# Menentukan hyperparameter.\n", "svm_param_grid = {\n", " 'svc__C': [0.1, 0.5, 1.0, 3.0, 5.0],\n", " 'svc__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\n", " 'svc__gamma': ['scale', 'auto']\n", "}\n", "\n", "# Mencari parameter terbaik menggunakan metode GridSearch.\n", "grid_search_svm = GridSearchCV(pipe_svm, svm_param_grid, cv=5)\n", "\n", "# Melakukan fit pemodelan pada data train.\n", "grid_svm_fit = grid_search_svm.fit(X_train, y_train)\n", "grid_svm_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## KNN [Pipeline]" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GridSearchCV(cv=5,\n",
              "             estimator=Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n",
              "                                       ('kneighborsclassifier',\n",
              "                                        KNeighborsClassifier())]),\n",
              "             param_grid={'kneighborsclassifier__n_neighbors': [3, 5, 7, 9],\n",
              "                         'kneighborsclassifier__weights': ['uniform',\n",
              "                                                           'distance']})
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" ], "text/plain": [ "GridSearchCV(cv=5,\n", " estimator=Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n", " ('kneighborsclassifier',\n", " KNeighborsClassifier())]),\n", " param_grid={'kneighborsclassifier__n_neighbors': [3, 5, 7, 9],\n", " 'kneighborsclassifier__weights': ['uniform',\n", " 'distance']})" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Membuat pipeline dengan normalisasi MinMaxScaler dan pemodelan KNN.\n", "pipe_knn = make_pipeline(MinMaxScaler(), KNeighborsClassifier())\n", "\n", "# Menentukan hyperparameter.\n", "knn_param_grid = {\n", " 'kneighborsclassifier__n_neighbors': [3, 5, 7, 9],\n", " 'kneighborsclassifier__weights': ['uniform', 'distance']\n", "}\n", "\n", "# Mencari parameter terbaik menggunakan metode GridSearch.\n", "grid_search_knn = GridSearchCV(pipe_knn, knn_param_grid, cv=5)\n", "\n", "# Melakukan fit pemodelan pada data train.\n", "grid_knn_fit = grid_search_knn.fit(X_train, y_train)\n", "grid_knn_fit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data train telah berhasil dilakukan pelatihan pemodelan `Logistic Regression`, `SVM`, dan `KNN`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Best Model Evaluation\n", "\n", "Pada proses ini akan dilakukan evaluasi pemodelan untuk melihat parameter yang terbaik pada tiap pemodelan." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Logistic Regression" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression - Train Set Score : 0.8238442822384429\n", "Logistic Regression - Test Set Score : 0.8183881952326901\n" ] } ], "source": [ "print('Logistic Regression - Train Set Score : ', grid_lr_fit.score(X_train, y_train))\n", "print('Logistic Regression - Test Set Score : ', grid_search_lr.score(X_test, y_test))" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross-validation scores: [0.83941606 0.81265207 0.81995134 0.81995134 0.81265207]\n", "Mean cross-validation score: 0.8209245742092458\n" ] } ], "source": [ "# Melakukan cross validation.\n", "cross_val_scores = cross_val_score(grid_search_lr.best_estimator_, X_train, y_train, cv=5)\n", "print('Cross-validation scores:', cross_val_scores)\n", "print('Mean cross-validation score:', cross_val_scores.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dari hasil analisa score hasil pemodelan `Logistic Regression` didapatkan selisih yang kecil (sekitar 0,3%) antara pemodelan pada data train dan data test, maka dapat disimpulkan sedikit mengalami overfitting." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'logisticregression__C': 3.0, 'logisticregression__max_iter': 100, 'logisticregression__solver': 'liblinear'}\n", "\n", "\n", "Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n", " ('logisticregression',\n", " LogisticRegression(C=3.0, solver='liblinear'))])\n" ] } ], "source": [ "# Menampilkan parameter terbaik dari hasil pengujian.\n", "best_lr = grid_search_lr.best_params_\n", "print(best_lr)\n", "print('\\n')\n", "\n", "best_pipe_lr = grid_search_lr.best_estimator_\n", "print(best_pipe_lr)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parameter terbaik pada metode pemodelan `Logistic Regression` adalah:\n", "* C = 5\n", "* itter = 100\n", "* solver = liblinear" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.83 0.96 0.89 700\n", " 1 0.65 0.25 0.37 181\n", "\n", " accuracy 0.82 881\n", " macro avg 0.74 0.61 0.63 881\n", "weighted avg 0.80 0.82 0.79 881\n", "\n" ] } ], "source": [ "print(classification_report(y_test, best_pipe_lr.predict(X_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Secara keseluruhan hasil f1-score di atas pada metode Logistic Regression menunjukkan data klasifikasi yang tidak seimbang, 89% pada klasifikasi 0 dan 37% pada klasifikasi 1. Namun secara akurasi pemodelan ini memiliki akurasi yang cukup baik yaitu 82%. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SVM" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "SVM - Train Set Score : 0.8496350364963504\n", "SVM - Test Set Score : 0.8331441543700341\n" ] } ], "source": [ "print('SVM - Train Set Score : ', grid_svm_fit.score(X_train, y_train))\n", "print('SVM - Test Set Score : ', grid_search_svm.score(X_test, y_test))" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross-validation scores: [0.84428224 0.81265207 0.8296837 0.82725061 0.83454988]\n", "Mean cross-validation score: 0.8296836982968369\n" ] } ], "source": [ "# Melakukan cross validation.\n", "cross_val_scores2 = cross_val_score(grid_search_svm.best_estimator_, X_train, y_train, cv=5)\n", "print('Cross-validation scores:', cross_val_scores2)\n", "print('Mean cross-validation score:', cross_val_scores2.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dari hasil analisa score hasil pemodelan `SVM` didapatkan selisih sekitar 1,6% antara pemodelan pada data train dan data test, maka dapat disimpulkan sedikit mengalami overfitting." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'svc__C': 5.0, 'svc__gamma': 'scale', 'svc__kernel': 'rbf'}\n", "\n", "\n", "Pipeline(steps=[('minmaxscaler', MinMaxScaler()), ('svc', SVC(C=5.0))])\n" ] } ], "source": [ "best_svm = grid_search_svm.best_params_\n", "print(best_svm)\n", "print('\\n')\n", "\n", "best_pipe_svm = grid_search_svm.best_estimator_\n", "print(best_pipe_svm)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parameter terbaik pada metode pemodelan `SVM` adalah:\n", "* C = 5\n", "* gamma = scale\n", "* kernel = rbf" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.85 0.96 0.90 700\n", " 1 0.68 0.35 0.46 181\n", "\n", " accuracy 0.83 881\n", " macro avg 0.77 0.65 0.68 881\n", "weighted avg 0.82 0.83 0.81 881\n", "\n" ] } ], "source": [ "print(classification_report(y_test, best_pipe_svm.predict(X_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Secara keseluruhan hasil f1-score di atas pada metode SVM menunjukkan data klasifikasi yang tidak seimbang, 90% pada klasifikasi 0 dan 46% pada klasifikasi 1. Namun secara akurasi pemodelan ini memiliki akurasi yang cukup baik yaitu 83%. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## KNN" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "KNN - Train Set Score : 0.8291970802919708\n", "KNN - Test Set Score : 0.8138479001135074\n" ] } ], "source": [ "print('KNN - Train Set Score : ', grid_knn_fit.score(X_train, y_train))\n", "print('KNN - Test Set Score : ', grid_search_knn.score(X_test, y_test))" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross-validation scores: [0.84428224 0.81265207 0.8296837 0.82725061 0.83454988]\n", "Mean cross-validation score: 0.8296836982968369\n" ] } ], "source": [ "# Melakukan cross validation.\n", "cross_val_scores3 = cross_val_score(grid_search_svm.best_estimator_, X_train, y_train, cv=5)\n", "print('Cross-validation scores:', cross_val_scores3)\n", "print('Mean cross-validation score:', cross_val_scores3.mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dari hasil analisa score hasil pemodelan `KNN` didapatkan selisih sekitar 1,6% antara pemodelan pada data train dan data test, maka dapat disimpulkan sedikit mengalami overfitting." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'kneighborsclassifier__n_neighbors': 9, 'kneighborsclassifier__weights': 'uniform'}\n", "\n", "\n", "Pipeline(steps=[('minmaxscaler', MinMaxScaler()),\n", " ('kneighborsclassifier', KNeighborsClassifier(n_neighbors=9))])\n" ] } ], "source": [ "best_knn = grid_search_knn.best_params_\n", "print(best_knn)\n", "print('\\n')\n", "\n", "best_pipe_knn = grid_search_knn.best_estimator_\n", "print(best_pipe_knn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parameter terbaik pada metode pemodelan `KNN` adalah:\n", "* K = 9\n", "* weights = uniform" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.83 0.95 0.89 700\n", " 1 0.60 0.27 0.37 181\n", "\n", " accuracy 0.81 881\n", " macro avg 0.72 0.61 0.63 881\n", "weighted avg 0.79 0.81 0.78 881\n", "\n" ] } ], "source": [ "print(classification_report(y_test, best_pipe_knn.predict(X_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Secara keseluruhan hasil f1-score di atas pada metode KNN menunjukkan data klasifikasi yang tidak seimbang, 89% pada klasifikasi 0 dan 37% pada klasifikasi 1. Namun secara akurasi pemodelan ini memiliki akurasi yang cukup baik yaitu 81%. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Saving\n", "\n", "Melakukan penyimpanan metode pemodelan dengan modul pickle." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "pickle.dump(best_pipe_lr, open('best_model_lr.pkl', 'wb'))\n", "pickle.dump(best_pipe_svm, open('best_model_svm.pkl', 'wb'))\n", "pickle.dump(best_pipe_knn, open('best_model_knn.pkl', 'wb'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Inference\n", "\n", "Melakukan percobaan pemodelan terhadap data inference." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "# Model loading.\n", "best_pipe_lr = pickle.load(open('best_model_lr.pkl', 'rb'))\n", "best_pipe_svm = pickle.load(open('best_model_svm.pkl', 'rb'))\n", "best_pipe_knn = pickle.load(open('best_model_knn.pkl', 'rb'))" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression Predictions: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1]\n", "SVM Predictions: [0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1]\n", "KNN Predictions: [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1]\n" ] } ], "source": [ "# Melakukan drop target klasifikasi.\n", "data_inf_X = data_inf.drop(['default_payment_next_month'], axis = 1)\n", "\n", "# Melakukan prediksi tiap-tiap model terhadap data inference.\n", "pred_lr = best_pipe_lr.predict(data_inf_X)\n", "pred_svm = best_pipe_svm.predict(data_inf_X)\n", "pred_knn = best_pipe_knn.predict(data_inf_X)\n", "\n", "print(\"Logistic Regression Predictions:\", pred_lr)\n", "print(\"SVM Predictions:\", pred_svm)\n", "print(\"KNN Predictions:\", pred_knn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Data inference (28 baris) telah berhasil terklasifikasi, memang terlihat lebih banyak klasifikasi 0 (tidak seimbang), sesuai hasil dari evaluasi model sebelumnya." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Conclusion" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conceptual Problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Apakah yang dimaksud dengan coeficient pada logistic regression?\n", "\n", " Koefisien ($β$) pada `Logistic Regression` merupakan peranan penting dalam pemodelan, yang dimana menunjukkan korelasi antara feature dengan target pemodelan klasifikasi. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Apakah fungsi parameter kernel pada SVM? Jelaskan salah satu kernel yang kalian pahami!\n", "\n", " Kernel merupakan salah satu hyperparameter untuk transformasi dimensi pada feature, sehingga mempermudah untuk memisahkan data guna mempermudah klasifikasi. Salah satu kernel SVM yang ada adalah rbf (Gaussian Kernel), merupakan kernel yang cocok untuk data yang kompleks dan untuk kelas yang non-linear *separable*.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. Bagaimana cara memilih `K` yang optimal pada KNN ?\n", "\n", " Dengan menggunakan hyperparameter tuning pada n_neighbours metode KNN, pada hasil dari kasus ini nilai `K` yang optimal terdapat pada n_neighbours = 9." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "4. Apa yang dimaksud dengan metrics-metrics berikut : `Accuracy`, `Precision`, `Recall`, `F1 Score`, dan kapan waktu yang tepat untuk menggunakannya ?\n", "\n", " * `Accuracy`: merupakan presentase akurasi dari hasil prediksi.\n", " * `Precision`: proporsi prediksi positif yang benar dari total prediksi positif\n", " * `Recall`: proporsi prediksi positif yang benar dari total sampel positif sebenarnya.\n", " * `F1 Score`: harmonik rata-rata dari precision dan recall, memberikan keseimbangan antara keduanya.\n", "\n", " Metric-metric ini digunakan saat evaluasi model." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overall" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Hasil eksplorasi data: \n", " * Jenis kelamin Perempuan lebih banyak dibandingkan Lak-laki pada dataset.\n", " * Distribusi umur pada dataset ada pada 21 tahun hingga 69 tahun.\n", " * Hanya kolom-kolom Status Repayment dan kolom-kolom Previous Payment yang memiliki korelasi kuat dengan target klasifikasi pada kolom 'default_payment_next_month'.\n", "\n", "2. Hasil pemodelan:\n", " * Metode pemodelan Logistic Regression terbaik adalah dengan hyperparameter C = 5, itter = 100, dan solver = liblinear. Dan menghasilkan tingkat akurasi 82% dengan sedikit overfitting (0,3%).\n", " * Metode pemodelan KNN terbaik adalah dengan hyperparameter K = 9 dan weights = uniform. Menghasilan tingkat akurasi 81% dengan sedikit overfitting (1,6%).\n", " * Metode pemodelan SVM terbaik adalah dengan hyperparameter C = 5, gamma = scale, dan kernel = rbf. Menghasilkan tingkat akurasi 83% dengan sedikit overfitting (1,6%).\n" ] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python (ipykernel)", "language": "python", "name": "kernelenv" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.1.undefined" } }, "nbformat": 4, "nbformat_minor": 0 }