{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "taruma_udemy_som_megacasestudy.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "OlNuDJ9ea3ps",
"colab_type": "text"
},
"source": [
"# Self-Organizing Map (SOM) - Mega Case Study\n",
"\n",
"Notebook ini berdasarkan kursus __Deep Learning A-Z™: Hands-On Artificial Neural Networks__ di Udemy. [Lihat Kursus](https://www.udemy.com/deeplearning/).\n",
"\n",
"## Informasi Notebook\n",
"- __notebook name__: `taruma_udemy_som_megacasestudy`\n",
"- __notebook version/date__: `1.0.0`/`20190729`\n",
"- __notebook server__: Google Colab\n",
"- __python version__: `3.6`\n",
"- __keras version__: `2.2.4`"
]
},
{
"cell_type": "code",
"metadata": {
"id": "FfiuZ-Tla3o0",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
},
"outputId": "89fc38c9-2c45-40c9-a517-bae298e491ed"
},
"source": [
"#### NOTEBOOK DESCRIPTION\n",
"\n",
"from datetime import datetime\n",
"\n",
"NOTEBOOK_TITLE = 'taruma_udemy_som_megacasestudy'\n",
"NOTEBOOK_VERSION = '1.0.0'\n",
"NOTEBOOK_DATE = 1 # Set 1, if you want add date classifier\n",
"\n",
"NOTEBOOK_NAME = \"{}_{}\".format(\n",
" NOTEBOOK_TITLE, \n",
" NOTEBOOK_VERSION.replace('.','_')\n",
")\n",
"PROJECT_NAME = \"{}_{}{}\".format(\n",
" NOTEBOOK_TITLE, \n",
" NOTEBOOK_VERSION.replace('.','_'), \n",
" \"_\" + datetime.utcnow().strftime(\"%Y%m%d_%H%M\") if NOTEBOOK_DATE else \"\"\n",
")\n",
"\n",
"print(f\"Nama Notebook: {NOTEBOOK_NAME}\")\n",
"print(f\"Nama Proyek: {PROJECT_NAME}\")"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Nama Notebook: taruma_udemy_som_megacasestudy_1_0_0\n",
"Nama Proyek: taruma_udemy_som_megacasestudy_1_0_0_20190729_1110\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "PcYS4RYgbQnS",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 68
},
"outputId": "2a5ab51d-57c5-4894-9991-d5c70cb0ff05"
},
"source": [
"#### System Version\n",
"import sys\n",
"import keras\n",
"print(\"versi python: {}\".format(sys.version))\n",
"print(\"versi keras: {}\".format(keras.__version__))"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
"versi python: 3.6.8 (default, Jan 14 2019, 11:02:34) \n",
"[GCC 8.0.1 20180414 (experimental) [trunk revision 259383]]\n",
"versi keras: 2.2.4\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LNmmcdjrhUtH",
"colab_type": "code",
"colab": {}
},
"source": [
"#### Load Notebook Extensions\n",
"%load_ext google.colab.data_table"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VE338N4Ff2fN",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 459
},
"outputId": "06155ced-fd26-4646-fe41-5078f471c8cc"
},
"source": [
"#### Download dataset\n",
"!wget -O SOM_megacase.zip \"https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P16-Mega-Case-Study.zip\"\n",
"!unzip SOM_megacase.zip"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"--2019-07-29 11:33:46-- https://sds-platform-private.s3-us-east-2.amazonaws.com/uploads/P16-Mega-Case-Study.zip\n",
"Resolving sds-platform-private.s3-us-east-2.amazonaws.com (sds-platform-private.s3-us-east-2.amazonaws.com)... 52.219.100.144\n",
"Connecting to sds-platform-private.s3-us-east-2.amazonaws.com (sds-platform-private.s3-us-east-2.amazonaws.com)|52.219.100.144|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 20790 (20K) [application/zip]\n",
"Saving to: ‘SOM_megacase.zip’\n",
"\n",
"\rSOM_megacase.zip 0%[ ] 0 --.-KB/s \rSOM_megacase.zip 100%[===================>] 20.30K --.-KB/s in 0.02s \n",
"\n",
"2019-07-29 11:33:46 (897 KB/s) - ‘SOM_megacase.zip’ saved [20790/20790]\n",
"\n",
"Archive: SOM_megacase.zip\n",
" creating: Mega_Case_Study/\n",
" inflating: Mega_Case_Study/.DS_Store \n",
" creating: __MACOSX/\n",
" creating: __MACOSX/Mega_Case_Study/\n",
" inflating: __MACOSX/Mega_Case_Study/._.DS_Store \n",
" inflating: Mega_Case_Study/ann.py \n",
" inflating: __MACOSX/Mega_Case_Study/._ann.py \n",
" inflating: Mega_Case_Study/Credit_Card_Applications.csv \n",
" inflating: __MACOSX/Mega_Case_Study/._Credit_Card_Applications.csv \n",
" inflating: Mega_Case_Study/mega_case_study.py \n",
" inflating: __MACOSX/Mega_Case_Study/._mega_case_study.py \n",
" inflating: Mega_Case_Study/minisom.py \n",
" inflating: Mega_Case_Study/som.py \n",
" inflating: __MACOSX/Mega_Case_Study/._som.py \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "IZzmjHbyf2eh",
"colab_type": "code",
"colab": {}
},
"source": [
"#### Atur dataset path\n",
"DATASET_DIRECTORY = 'Mega_Case_Study/'"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "tT5MxXupgyYz",
"colab_type": "text"
},
"source": [
"# STEP 1-2"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tiE5lhfhglh6",
"colab_type": "code",
"colab": {}
},
"source": [
"# Mega Case Study - Make a Hybrid Deep Learning Model\n",
"# Part 1 - Identifying the Frauds with the Self-Organizing Map"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jqQcvGUchNQx",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"outputId": "d9be8d84-9c91-4dcf-89f4-24aff6aa1994"
},
"source": [
"# Importing the libraries\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# Importing the dataset\n",
"# Dataset http://archive.ics.uci.edu/ml/datasets/statlog+(australian+credit+approval)\n",
"dataset = pd.read_csv(DATASET_DIRECTORY + 'Credit_Card_Applications.csv')\n",
"dataset"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
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\n",
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\n",
" \n",
" | 666 | \n",
" 15730150 | \n",
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" 0 | \n",
"
\n",
" \n",
" | 667 | \n",
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\n",
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\n",
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\n",
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" 1 | \n",
" 1 | \n",
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\n",
" \n",
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" 1 | \n",
" 1 | \n",
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\n",
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" 15615296 | \n",
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" 2 | \n",
" 1 | \n",
" 1 | \n",
" 0.000 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 0 | \n",
" 51 | \n",
" 0 | \n",
"
\n",
" \n",
" | 673 | \n",
" 15699294 | \n",
" 1 | \n",
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" 12.335 | \n",
" 2 | \n",
" 11 | \n",
" 8 | \n",
" 1.585 | \n",
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" 1 | \n",
" 1 | \n",
"
\n",
" \n",
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" 15788634 | \n",
" 0 | \n",
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" 0 | \n",
" 1 | \n",
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" 1 | \n",
" 1 | \n",
"
\n",
" \n",
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" 0 | \n",
" 1 | \n",
" 2 | \n",
" 0 | \n",
" 18 | \n",
" 0 | \n",
"
\n",
" \n",
" | 676 | \n",
" 15618258 | \n",
" 0 | \n",
" 22.75 | \n",
" 6.165 | \n",
" 2 | \n",
" 6 | \n",
" 4 | \n",
" 0.165 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 220 | \n",
" 1001 | \n",
" 0 | \n",
"
\n",
" \n",
" | 677 | \n",
" 15722535 | \n",
" 1 | \n",
" 23.00 | \n",
" 0.750 | \n",
" 2 | \n",
" 7 | \n",
" 4 | \n",
" 0.500 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
" 320 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 678 | \n",
" 15711977 | \n",
" 1 | \n",
" 25.67 | \n",
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" 8 | \n",
" 4 | \n",
" 1.500 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 2 | \n",
" 160 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 679 | \n",
" 15690169 | \n",
" 1 | \n",
" 48.58 | \n",
" 0.205 | \n",
" 1 | \n",
" 4 | \n",
" 4 | \n",
" 0.250 | \n",
" 1 | \n",
" 1 | \n",
" 11 | \n",
" 0 | \n",
" 2 | \n",
" 380 | \n",
" 2733 | \n",
" 1 | \n",
"
\n",
" \n",
" | 680 | \n",
" 15790689 | \n",
" 1 | \n",
" 21.17 | \n",
" 0.000 | \n",
" 2 | \n",
" 8 | \n",
" 4 | \n",
" 0.500 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 681 | \n",
" 15665181 | \n",
" 1 | \n",
" 35.25 | \n",
" 16.500 | \n",
" 1 | \n",
" 8 | \n",
" 4 | \n",
" 4.000 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 80 | \n",
" 1 | \n",
" 0 | \n",
"
\n",
" \n",
" | 682 | \n",
" 15633608 | \n",
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" 22.92 | \n",
" 11.585 | \n",
" 2 | \n",
" 13 | \n",
" 4 | \n",
" 0.040 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 80 | \n",
" 1350 | \n",
" 1 | \n",
"
\n",
" \n",
" | 683 | \n",
" 15805261 | \n",
" 0 | \n",
" 48.17 | \n",
" 1.335 | \n",
" 2 | \n",
" 3 | \n",
" 7 | \n",
" 0.335 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 0 | \n",
" 121 | \n",
" 0 | \n",
"
\n",
" \n",
" | 684 | \n",
" 15740356 | \n",
" 1 | \n",
" 43.00 | \n",
" 0.290 | \n",
" 1 | \n",
" 13 | \n",
" 8 | \n",
" 1.750 | \n",
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" 8 | \n",
" 0 | \n",
" 2 | \n",
" 100 | \n",
" 376 | \n",
" 1 | \n",
"
\n",
" \n",
" | 685 | \n",
" 15808223 | \n",
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" 4 | \n",
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" 0 | \n",
" 2 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
" | 686 | \n",
" 15769980 | \n",
" 1 | \n",
" 20.67 | \n",
" 0.415 | \n",
" 2 | \n",
" 8 | \n",
" 4 | \n",
" 0.125 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 0 | \n",
" 45 | \n",
" 0 | \n",
"
\n",
" \n",
" | 687 | \n",
" 15675450 | \n",
" 0 | \n",
" 18.83 | \n",
" 9.540 | \n",
" 2 | \n",
" 6 | \n",
" 4 | \n",
" 0.085 | \n",
" 1 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 2 | \n",
" 100 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
" | 688 | \n",
" 15776494 | \n",
" 0 | \n",
" 27.42 | \n",
" 14.500 | \n",
" 2 | \n",
" 14 | \n",
" 8 | \n",
" 3.085 | \n",
" 1 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
" 2 | \n",
" 120 | \n",
" 12 | \n",
" 1 | \n",
"
\n",
" \n",
" | 689 | \n",
" 15592412 | \n",
" 1 | \n",
" 41.00 | \n",
" 0.040 | \n",
" 2 | \n",
" 10 | \n",
" 4 | \n",
" 0.040 | \n",
" 0 | \n",
" 1 | \n",
" 1 | \n",
" 0 | \n",
" 1 | \n",
" 560 | \n",
" 1 | \n",
" 1 | \n",
"
\n",
" \n",
"
\n",
"
690 rows × 16 columns
\n",
"
"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Gmd04TIvhj_V",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 301
},
"outputId": "f83a7313-3af9-43c3-8d43-ec23f52a16f5"
},
"source": [
"# Finding the frauds\n",
"mappings = som.win_map(X)\n",
"frauds = np.concatenate((mappings[(3, 1)], mappings[(5, 3)]), axis=0)\n",
"frauds = sc.inverse_transform(frauds)\n",
"pd.DataFrame(frauds)"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.module+javascript": "\n import \"https://ssl.gstatic.com/colaboratory/data_table/81868506e94e6988/data_table.js\";\n\n window.createDataTable({\n data: [[{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 15790254,\n 'f': \"15790254\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 18.92,\n 'f': \"18.92\",\n },\n{\n 'v': 9,\n 'f': \"9\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 0.75,\n 'f': \"0.75\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 88,\n 'f': \"88\",\n },\n{\n 'v': 592,\n 'f': \"592\",\n }],\n [{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 15768600,\n 'f': \"15768600\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 22.42,\n 'f': \"22.42\",\n },\n{\n 'v': 5.665,\n 'f': \"5.665\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 11,\n 'f': \"11\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 2.585,\n 'f': \"2.585\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 7,\n 'f': \"7\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 129,\n 'f': \"129\",\n },\n{\n 'v': 3258,\n 'f': \"3258\",\n }],\n [{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 15748552,\n 'f': \"15748552\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 30.669999999999998,\n 'f': \"30.669999999999998\",\n },\n{\n 'v': 12,\n 'f': \"12\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 220,\n 'f': \"220\",\n },\n{\n 'v': 20,\n 'f': \"20\",\n }],\n [{\n 'v': 3,\n 'f': \"3\",\n },\n{\n 'v': 15757467,\n 'f': \"15757467\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 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'v': 5,\n 'f': \"5\",\n },\n{\n 'v': 15801441,\n 'f': \"15801441\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 35.75,\n 'f': \"35.75\",\n },\n{\n 'v': 0.9149999999999999,\n 'f': \"0.9149999999999999\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 0.75,\n 'f': \"0.75\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 1583.9999999999998,\n 'f': \"1583.9999999999998\",\n }],\n [{\n 'v': 6,\n 'f': \"6\",\n },\n{\n 'v': 15815443,\n 'f': \"15815443\",\n },\n{\n 'v': 0,\n 'f': \"0\",\n },\n{\n 'v': 57.08,\n 'f': \"57.08\",\n },\n{\n 'v': 19.5,\n 'f': \"19.5\",\n },\n{\n 'v': 2,\n 'f': \"2\",\n },\n{\n 'v': 8,\n 'f': \"8\",\n },\n{\n 'v': 4,\n 'f': \"4\",\n },\n{\n 'v': 5.5,\n 'f': \"5.5\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 1,\n 'f': \"1\",\n },\n{\n 'v': 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" \n",
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\n",
"
690 rows × 2 columns
\n",
"