{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4da06864", "metadata": { "id": "4da06864" }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "id": "af4785ce", "metadata": { "id": "af4785ce" }, "outputs": [], "source": [ "data=pd.read_csv('Cybercrime.csv')" ] }, { "cell_type": "code", "execution_count": 3, "id": "a4c26bbe", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 330 }, "id": "a4c26bbe", "outputId": "3b3c5c4f-1984-4ef4-878d-f0f812ec0753" }, "outputs": [ { "data": { "text/html": [ "
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CardYearMonthDayAmountTransactionMCCIs Fraud?
00200291134.0925300No
1020029138.4825411No
20200292120.3425411No
30200292128.9525651No
40200293104.7125912No
\n", "
" ], "text/plain": [ " Card Year Month Day Amount Transaction MCC Is Fraud?\n", "0 0 2002 9 1 134.09 2 5300 No\n", "1 0 2002 9 1 38.48 2 5411 No\n", "2 0 2002 9 2 120.34 2 5411 No\n", "3 0 2002 9 2 128.95 2 5651 No\n", "4 0 2002 9 3 104.71 2 5912 No" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 4, "id": "baa8dc96", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "baa8dc96", "outputId": "fa8dc29f-80d7-4249-b38a-fc11961d1da8" }, "outputs": [ { "data": { "text/plain": [ "(19963, 8)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "code", "execution_count": 5, "id": "1e409ca9", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1e409ca9", "outputId": "96aaf709-2133-4e31-a3b1-b3e09bffc596" }, "outputs": [ { "data": { "text/plain": [ "159704" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.size" ] }, { "cell_type": "code", "execution_count": 6, "id": "6ae0dbf6", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6ae0dbf6", "outputId": "52b55758-eb71-4c22-cf8a-4bae8d8407c9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 19963 entries, 0 to 19962\n", "Data columns (total 8 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Card 19963 non-null int64 \n", " 1 Year 19963 non-null int64 \n", " 2 Month 19963 non-null int64 \n", " 3 Day 19963 non-null int64 \n", " 4 Amount 19963 non-null float64\n", " 5 Transaction 19963 non-null int64 \n", " 6 MCC 19963 non-null int64 \n", " 7 Is Fraud? 19963 non-null object \n", "dtypes: float64(1), int64(6), object(1)\n", "memory usage: 1.2+ MB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "code", "execution_count": 7, "id": "9f627fb8", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9f627fb8", "outputId": "88213f7b-e9ed-41cc-a790-254f9e91f518" }, "outputs": [ { "data": { "text/plain": [ "Card 0\n", "Year 0\n", "Month 0\n", "Day 0\n", "Amount 0\n", "Transaction 0\n", "MCC 0\n", "Is Fraud? 0\n", "dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 8, "id": "e3b8aeb4", "metadata": { "id": "e3b8aeb4" }, "outputs": [], "source": [ "# Kmeans clustering\n" ] }, { "cell_type": "code", "execution_count": 9, "id": "00eded7d", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "00eded7d", "outputId": "920015ee-7b7f-4d2e-8606-ce2ba593b44e" }, "outputs": [ { "data": { "text/plain": [ "array([0, 1])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Import label encoder\n", "from sklearn import preprocessing\n", "\n", "# label_encoder object knows\n", "# how to understand word labels.\n", "label_encoder = preprocessing.LabelEncoder()\n", "\n", "# Encode labels in column 'species'.\n", "data['Is Fraud?']= label_encoder.fit_transform(data['Is Fraud?'])\n", "\n", "data['Is Fraud?'].unique()\n" ] }, { "cell_type": "code", "execution_count": 10, "id": "23cc8beb", "metadata": { "id": "23cc8beb" }, "outputs": [], "source": [ "X = data.iloc[:, [1,6,7]].values" ] }, { "cell_type": "code", "execution_count": 11, "id": "a75c408b", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "a75c408b", "outputId": "b9924f10-c8a0-4fce-efea-8e1b3442bb95" }, "outputs": [], "source": [ "from sklearn.cluster import KMeans\n", "kmeans = KMeans(n_clusters = 2, init = 'k-means++', random_state = 42)\n", "y_kmeans = kmeans.fit_predict(X)" ] }, { "cell_type": "code", "execution_count": 12, "id": "db9a4623", "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "db9a4623", "outputId": "be83dc26-9c1c-4160-bdf2-cc86886da30c" }, "outputs": [ { "data": { "text/plain": [ "array([0, 0, 0, ..., 0, 0, 0])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y_kmeans" ] }, { "cell_type": "code", "execution_count": 20, "id": "d194406b", "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "d194406b", "outputId": "df61ec37-a3ec-4212-bf80-8a033156caa2" }, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Fraud')\n", "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Not')\n", "\n", "plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s = 300, c = 'yellow', label = 'Centroids')\n", "plt.title('Clusters of cyber crime')\n", "plt.xlabel('Year')\n", "plt.ylabel('Fraud')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "dcfY3zcX8gyw", "metadata": { "id": "dcfY3zcX8gyw" }, "outputs": [], "source": [] } ], "metadata": { "colab": { "provenance": [] }, "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }