Day 43.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fraud Detection Using Self Organization Map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 1: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the dataset\n",
    "dataset = pd.read_csv('dataset\\Credit_Card_applications.csv')\n",
    "X = dataset.iloc [: ,:-1].values    # independent variables\n",
    "y = dataset.iloc [: , -1].values    # dependent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature Scaling\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "sc = MinMaxScaler(feature_range = (0,1))\n",
    "X = sc.fit_transform(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 2: Building The SOM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the SOM model\n",
    "from minisom import MiniSom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init the model\n",
    "som = MiniSom( x = 10, y = 10, input_len = 15, sigma = 1.0, learning_rate = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# init the weight\n",
    "som.random_weights_init(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# traing the model\n",
    "som.train_random(data = X, num_iteration = 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 3: Visualizing the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making a self organization map\n",
    "from pylab import bone, pcolor, colorbar, plot, show\n",
    "bone()\n",
    "pcolor(som.distance_map().T)\n",
    "colorbar()\n",
    "markers = ['o' , 's']\n",
    "colors = ['r', 'g']\n",
    "for i, x in enumerate(X):\n",
    "    w = som.winner(x)\n",
    "    plot(w[0] + 0.5,\n",
    "         w[1] + 0.5,\n",
    "         markers[y[i]],\n",
    "         markeredgecolor = colors[y[i]],\n",
    "         markerfacecolor = 'None',\n",
    "         markersize = 10,\n",
    "         markeredgewidth = 2)\n",
    "show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 4: Cathch the Potential Fraud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mapping the winning node\n",
    "mappings = som.win_map(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#catch the cheater\n",
    "frauds = np.concatenate((mappings[(7,8)], mappings[(3,1)], mappings[(5,1)] ) , axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rescale the value using inverse function\n",
    "frauds = sc.inverse_transform(frauds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.5571415e+07, 1.0000000e+00, 3.7580000e+01, 0.0000000e+00,\n",
       "        2.0000000e+00, 8.0000000e+00, 4.0000000e+00, 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        3.0000000e+00, 1.8400000e+02, 1.0000000e+00],\n",
       "       [1.5699340e+07, 1.0000000e+00, 2.3170000e+01, 0.0000000e+00,\n",
       "        2.0000000e+00, 8.0000000e+00, 4.0000000e+00, 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        3.0000000e+00, 1.8400000e+02, 1.0000000e+00],\n",
       "       [1.5644400e+07, 1.0000000e+00, 3.4580000e+01, 0.0000000e+00,\n",
       "        2.0000000e+00, 8.0000000e+00, 4.0000000e+00, 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        3.0000000e+00, 1.8400000e+02, 1.0000000e+00],\n",
       "       [1.5684440e+07, 1.0000000e+00, 3.3670000e+01, 2.1650000e+00,\n",
       "        2.0000000e+00, 8.0000000e+00, 4.0000000e+00, 1.5000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        3.0000000e+00, 1.2000000e+02, 1.0000000e+00],\n",
       "       [1.5648069e+07, 0.0000000e+00, 2.4500000e+01, 1.7500000e+00,\n",
       "        1.0000000e+00, 8.0000000e+00, 4.0000000e+00, 1.6500000e-01,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 1.3200000e+02, 1.0000000e+00],\n",
       "       [1.5731586e+07, 0.0000000e+00, 2.9500000e+01, 2.0000000e+00,\n",
       "        1.0000000e+00, 1.0000000e+01, 8.0000000e+00, 2.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 2.5600000e+02, 1.8000000e+01],\n",
       "       [1.5575605e+07, 0.0000000e+00, 2.4750000e+01, 1.3665000e+01,\n",
       "        2.0000000e+00, 1.1000000e+01, 8.0000000e+00, 1.5000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 2.8000000e+02, 2.0000000e+00],\n",
       "       [1.5644453e+07, 0.0000000e+00, 1.8170000e+01, 1.0000000e+01,\n",
       "        1.0000000e+00, 1.1000000e+01, 8.0000000e+00, 1.6500000e-01,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 3.4000000e+02, 1.0000000e+00],\n",
       "       [1.5694666e+07, 0.0000000e+00, 2.6500000e+01, 2.7100000e+00,\n",
       "        1.0000000e+00, 8.0000000e+00, 4.0000000e+00, 8.5000000e-02,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        1.0000000e+00, 8.0000000e+01, 1.0000000e+00],\n",
       "       [1.5646594e+07, 0.0000000e+00, 1.5920000e+01, 2.8750000e+00,\n",
       "        2.0000000e+00, 1.1000000e+01, 4.0000000e+00, 8.5000000e-02,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 1.2000000e+02, 1.0000000e+00],\n",
       "       [1.5699238e+07, 0.0000000e+00, 2.2500000e+01, 8.4600000e+00,\n",
       "        1.0000000e+00, 1.4000000e+01, 4.0000000e+00, 2.4600000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 1.6400000e+02, 1.0000000e+00],\n",
       "       [1.5593959e+07, 0.0000000e+00, 2.1750000e+01, 1.7500000e+00,\n",
       "        1.0000000e+00, 5.0000000e+00, 3.0000000e+00, 0.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 1.6000000e+02, 1.0000000e+00],\n",
       "       [1.5577771e+07, 0.0000000e+00, 2.9500000e+01, 1.0850000e+00,\n",
       "        1.0000000e+00, 1.4000000e+01, 4.0000000e+00, 1.0000000e+00,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 2.8000000e+02, 1.4000000e+01],\n",
       "       [1.5599272e+07, 0.0000000e+00, 3.3670000e+01, 3.7500000e-01,\n",
       "        2.0000000e+00, 1.3000000e+01, 4.0000000e+00, 3.7500000e-01,\n",
       "        0.0000000e+00, 0.0000000e+00, 0.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 3.0000000e+02, 4.5000000e+01],\n",
       "       [1.5704581e+07, 1.0000000e+00, 2.3750000e+01, 4.1500000e-01,\n",
       "        1.0000000e+00, 8.0000000e+00, 4.0000000e+00, 4.0000000e-02,\n",
       "        0.0000000e+00, 1.0000000e+00, 2.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 1.2800000e+02, 7.0000000e+00],\n",
       "       [1.5725776e+07, 1.0000000e+00, 2.3080000e+01, 0.0000000e+00,\n",
       "        2.0000000e+00, 4.0000000e+00, 4.0000000e+00, 1.0000000e+00,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.1000000e+01, 0.0000000e+00,\n",
       "        1.0000000e+00, 0.0000000e+00, 1.0000000e+00],\n",
       "       [1.5697460e+07, 1.0000000e+00, 2.2000000e+01, 7.9000000e-01,\n",
       "        2.0000000e+00, 9.0000000e+00, 4.0000000e+00, 2.9000000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 4.2000000e+02, 2.8400000e+02],\n",
       "       [1.5737998e+07, 1.0000000e+00, 1.8830000e+01, 4.1500000e-01,\n",
       "        1.0000000e+00, 8.0000000e+00, 4.0000000e+00, 1.6500000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 2.0000000e+02, 2.0000000e+00],\n",
       "       [1.5586183e+07, 1.0000000e+00, 1.7080000e+01, 2.5000000e-01,\n",
       "        2.0000000e+00, 1.1000000e+01, 4.0000000e+00, 3.3500000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 4.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 1.6000000e+02, 9.0000000e+00],\n",
       "       [1.5718921e+07, 1.0000000e+00, 1.6330000e+01, 2.7500000e+00,\n",
       "        2.0000000e+00, 6.0000000e+00, 4.0000000e+00, 6.6500000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 8.0000000e+01, 2.2000000e+01],\n",
       "       [1.5607988e+07, 1.0000000e+00, 3.8420000e+01, 7.0500000e-01,\n",
       "        2.0000000e+00, 8.0000000e+00, 4.0000000e+00, 3.7500000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 2.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 2.2500000e+02, 5.0100000e+02],\n",
       "       [1.5683993e+07, 1.0000000e+00, 1.6000000e+01, 3.1250000e+00,\n",
       "        2.0000000e+00, 9.0000000e+00, 4.0000000e+00, 8.5000000e-02,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 0.0000000e+00, 7.0000000e+00],\n",
       "       [1.5797767e+07, 1.0000000e+00, 2.5170000e+01, 3.0000000e+00,\n",
       "        2.0000000e+00, 8.0000000e+00, 4.0000000e+00, 1.2500000e+00,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 0.0000000e+00, 2.3000000e+01],\n",
       "       [1.5744044e+07, 1.0000000e+00, 2.3250000e+01, 1.2625000e+01,\n",
       "        2.0000000e+00, 8.0000000e+00, 4.0000000e+00, 1.2500000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 2.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 0.0000000e+00, 5.5530000e+03],\n",
       "       [1.5632010e+07, 1.0000000e+00, 3.5750000e+01, 2.4150000e+00,\n",
       "        2.0000000e+00, 9.0000000e+00, 4.0000000e+00, 1.2500000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 2.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 2.2000000e+02, 2.0000000e+00],\n",
       "       [1.5804235e+07, 1.0000000e+00, 2.9500000e+01, 5.8000000e-01,\n",
       "        2.0000000e+00, 9.0000000e+00, 4.0000000e+00, 2.9000000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 3.4000000e+02, 2.8040000e+03],\n",
       "       [1.5627365e+07, 1.0000000e+00, 2.4420000e+01, 2.0000000e+00,\n",
       "        2.0000000e+00, 1.0000000e+01, 2.0000000e+00, 1.6500000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 2.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 3.2000000e+02, 1.3010000e+03],\n",
       "       [1.5700300e+07, 1.0000000e+00, 1.7580000e+01, 1.0000000e+01,\n",
       "        2.0000000e+00, 9.0000000e+00, 8.0000000e+00, 1.6500000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 1.2000000e+02, 2.0000000e+00],\n",
       "       [1.5791944e+07, 1.0000000e+00, 1.9420000e+01, 7.2500000e+00,\n",
       "        2.0000000e+00, 7.0000000e+00, 4.0000000e+00, 4.0000000e-02,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 1.0000000e+02, 2.0000000e+00],\n",
       "       [1.5576680e+07, 1.0000000e+00, 2.4080000e+01, 8.7500000e-01,\n",
       "        2.0000000e+00, 7.0000000e+00, 4.0000000e+00, 8.5000000e-02,\n",
       "        0.0000000e+00, 1.0000000e+00, 4.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 2.5400000e+02, 1.9510000e+03],\n",
       "       [1.5620570e+07, 1.0000000e+00, 2.5420000e+01, 5.4000000e-01,\n",
       "        2.0000000e+00, 9.0000000e+00, 4.0000000e+00, 1.6500000e-01,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        2.0000000e+00, 2.7200000e+02, 4.4500000e+02],\n",
       "       [1.5592412e+07, 1.0000000e+00, 4.1000000e+01, 4.0000000e-02,\n",
       "        2.0000000e+00, 1.0000000e+01, 4.0000000e+00, 4.0000000e-02,\n",
       "        0.0000000e+00, 1.0000000e+00, 1.0000000e+00, 0.0000000e+00,\n",
       "        1.0000000e+00, 5.6000000e+02, 1.0000000e+00]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frauds"
   ]
  }
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