Anomaly_Detection_Fraud_Detection.ipynb

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    "colab": {
      "name": "Anomaly_Detection_Fraud_Detection.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpZbjBOx_M9R"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.preprocessing import normalize"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "9Uc_yn0v_dv7",
        "outputId": "b5bb622a-37e6-4f00-cf7a-2f2284572971"
      },
      "source": [
        "df = pd.read_csv('CC GENERAL.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CUST_ID</th>\n",
              "      <th>BALANCE</th>\n",
              "      <th>BALANCE_FREQUENCY</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>ONEOFF_PURCHASES</th>\n",
              "      <th>INSTALLMENTS_PURCHASES</th>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <th>PURCHASES_FREQUENCY</th>\n",
              "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
              "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_TRX</th>\n",
              "      <th>PURCHASES_TRX</th>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>MINIMUM_PAYMENTS</th>\n",
              "      <th>PRC_FULL_PAYMENT</th>\n",
              "      <th>TENURE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C10001</td>\n",
              "      <td>40.900749</td>\n",
              "      <td>0.818182</td>\n",
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              "      <td>1000.0</td>\n",
              "      <td>201.802084</td>\n",
              "      <td>139.509787</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C10002</td>\n",
              "      <td>3202.467416</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6442.945483</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>4103.032597</td>\n",
              "      <td>1072.340217</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>1.000000</td>\n",
              "      <td>773.17</td>\n",
              "      <td>773.17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>622.066742</td>\n",
              "      <td>627.284787</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C10004</td>\n",
              "      <td>1666.670542</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>1499.00</td>\n",
              "      <td>1499.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>205.788017</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C10005</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>16.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>678.334763</td>\n",
              "      <td>244.791237</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "  CUST_ID      BALANCE  ...  PRC_FULL_PAYMENT  TENURE\n",
              "0  C10001    40.900749  ...          0.000000      12\n",
              "1  C10002  3202.467416  ...          0.222222      12\n",
              "2  C10003  2495.148862  ...          0.000000      12\n",
              "3  C10004  1666.670542  ...          0.000000      12\n",
              "4  C10005   817.714335  ...          0.000000      12\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WUg4c2L1_k_C"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EHKixwvo_rU2"
      },
      "source": [
        "X=df.drop('CUST_ID',axis=1)\n",
        "X.fillna(method='ffill',inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bccqZlyI_6UH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "outputId": "1799823a-3fae-453e-af0e-7e1cb74ed6dc"
      },
      "source": [
        "scaler= StandardScaler()\n",
        "X=scaler.fit_transform(X)\n",
        "X_new=pd.DataFrame(normalize(X))\n",
        "X_new.head(4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>11</th>\n",
              "      <th>12</th>\n",
              "      <th>13</th>\n",
              "      <th>14</th>\n",
              "      <th>15</th>\n",
              "      <th>16</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.312038</td>\n",
              "      <td>-0.106331</td>\n",
              "      <td>-0.181129</td>\n",
              "      <td>-0.152156</td>\n",
              "      <td>-0.148808</td>\n",
              "      <td>-0.198985</td>\n",
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              "      <td>-0.130160</td>\n",
              "      <td>-0.224036</td>\n",
              "      <td>0.153753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.219928</td>\n",
              "      <td>0.037539</td>\n",
              "      <td>-0.131224</td>\n",
              "      <td>-0.099751</td>\n",
              "      <td>-0.127038</td>\n",
              "      <td>0.728177</td>\n",
              "      <td>-0.341439</td>\n",
              "      <td>-0.189662</td>\n",
              "      <td>-0.256269</td>\n",
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              "      <td>0.030762</td>\n",
              "      <td>-0.165387</td>\n",
              "      <td>0.192455</td>\n",
              "      <td>0.228782</td>\n",
              "      <td>0.024353</td>\n",
              "      <td>0.065458</td>\n",
              "      <td>0.100798</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.126684</td>\n",
              "      <td>0.146785</td>\n",
              "      <td>-0.030505</td>\n",
              "      <td>0.030851</td>\n",
              "      <td>-0.128792</td>\n",
              "      <td>-0.132251</td>\n",
              "      <td>0.359775</td>\n",
              "      <td>0.757449</td>\n",
              "      <td>-0.259805</td>\n",
              "      <td>-0.191342</td>\n",
              "      <td>-0.134881</td>\n",
              "      <td>-0.030888</td>\n",
              "      <td>0.234046</td>\n",
              "      <td>-0.108741</td>\n",
              "      <td>-0.028363</td>\n",
              "      <td>-0.148900</td>\n",
              "      <td>0.102189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.020571</td>\n",
              "      <td>-0.426063</td>\n",
              "      <td>0.097223</td>\n",
              "      <td>0.228832</td>\n",
              "      <td>-0.190449</td>\n",
              "      <td>-0.154451</td>\n",
              "      <td>-0.424878</td>\n",
              "      <td>-0.167299</td>\n",
              "      <td>-0.384185</td>\n",
              "      <td>-0.108474</td>\n",
              "      <td>-0.138062</td>\n",
              "      <td>-0.231084</td>\n",
              "      <td>0.346093</td>\n",
              "      <td>-0.250827</td>\n",
              "      <td>-0.041942</td>\n",
              "      <td>-0.220185</td>\n",
              "      <td>0.151110</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         0         1         2   ...        14        15        16\n",
              "0 -0.312038 -0.106331 -0.181129  ... -0.130160 -0.224036  0.153753\n",
              "1  0.219928  0.037539 -0.131224  ...  0.024353  0.065458  0.100798\n",
              "2  0.126684  0.146785 -0.030505  ... -0.028363 -0.148900  0.102189\n",
              "3  0.020571 -0.426063  0.097223  ... -0.041942 -0.220185  0.151110\n",
              "\n",
              "[4 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "W2D-4BZmAPnx",
        "outputId": "00e53b60-c0e5-466b-8073-45542a3f579e"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "pca = PCA(n_components=2)\n",
        "X_new=pca.fit_transform(X_new)\n",
        "X_new = pd.DataFrame(X_new)\n",
        "X_new.columns='C1','C2'\n",
        "X_new.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>C1</th>\n",
              "      <th>C2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.489949</td>\n",
              "      <td>-0.679976</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.519099</td>\n",
              "      <td>0.544827</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.330633</td>\n",
              "      <td>0.268880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.481657</td>\n",
              "      <td>-0.097611</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.563512</td>\n",
              "      <td>-0.482505</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         C1        C2\n",
              "0 -0.489949 -0.679976\n",
              "1 -0.519099  0.544827\n",
              "2  0.330633  0.268880\n",
              "3 -0.481657 -0.097611\n",
              "4 -0.563512 -0.482505"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "umo2pPW0BOen",
        "outputId": "cdb71403-ff78-42bd-d936-cd6d237326cc"
      },
      "source": [
        "from sklearn.mixture import GaussianMixture\n",
        "gm = GaussianMixture(n_components=3,n_init=10)\n",
        "gm.fit(X_new)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianMixture(n_components=3, n_init=10)"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBKRbrjDBmYO",
        "outputId": "63c32a7a-b257-4361-f680-ad22e324b2ed"
      },
      "source": [
        "gm.weights_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.22270354, 0.34844001, 0.42885645])"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ItgBfRqlBwpf",
        "outputId": "c003628e-06e9-4373-a76a-96a73c626d55"
      },
      "source": [
        "gm.means_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.36127408,  0.52994364],\n",
              "       [-0.42239485, -0.30477819],\n",
              "       [ 0.53079833, -0.02756962]])"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Azt-9YAtB2XR",
        "outputId": "78eb1064-730d-44a2-b5bc-c0b7f8e67eb5"
      },
      "source": [
        "gm.covariances_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[ 0.10139434,  0.03542391],\n",
              "        [ 0.03542391,  0.06488322]],\n",
              "\n",
              "       [[ 0.07770304, -0.03147434],\n",
              "        [-0.03147434,  0.05484877]],\n",
              "\n",
              "       [[ 0.05205846,  0.01663584],\n",
              "        [ 0.01663584,  0.1190574 ]]])"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N60XBlFtCDKi",
        "outputId": "ef7c2157-2295-4817-b379-6ceb295dfbc0"
      },
      "source": [
        "gm.converged_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nTcYGDPKB8G6",
        "outputId": "f177a9e0-0b80-47cc-a545-6fc6ca2d8842"
      },
      "source": [
        "labels=gm.predict(X_new)\n",
        "labels"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 2, ..., 2, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K-Txu1Tv8gGa",
        "outputId": "cfcbd1a4-94ff-465b-9b60-26aa49f2cb27"
      },
      "source": [
        "gm.predict_proba(X_new)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[7.94998229e-06, 9.99809821e-01, 1.82228674e-04],\n",
              "       [9.99087563e-01, 9.10981139e-04, 1.45605889e-06],\n",
              "       [1.19578933e-02, 7.61331875e-06, 9.88034493e-01],\n",
              "       ...,\n",
              "       [4.61727875e-05, 9.65434240e-02, 9.03410403e-01],\n",
              "       [5.71657905e-03, 9.92857783e-01, 1.42563747e-03],\n",
              "       [1.88409454e-01, 8.33201888e-02, 7.28270357e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "B9M3dOSzCSA-",
        "outputId": "612d426c-0dba-43da-daab-fec4f9faddda"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.scatter(X_new['C1'],X_new['C2'], c=labels,cmap=plt.cm.winter,alpha=0.6)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GaJKCSG5DYDT",
        "outputId": "84c788b9-c03b-4461-875e-8f807b2397a3"
      },
      "source": [
        "X_samp,Y_samp = gm.sample(5)\n",
        "print(X_samp,Y_samp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.77661991  0.34842033]\n",
            " [-0.38858853 -0.52221794]\n",
            " [ 0.53370575 -0.19368474]\n",
            " [ 0.77767267  0.63301055]\n",
            " [ 0.21864363  0.13293744]] [0 1 2 2 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ToHNLPE4Dkqy"
      },
      "source": [
        "densities=gm.score_samples(X_new)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VRtjiA2LKYX_",
        "outputId": "19cf8a35-c6cf-481f-d1ba-7c35ccd34e81"
      },
      "source": [
        "density_threshold = np.percentile(densities,2)\n",
        "anomaly = X_new[densities<density_threshold]\n",
        "print(\"possible number of frauds:\",end=\" \")\n",
        "print(len(anomaly))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "possible number of frauds: 179\n"
          ]
        }
      ]
    }
  ]
}