BankNote_Authentication.ipynb

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    "colab": {
      "name": "BankNote Authentication.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yashmundhada/BankNote-authentication/blob/main/BankNote_Authentication.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UDmnWxb3y4of"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNlgLjIe5Qim"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C2YqTi_bzPIi"
      },
      "source": [
        "dataset=pd.read_csv('BankNote_Authentication.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P9Mi8uIRzTB0",
        "outputId": "9c9dcb04-2415-4644-d18a-e8f600288662"
      },
      "source": [
        "print(dataset.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['variance', 'skewness', 'curtosis', 'entropy', 'class'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "h-4XswcMzTEd",
        "outputId": "d3b34b66-e44c-4889-c07f-f4e66a802201"
      },
      "source": [
        "dataset.head()"
      ],
      "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>variance</th>\n",
              "      <th>skewness</th>\n",
              "      <th>curtosis</th>\n",
              "      <th>entropy</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3.62160</td>\n",
              "      <td>8.6661</td>\n",
              "      <td>-2.8073</td>\n",
              "      <td>-0.44699</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.54590</td>\n",
              "      <td>8.1674</td>\n",
              "      <td>-2.4586</td>\n",
              "      <td>-1.46210</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.86600</td>\n",
              "      <td>-2.6383</td>\n",
              "      <td>1.9242</td>\n",
              "      <td>0.10645</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.45660</td>\n",
              "      <td>9.5228</td>\n",
              "      <td>-4.0112</td>\n",
              "      <td>-3.59440</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.32924</td>\n",
              "      <td>-4.4552</td>\n",
              "      <td>4.5718</td>\n",
              "      <td>-0.98880</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "   variance  skewness  curtosis  entropy  class\n",
              "0   3.62160    8.6661   -2.8073 -0.44699      0\n",
              "1   4.54590    8.1674   -2.4586 -1.46210      0\n",
              "2   3.86600   -2.6383    1.9242  0.10645      0\n",
              "3   3.45660    9.5228   -4.0112 -3.59440      0\n",
              "4   0.32924   -4.4552    4.5718 -0.98880      0"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YIUbHhAz8QLN"
      },
      "source": [
        "## **Preprocessing Dataset**\n",
        "### 1. Checking the correctness of dataset by checking any other value of class from bottom\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZIb_LrOczTG-",
        "outputId": "35654b7c-0bbe-4032-be1c-edecdad7ec35"
      },
      "source": [
        "dataset.tail()"
      ],
      "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>variance</th>\n",
              "      <th>skewness</th>\n",
              "      <th>curtosis</th>\n",
              "      <th>entropy</th>\n",
              "      <th>class</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1367</th>\n",
              "      <td>0.40614</td>\n",
              "      <td>1.34920</td>\n",
              "      <td>-1.4501</td>\n",
              "      <td>-0.55949</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1368</th>\n",
              "      <td>-1.38870</td>\n",
              "      <td>-4.87730</td>\n",
              "      <td>6.4774</td>\n",
              "      <td>0.34179</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1369</th>\n",
              "      <td>-3.75030</td>\n",
              "      <td>-13.45860</td>\n",
              "      <td>17.5932</td>\n",
              "      <td>-2.77710</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1370</th>\n",
              "      <td>-3.56370</td>\n",
              "      <td>-8.38270</td>\n",
              "      <td>12.3930</td>\n",
              "      <td>-1.28230</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1371</th>\n",
              "      <td>-2.54190</td>\n",
              "      <td>-0.65804</td>\n",
              "      <td>2.6842</td>\n",
              "      <td>1.19520</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      variance  skewness  curtosis  entropy  class\n",
              "1367   0.40614   1.34920   -1.4501 -0.55949      1\n",
              "1368  -1.38870  -4.87730    6.4774  0.34179      1\n",
              "1369  -3.75030 -13.45860   17.5932 -2.77710      1\n",
              "1370  -3.56370  -8.38270   12.3930 -1.28230      1\n",
              "1371  -2.54190  -0.65804    2.6842  1.19520      1"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xcoRBaLkCt8o"
      },
      "source": [
        "Since we have to identify whether a note bundle is fake or real therefore we set our target variable as class\n",
        "\n",
        "```\n",
        "0 = FAKE\n",
        "\n",
        "1 = REAL\n",
        "```"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AquIA_6-zTJm",
        "outputId": "e4be4ee7-c599-4704-fd3e-099a48a5009a"
      },
      "source": [
        "print(\"Shape of Dataset:\\n\", dataset.shape)  #number of rows and column\n",
        "print(\"\\n\")\n",
        "dataset.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of Dataset:\n",
            " (1372, 5)\n",
            "\n",
            "\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1372 entries, 0 to 1371\n",
            "Data columns (total 5 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   variance  1372 non-null   float64\n",
            " 1   skewness  1372 non-null   float64\n",
            " 2   curtosis  1372 non-null   float64\n",
            " 3   entropy   1372 non-null   float64\n",
            " 4   class     1372 non-null   int64  \n",
            "dtypes: float64(4), int64(1)\n",
            "memory usage: 53.7 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PWHUEIhj93oW"
      },
      "source": [
        "### 2. Checking distribution of dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "z5zpT5_czTOe",
        "outputId": "3ad30bfc-b107-44f1-ed75-94720fedfa28"
      },
      "source": [
        "sns.boxplot(data=dataset.iloc[:,:-1], palette=\"Set1\")   #range\n",
        "plt.rcParams['figure.figsize'] = [15, 10]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MMuaQNzPLmEPezCxjDnkzs4w55M3MMlbqylDW2davX8/IyEipeUw+fu3ataXm09/fz5o1a0rNwyxHDnmr1THHHFN3CWZZc8jbnHnP2az9+Zi8mVnGHPJmZhkrFfKS3i5pp6SDkgaaxi+X9LSku9Pf+vKlmpnZ0Sp7TH4H8JvA56eZ9i8RcXbJ+ZuZWQmlQj4i7gOQ1JpqzMyspao8Jn+6pK2SvinpDRUux8zMZnDYPXlJNwInTjPpoxHx1Rkeths4LSJ+KOkc4B8knRURP5pm/quB1QCnnXbakVduZmaHddiQj4jzj3amEfEM8Ewa3iLpX4BXAMPT3HcDsAFgYGAgjnZZZmY2s0p+DCWpFxiPiIakfuBM4LC/f9+yZctjkh6qoqYWOx54rO4iMuL12Vpen62zUNblT800oVTIS3ob8BdAL3CdpLsj4k3AG4FPSHoWOAisiYjxw80vInrL1DNfJA1HxMDh72lHwuuztbw+WyeHdVn27JprgWunGf/3wN+XmbeZmZXnX7yamWXMIT83G+ouIDNen63l9dk6C35dKsIntJiZ5cp78mZmGXPIm5llzCF/GJK+IellddfRjiTtknR83XV0Mkkvk/TfSzze729A0lslvbruOqrgkJ+BCl0R8ZaI+Le66zGbStIi4GXAnEPe7+9D3gpMG/JpPS9Y2Ye8pMslva+p/XFJH5N0k6S7JG2XdGGatlzSA5K+SNGN8qnNe6uS/kHSltSH/uqmeT4h6ZOS7pH0HUnL0vhlkq5N4++R9Etp/Lsk3Zn62v+8pO75XCdzIelYSdel57FD0juapi2WtEnS76X7bUzPb2vTur1O0n9Iw1sl/Uka/kR63ApJt0r6f5Lul/Qlpe5NJZ2TOrrbIul6SSel8R+QdK+kbZK+ksb9StN1DLZKWjLf62ouJL0nPY97JP0fSVdJuqhp+hPpdoWk2yV9DbgXuBw4Iz3fK9LOyRXpNdo++TpJOknSbel+O5Q6DZx8f8/2+i5U021n022rabv8DeCKdN8z0nvxf0kaBj4o6bz0ftqe3t8vTsvYJWldGn+npJ+WtETSg5JelO7z0ub2vIuIrP+Anwe+2dS+FzgVeGlqHw98HxCwnOIXur/YdP9dwPFpuCfdLqb4EHh5agfw62l4HfCxNHw1cEka7gaOA34G+EfgRWn8XwHvqXs9HcF6/C3gyqb2cWndLAdunHwOwKeAd6XhlwHfA44FPgK8Lz3uu8D16T63AK8EVgCPA6dQ7Hx8G3g98CLgW0Bvuv87gI1peBR48eSy0u0/Ar+chl8CLKp73R3Buj0rradD7zPgKuCipvs8kW5XAE8Cp6f2cmDHlNfphvR+Wwb8K3AS8D8oOhWcfC8uaX5/T/f61r1eSq7TabezWbbVqev7VuCv0vAxwA+AV6T2F5u2611N6/U9wNfT8N8Cb03Dq4HP1LUust+Tj4itwAmS+iT9HLAPeBT4lKRtFAF1MsUGAfBQRHxnhtl9QNI9wHcoPijOTON/DHw9DW+h2PAAfhX4XKqjERGPA+cB5wDflXR3ave34rlWbDuwStKnJb0hPReArwJ/GxFfTO1fAz6SntutFBvIacDtFN1d/DJwHfASST9JEVYPpMfeGREPR8RB4G6K9fhK4GeBG9I8P0bxQQCwDfiSpHcBE2ncHcBnJX2AIvgnx7ezXwWuiYjHAOLwXYDcGREPzjDt9cCX0/ttD/BN4D9SfLC+V9LHgddExP4pj5vp9V2oZtrOZtpWp3N1un0l8GBEfC+1v0DxXp705abbc9PwXwPvTcPvpQj9WizoY01H4RrgIoouk68G/gtFfzvnRMSzknZRhBEUe0kvIGkFcD5wbkQ8JenWpsc8G+kjG2gw+3oV8IWI+OM5P5saRMT3JL0WeAtwmaSb0qQ7gDdLGkrrQMBvNQU3AJJ+Ahig6KjuBoq9x9+j2NAmPdM0PLkeBeyMiHN5of9EsbH9OvBRSa+JiMslXZfqvEPSmyLi/lJPvh4TpMOpkrqAn2iaNu17dDYRcZukN1Kss6skfbbpg3na1zciPlHqGdRr2u1M0h8exbZ6pOs5pg5HxB0qDv+uALojYscRzqvlst+TT64G3kkR9NdQHDLYmwJ+JbP04NbkOGBfCvhXAb94BI+5CfhvAOl44HFp3EWSTkjjeyQdyfJrJakPeCoi/g64AnhtmvQnFP8d/WVqXw/8QdPx9J8HiIgfU/zL+3aKQzG3A38I3HaYRT8A9Eo6N83vRZLOSsF3akTcAvwRxevzEklnRMT2iPg0xd7rq8o/+8rdDLxd0suheE9QHAY4J03/DYrDVtPZDzR/73A78I70fuul+BC8M73H9kTElRR7ma9tnsksr+9CdbTb2dT12OwBYLmkn07td1P8hzTpHU23324a/0VgiBr34qFDQj4idlK8gI9ExG7gS8CApO0Ux9GOZE9vM7BI0n0UX3bNdEin2QeBlWk5W4BXR8S9FIcc/ikdLrqB4phpu3sNRVjcDfwpcFnTtA8CiyWtAy6lCKRtknam9qTbKT5cn07Dp6TbGaUPh4uAT6dDZXcDv0RxXPnv0rrdCvzvKM4SuSR9cbgNeBbYVPJ5Vy69Pz8JfDM9x88CVwK/ktrnMsNeZUT8kOI/lh2SrqDoMHAbcA/Fh8faiHiU4lj+PZK2UoTRn0+Z1Wyv74Izh+3sK8CH05erZ0yZ1wGKQy7XpPfbQWB9012WpmV8EPhQ0/gvAUt57nBOLdytgZnZHKVDvQOT36dMmXYRcGFEvHveC2vSKcfkzczmjaS/AC6g+I6j3lq8J29mlq+OOCZvZtapHPJmZhlzyJuZZcwhb2aWMYe8mVnG/h2RSy2BnT13gwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znphrtJv-w9E"
      },
      "source": [
        "### 3. Checking correlation in the dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "D3y4XD0XzTMB",
        "outputId": "dd0c74b9-69d9-4112-9909-bee70c720e4a"
      },
      "source": [
        "correlationVal = dataset.corr()\n",
        "\n",
        "# producing a heatmap for correlation values of dataset\n",
        "correlationVal.style.background_gradient(cmap='PuBu', low=0, high=0, axis=0, subset=None)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "        }</style><table id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002\" class=\"dataframe\"><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >variance</th>        <th class=\"col_heading level0 col1\" >skewness</th>        <th class=\"col_heading level0 col2\" >curtosis</th>        <th class=\"col_heading level0 col3\" >entropy</th>        <th class=\"col_heading level0 col4\" >class</th>    </tr></thead><tbody>\n",
              "                <tr>\n",
              "                        <th id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002level0_row0\" class=\"row_heading level0 row0\" >variance</th>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row0_col1\" class=\"data row0 col1\" >0.264026</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row0_col2\" class=\"data row0 col2\" >-0.380850</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row0_col3\" class=\"data row0 col3\" >0.276817</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row0_col4\" class=\"data row0 col4\" >-0.724843</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002level0_row1\" class=\"row_heading level0 row1\" >skewness</th>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row1_col0\" class=\"data row1 col0\" >0.264026</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row1_col2\" class=\"data row1 col2\" >-0.786895</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row1_col3\" class=\"data row1 col3\" >-0.526321</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row1_col4\" class=\"data row1 col4\" >-0.444688</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002level0_row2\" class=\"row_heading level0 row2\" >curtosis</th>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row2_col0\" class=\"data row2 col0\" >-0.380850</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row2_col1\" class=\"data row2 col1\" >-0.786895</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row2_col3\" class=\"data row2 col3\" >0.318841</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row2_col4\" class=\"data row2 col4\" >0.155883</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002level0_row3\" class=\"row_heading level0 row3\" >entropy</th>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row3_col0\" class=\"data row3 col0\" >0.276817</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row3_col1\" class=\"data row3 col1\" >-0.526321</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row3_col2\" class=\"data row3 col2\" >0.318841</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row3_col4\" class=\"data row3 col4\" >-0.023424</td>\n",
              "            </tr>\n",
              "            <tr>\n",
              "                        <th id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002level0_row4\" class=\"row_heading level0 row4\" >class</th>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row4_col0\" class=\"data row4 col0\" >-0.724843</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row4_col1\" class=\"data row4 col1\" >-0.444688</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row4_col2\" class=\"data row4 col2\" >0.155883</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row4_col3\" class=\"data row4 col3\" >-0.023424</td>\n",
              "                        <td id=\"T_53525fa6_49c2_11ec_89c3_0242ac1c0002row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
              "            </tr>\n",
              "    </tbody></table>"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7f6379014610>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90LdPg9_CNQr"
      },
      "source": [
        "**Findings:**\n",
        "\n",
        "1. Data is well distributed as we can see in box plot which thus gives class prediction in the set { 0, 1 }.\n",
        "2. Variance and Skewness has an inverse effect on Class label as negative correlation. \n",
        "3. Variance and Skewness has the maximum effect on Class label. We will plot all the graphs considering these two variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yi7wPZ9q5PLK"
      },
      "source": [
        " ##**Python pyspark** \n",
        "\n",
        "The vision is to include the power of Distributed Computing which is provided in the **pyspark** library in Python built alongside Apache Spark community. Since we aim to include parallelism in the library and its functions will prove helpful in helping us achieve false note detection efficiently."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WsLUiChd1Njh",
        "outputId": "de629ccf-5883-4bdb-840d-48cf7cc8becd"
      },
      "source": [
        "pip install pyspark"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pyspark\n",
            "  Downloading pyspark-3.2.0.tar.gz (281.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 281.3 MB 38 kB/s \n",
            "\u001b[?25hCollecting py4j==0.10.9.2\n",
            "  Downloading py4j-0.10.9.2-py2.py3-none-any.whl (198 kB)\n",
            "\u001b[K     |████████████████████████████████| 198 kB 37.6 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyspark\n",
            "  Building wheel for pyspark (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyspark: filename=pyspark-3.2.0-py2.py3-none-any.whl size=281805912 sha256=a6bc3dbd19244b82f663a430a4a30c7f5db2cbf86af6d72f658275c0ba0f3aec\n",
            "  Stored in directory: /root/.cache/pip/wheels/0b/de/d2/9be5d59d7331c6c2a7c1b6d1a4f463ce107332b1ecd4e80718\n",
            "Successfully built pyspark\n",
            "Installing collected packages: py4j, pyspark\n",
            "Successfully installed py4j-0.10.9.2 pyspark-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X-jBmBUq5G1P"
      },
      "source": [
        "from pyspark import SparkContext\n",
        "from pyspark.sql import SparkSession"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZYRqegAnHwC1"
      },
      "source": [
        "SparkContext() is basically the entry point similar to object initiation in OOPS\n",
        "\n",
        "SparkSession() is for the use of inner disributed features of system essential for disributed computing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHHiysrPzMnw"
      },
      "source": [
        "sc = SparkContext.getOrCreate();\n",
        "spark = SparkSession(sc)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zjRHyvljMU3D"
      },
      "source": [
        "### 1. Conversion of Dataset(curently in Python Dataframe) to Python Numpy Arrays\n",
        "\n",
        "The below is performed since training and testing of dataset requires data to be in numpy array format"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKJRwd6Az8Ki",
        "outputId": "ad6584f2-8dcb-455d-da25-cea937284af8"
      },
      "source": [
        "print(\"4 Variables determing o/p\")\n",
        "X=dataset.iloc[:,0:4].values\n",
        "print(X)\n",
        "\n",
        "print(\"\\nClass values\")\n",
        "Y=dataset.iloc[:,-1].values\n",
        "print(Y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4 Variables determing o/p\n",
            "[[  3.6216    8.6661   -2.8073   -0.44699]\n",
            " [  4.5459    8.1674   -2.4586   -1.4621 ]\n",
            " [  3.866    -2.6383    1.9242    0.10645]\n",
            " ...\n",
            " [ -3.7503  -13.4586   17.5932   -2.7771 ]\n",
            " [ -3.5637   -8.3827   12.393    -1.2823 ]\n",
            " [ -2.5419   -0.65804   2.6842    1.1952 ]]\n",
            "\n",
            "Class values\n",
            "[0 0 0 ... 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FbulbucsNvSk"
      },
      "source": [
        "### 2. Dataset division into Training and Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Gor5vMa5Ige"
      },
      "source": [
        "We'll be performing Logistic Regression which is a technique used model the probability of a certain class or event existing such as pass/fail, win/lose, alive/dead or healthy/sick. \n",
        "\n",
        "Here if class value returned:\n",
        "\n",
        "0 = FAKE\n",
        "\n",
        "1 = REAL"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wvv1PWglBcF3"
      },
      "source": [
        "### 3. Importing Necessary Libs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9LzYs3eAHBh3"
      },
      "source": [
        "from pyspark.mllib.regression import LabeledPoint\n",
        "from pyspark.mllib.classification import LogisticRegressionWithLBFGS,SVMWithSGD,LogisticRegressionWithSGD,NaiveBayes"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5PLoX8mQz8NX"
      },
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X, Y, test_size=0.2, random_state=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OpqDoQhiz8QL"
      },
      "source": [
        "data=[]\n",
        "for i in range(len(X_train)):\n",
        "    data.append(LabeledPoint(y_train[i],X_train[i]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9pqvnCeNz8S0",
        "outputId": "88ac0892-1684-4a6f-9dd8-de14dd0e1e36"
      },
      "source": [
        "print(data[:10])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LabeledPoint(0.0, [2.9719,6.8369,-0.2702,0.71291]), LabeledPoint(1.0, [-0.90784,-7.9026,6.7807,0.34179]), LabeledPoint(1.0, [-0.97325,-6.4168,5.6026,1.0323]), LabeledPoint(1.0, [-1.3414,-1.9162,-0.15538,-0.11984000000000002]), LabeledPoint(1.0, [-0.0012852,0.13863,-0.19651,0.0081754]), LabeledPoint(0.0, [-1.8411,10.8306,2.7689999999999997,-3.0901]), LabeledPoint(1.0, [-1.8974,3.5074,-1.7842,-3.8491]), LabeledPoint(1.0, [-2.3629,-0.10554000000000001,1.9336,1.1358]), LabeledPoint(1.0, [-2.3147,3.6668,-0.6969,-1.2474]), LabeledPoint(0.0, [1.9358,8.1654,-0.023425,-2.2586])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "HDOXhRz3z8YM",
        "outputId": "b80ed4a7-5c7e-4111-817d-c874700b4326"
      },
      "source": [
        "dic={}\n",
        "\n",
        "\n",
        "lrm1 = LogisticRegressionWithLBFGS.train(sc.parallelize(data), iterations=1000)     \n",
        "y_predict=lrm1.predict(sc.parallelize(X_test)).collect()\n",
        "cm=confusion_matrix(y_true=y_test,y_pred=y_predict)\n",
        "acc=accuracy_score(y_test,y_predict)\n",
        "dic[\"Logistic Regression With LBFGS\"]=acc\n",
        "print(\"The confusion matrix is:\")\n",
        "print(\"\")\n",
        "print(cm)\n",
        "print(\"\")\n",
        "print(\"The accuracy of the {} is: {}%\".format(\"Logistic Regression With LBFGS\",acc*100))\n",
        "y_whole=lrm1.predict(sc.parallelize(X)).collect()\n",
        "plt.figure(3)\n",
        "for i in range(len(X)):\n",
        "    if(y_whole[i]==1):\n",
        "        plt.scatter(X[i][0],X[i][1],color=\"blue\")\n",
        "    if(y_whole[i]==0):\n",
        "        plt.scatter(X[i][0],X[i][1],color=\"red\")\n",
        "plt.title(\"Observation after training the spark model.\")\n",
        "plt.xlabel(\"Variance\")\n",
        "plt.ylabel(\"Skewness\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The confusion matrix is:\n",
            "\n",
            "[[168   0]\n",
            " [  8  99]]\n",
            "\n",
            "The accuracy of the Logistic Regression With LBFGS is: 97.0909090909091%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 780
        },
        "id": "syJgVryF0P2j",
        "outputId": "77a9db19-d60a-4672-e03a-f5f44835858e"
      },
      "source": [
        "lrm2 = LogisticRegressionWithSGD.train(sc.parallelize(data), iterations=1000)\n",
        "y_predict=lrm2.predict(sc.parallelize(X_test)).collect()\n",
        "cm=confusion_matrix(y_true=y_test,y_pred=y_predict)\n",
        "acc=accuracy_score(y_test,y_pred=y_predict)\n",
        "dic[\"Logistic Regression With SGD\"]=acc\n",
        "print(\"The confusion matrix is:\")\n",
        "print(\"\")\n",
        "print(cm)\n",
        "print(\"\")\n",
        "print(\"The accuracy of the {} is: {}%\".format(\"Logistic Regression With SGD\",acc*100))\n",
        "\n",
        "y_whole=lrm2.predict(sc.parallelize(X)).collect()\n",
        "plt.figure(3)\n",
        "for i in range(len(X)):\n",
        "    if(y_whole[i]==1):\n",
        "        plt.scatter(X[i][0],X[i][1],color=\"blue\")\n",
        "    if(y_whole[i]==0):\n",
        "        plt.scatter(X[i][0],X[i][1],color=\"red\")\n",
        "plt.title(\"Observation after training the spark model.\")\n",
        "plt.xlabel(\"Variance\")\n",
        "plt.ylabel(\"Skewness\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pyspark/mllib/classification.py:327: FutureWarning: Deprecated in 2.0.0. Use ml.classification.LogisticRegression or LogisticRegressionWithLBFGS.\n",
            "  \"LogisticRegressionWithLBFGS.\", FutureWarning)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The confusion matrix is:\n",
            "\n",
            "[[168   0]\n",
            " [  8  99]]\n",
            "\n",
            "The accuracy of the Logistic Regression With SGD is: 97.0909090909091%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "irkFBy1p0P5e",
        "outputId": "fc18c922-4622-4e00-9814-c579d38560de"
      },
      "source": [
        "lrm3 = SVMWithSGD.train(sc.parallelize(data), iterations=1000)\n",
        "y_predict=lrm3.predict(sc.parallelize(X_test)).collect()\n",
        "cm=confusion_matrix(y_true=y_test,y_pred=y_predict)\n",
        "acc=accuracy_score(y_test,y_pred=y_predict)\n",
        "dic[\"SVM With SGD\"]=acc\n",
        "print(\"The confusion matrix is:\")\n",
        "print(\"\")\n",
        "print(cm)\n",
        "print(\"\")\n",
        "print(\"The accuracy of the {} is: {}%\".format(\"SVM With SGD\",acc*100))\n",
        "y_whole=lrm3.predict(sc.parallelize(X)).collect()\n",
        "plt.figure(3)\n",
        "for i in range(len(X)):\n",
        "    if(y_whole[i]==1):\n",
        "        plt.scatter(X[i][0],X[i][1],color=\"blue\")\n",
        "    if(y_whole[i]==0):\n",
        "        plt.scatter(X[i][0],X[i][1],color=\"red\")\n",
        "plt.title(\"Observation after training the spark model.\")\n",
        "plt.xlabel(\"Variance\")\n",
        "plt.ylabel(\"Skewness\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The confusion matrix is:\n",
            "\n",
            "[[168   0]\n",
            " [  9  98]]\n",
            "\n",
            "The accuracy of the SVM With SGD is: 96.72727272727273%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 725
        },
        "id": "_N_83gRh0P8J",
        "outputId": "73769937-e495-45e8-9cd6-a0b1b89e304e"
      },
      "source": [
        "from pyspark.mllib.tree import RandomForest\n",
        "lrm4 = RandomForest.trainClassifier(sc.parallelize(data),2,{},100)\n",
        "y_predict=lrm4.predict(sc.parallelize(X_test)).collect()\n",
        "cm=confusion_matrix(y_true=y_test,y_pred=y_predict)\n",
        "acc=accuracy_score(y_test,y_pred=y_predict)\n",
        "dic[\"Random Forest\"]=acc\n",
        "print(\"The confusion matrix is:\")\n",
        "print(\"\")\n",
        "print(cm)\n",
        "print(\"\")\n",
        "print(\"The accuracy of the {} is: {}%\".format(\"Random Forest\",acc*100))\n",
        "y_whole=lrm4.predict(sc.parallelize(X)).collect()\n",
        "plt.figure(3)\n",
        "for i in range(len(X)):\n",
        "    if(y_whole[i]==1):\n",
        "        plt.scatter(X[i][0],X[i][1],color=\"blue\")\n",
        "    if(y_whole[i]==0):\n",
        "        plt.scatter(X[i][0],X[i][1],color=\"red\")\n",
        "plt.title(\"Observation after training the spark model.\")\n",
        "plt.xlabel(\"Variance\")\n",
        "plt.ylabel(\"Skewness\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The confusion matrix is:\n",
            "\n",
            "[[158  10]\n",
            " [  2 105]]\n",
            "\n",
            "The accuracy of the Random Forest is: 95.63636363636364%\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "ufxTaYxq0P-6",
        "outputId": "faa05b75-b548-4549-94db-86f8851934fd"
      },
      "source": [
        "Estimators=[]\n",
        "Accuracy=[]\n",
        "for i in dic:\n",
        "    Estimators.append(i)\n",
        "    Accuracy.append(dic[i]*100)\n",
        "d={'Estimators':Estimators,\"Accuracy\":Accuracy}\n",
        "df=pd.DataFrame(data=d)\n",
        "plt.rcParams['figure.figsize'] = [10, 5]\n",
        "plt.ylim(0,110)\n",
        "plt.title(\"All classification estimators with accuracy score\")\n",
        "bars=plt.bar(x=Estimators,height=Accuracy)\n",
        "for bar in bars:\n",
        "    yval = round(bar.get_height(),2)\n",
        "    plt.text(bar.get_x()+0.2, round((yval + .005),2), yval)\n",
        "plt.xticks(rotation=80)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fTVvq0JidI1w"
      },
      "source": [
        "Checking of the Unlabeled Notes\n",
        "Since We can deduce that the notes which are real and considered as fake has a huge impact on the economy as the real notes are being discarded considered as fake notes. i.e.\n",
        "\n",
        "Actual Value of note: - 1\n",
        "\n",
        "Predicted Value of note: - 0\n",
        "\n",
        "We will check the notes which are labeled as fakes and are actually real by the different models and determine whether the different models capture and labeled these notes correctly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLQ3w7IYdHmJ"
      },
      "source": [
        "Using Logistic Regression With LBFGS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOXfRs7zz8ah",
        "outputId": "45b98f84-f655-426e-f584-7769aa503199"
      },
      "source": [
        "y_wrong_lbfgs=[]\n",
        "y_whole_lbfgs=lrm1.predict(sc.parallelize(X_test)).collect()\n",
        "for i in range(len(y_whole_lbfgs)):                              \n",
        "    if(y_test[i]==1 and y_whole_lbfgs[i]==0):                           #actual_value_of_note=1 && predicted_value_of_note=1\n",
        "        y_wrong_lbfgs.append(X_test[i])\n",
        "y_wrong_lbfgs=np.array(y_wrong_lbfgs)\n",
        "print(y_wrong_lbfgs)\n",
        "print(\"*\"*45)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.24394   1.4733   -1.4192   -0.58535 ]\n",
            " [ 0.50225   0.65388  -1.1793    0.39998 ]\n",
            " [ 0.518     0.25865  -0.84085   0.96118 ]\n",
            " [ 0.84546   3.4826   -3.6307   -1.3961  ]\n",
            " [ 1.3403    4.1323   -4.7018   -2.5987  ]\n",
            " [ 0.12126   0.22347  -0.47327   0.97024 ]\n",
            " [-1.4375   -1.8624    4.026     0.55127 ]\n",
            " [ 0.66365  -0.045533 -0.18794   0.23447 ]]\n",
            "*********************************************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cdGcLrXSdPvZ"
      },
      "source": [
        "Using Logistic Regression With SGD"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3CmItbAb0jAi",
        "outputId": "b25a4cf3-92a7-45e7-957e-a187fd850462"
      },
      "source": [
        "y_wrong_sgd=[]\n",
        "y_whole_sgd=lrm2.predict(sc.parallelize(y_wrong_lbfgs)).collect()\n",
        "for i in range(len(y_whole_sgd)):\n",
        "    if(y_whole_sgd[i]==0):\n",
        "        y_wrong_sgd.append(y_wrong_lbfgs[i])\n",
        "y_wrong_sgd=np.array(y_wrong_sgd)\n",
        "print(y_wrong_sgd)\n",
        "print(\"*\"*45)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.24394   1.4733   -1.4192   -0.58535 ]\n",
            " [ 0.50225   0.65388  -1.1793    0.39998 ]\n",
            " [ 0.518     0.25865  -0.84085   0.96118 ]\n",
            " [ 0.84546   3.4826   -3.6307   -1.3961  ]\n",
            " [ 1.3403    4.1323   -4.7018   -2.5987  ]\n",
            " [ 0.12126   0.22347  -0.47327   0.97024 ]\n",
            " [ 0.66365  -0.045533 -0.18794   0.23447 ]]\n",
            "*********************************************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-RvRgCZdTHs"
      },
      "source": [
        "Using SVM With SGD"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aDrFnFrN0pdY",
        "outputId": "81271e2c-b34d-4c89-cd51-223106956bc6"
      },
      "source": [
        "y_wrong_svm=[]\n",
        "y_whole_svm=lrm3.predict(sc.parallelize(y_wrong_sgd)).collect()\n",
        "for i in range(len(y_whole_svm)):\n",
        "    if(y_whole_svm[i]==0):\n",
        "        y_wrong_svm.append(y_wrong_sgd[i])\n",
        "y_wrong_svm=np.array(y_wrong_svm)\n",
        "print(y_wrong_svm)\n",
        "print(\"*\"*45)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.24394   1.4733   -1.4192   -0.58535 ]\n",
            " [ 0.50225   0.65388  -1.1793    0.39998 ]\n",
            " [ 0.518     0.25865  -0.84085   0.96118 ]\n",
            " [ 0.84546   3.4826   -3.6307   -1.3961  ]\n",
            " [ 1.3403    4.1323   -4.7018   -2.5987  ]\n",
            " [ 0.12126   0.22347  -0.47327   0.97024 ]\n",
            " [ 0.66365  -0.045533 -0.18794   0.23447 ]]\n",
            "*********************************************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crmvgjqsdXpI"
      },
      "source": [
        "Using Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jrzC6qd90pmL",
        "outputId": "cad29f31-1800-46d6-8eaf-4b7dc1f57a85"
      },
      "source": [
        "y_wrong_forest=[]\n",
        "y_whole_forest=lrm4.predict(sc.parallelize(y_wrong_svm)).collect()\n",
        "for i in range(len(y_whole_forest)):\n",
        "    if(y_whole_forest[i]==0):                                                   \n",
        "        y_wrong_forest.append(y_wrong_svm[i])\n",
        "y_wrong_forest=np.array(y_wrong_forest)\n",
        "print(y_wrong_forest)\n",
        "print(\"*\"*45)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.66365  -0.045533 -0.18794   0.23447 ]]\n",
            "*********************************************\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Kbj28Bg0dajc"
      },
      "source": [
        "We can conclude that using **Random Forest** we can detect most of the real notes which are labeled as fake notes and will be discarded."
      ]
    }
  ]
}