AnamolyDetectionHealthcare.ipynb

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        "# ANAMOLY DETECTION ON HEALTHCARE DATA\n",
        "### Name  : Aditya Sahani & Shikhar Dave\n",
        "#### Dataset : https://www.kaggle.com/datasets/tamilsel/healthcare-providers-data/code\n",
        "#### Technology and Models Used : Isolation Forest, LocalOutlierFactor and Autoencoders\n",
        "#### Data has around 3 % anamoly i.e. around 3000"
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      "source": [
        "## Importing all the necessaries libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/sahaniaditya/healthcare_anamoly_detection/main/Healthcare%20Providers.csv\")"
      ],
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        "df.head()"
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              "     index  National Provider Identifier  \\\n",
              "0  8774979                    1891106191   \n",
              "1  3354385                    1346202256   \n",
              "2  3001884                    1306820956   \n",
              "3  7594822                    1770523540   \n",
              "4   746159                    1073627758   \n",
              "\n",
              "  Last Name/Organization Name of the Provider First Name of the Provider  \\\n",
              "0                                 UPADHYAYULA                  SATYASREE   \n",
              "1                                       JONES                      WENDY   \n",
              "2                                    DUROCHER                    RICHARD   \n",
              "3                                     FULLARD                     JASPER   \n",
              "4                                    PERROTTI                    ANTHONY   \n",
              "\n",
              "  Middle Initial of the Provider Credentials of the Provider  \\\n",
              "0                            NaN                        M.D.   \n",
              "1                              P                        M.D.   \n",
              "2                              W                         DPM   \n",
              "3                            NaN                          MD   \n",
              "4                              E                          DO   \n",
              "\n",
              "  Gender of the Provider Entity Type of the Provider  \\\n",
              "0                      F                           I   \n",
              "1                      F                           I   \n",
              "2                      M                           I   \n",
              "3                      M                           I   \n",
              "4                      M                           I   \n",
              "\n",
              "  Street Address 1 of the Provider Street Address 2 of the Provider  ...  \\\n",
              "0                1402 S GRAND BLVD                   FDT 14TH FLOOR  ...   \n",
              "1                  2950 VILLAGE DR                              NaN  ...   \n",
              "2                20 WASHINGTON AVE                          STE 212  ...   \n",
              "3               5746 N BROADWAY ST                              NaN  ...   \n",
              "4                 875 MILITARY TRL                        SUITE 200  ...   \n",
              "\n",
              "  HCPCS Code                                  HCPCS Description  \\\n",
              "0      99223  Initial hospital inpatient care, typically 70 ...   \n",
              "1      G0202  Screening mammography, bilateral (2-view study...   \n",
              "2      99348  Established patient home visit, typically 25 m...   \n",
              "3      81002                            Urinalysis, manual test   \n",
              "4      96372  Injection beneath the skin or into muscle for ...   \n",
              "\n",
              "  HCPCS Drug Indicator Number of Services Number of Medicare Beneficiaries  \\\n",
              "0                    N                 27                               24   \n",
              "1                    N                175                              175   \n",
              "2                    N                 32                               13   \n",
              "3                    N                 20                               18   \n",
              "4                    N                 33                               24   \n",
              "\n",
              "  Number of Distinct Medicare Beneficiary/Per Day Services  \\\n",
              "0                                                 27         \n",
              "1                                                175         \n",
              "2                                                 32         \n",
              "3                                                 20         \n",
              "4                                                 31         \n",
              "\n",
              "  Average Medicare Allowed Amount Average Submitted Charge Amount  \\\n",
              "0                    200.58777778                    305.21111111   \n",
              "1                          123.73                           548.8   \n",
              "2                           90.65                             155   \n",
              "3                             3.5                               5   \n",
              "4                           26.52                              40   \n",
              "\n",
              "  Average Medicare Payment Amount Average Medicare Standardized Amount  \n",
              "0                    157.26222222                         160.90888889  \n",
              "1                          118.83                         135.31525714  \n",
              "2                      64.4396875                           60.5959375  \n",
              "3                            3.43                                 3.43  \n",
              "4                    19.539393939                         19.057575758  \n",
              "\n",
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              "      <th>National Provider Identifier</th>\n",
              "      <th>Last Name/Organization Name of the Provider</th>\n",
              "      <th>First Name of the Provider</th>\n",
              "      <th>Middle Initial of the Provider</th>\n",
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              "      <th>Street Address 2 of the Provider</th>\n",
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              "      <th>HCPCS Code</th>\n",
              "      <th>HCPCS Description</th>\n",
              "      <th>HCPCS Drug Indicator</th>\n",
              "      <th>Number of Services</th>\n",
              "      <th>Number of Medicare Beneficiaries</th>\n",
              "      <th>Number of Distinct Medicare Beneficiary/Per Day Services</th>\n",
              "      <th>Average Medicare Allowed Amount</th>\n",
              "      <th>Average Submitted Charge Amount</th>\n",
              "      <th>Average Medicare Payment Amount</th>\n",
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              "      <td>8774979</td>\n",
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              "      <td>UPADHYAYULA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>M.D.</td>\n",
              "      <td>F</td>\n",
              "      <td>I</td>\n",
              "      <td>1402 S GRAND BLVD</td>\n",
              "      <td>FDT 14TH FLOOR</td>\n",
              "      <td>...</td>\n",
              "      <td>99223</td>\n",
              "      <td>Initial hospital inpatient care, typically 70 ...</td>\n",
              "      <td>N</td>\n",
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              "      <td>24</td>\n",
              "      <td>27</td>\n",
              "      <td>200.58777778</td>\n",
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              "      <td>160.90888889</td>\n",
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              "      <th>1</th>\n",
              "      <td>3354385</td>\n",
              "      <td>1346202256</td>\n",
              "      <td>JONES</td>\n",
              "      <td>WENDY</td>\n",
              "      <td>P</td>\n",
              "      <td>M.D.</td>\n",
              "      <td>F</td>\n",
              "      <td>I</td>\n",
              "      <td>2950 VILLAGE DR</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>G0202</td>\n",
              "      <td>Screening mammography, bilateral (2-view study...</td>\n",
              "      <td>N</td>\n",
              "      <td>175</td>\n",
              "      <td>175</td>\n",
              "      <td>175</td>\n",
              "      <td>123.73</td>\n",
              "      <td>548.8</td>\n",
              "      <td>118.83</td>\n",
              "      <td>135.31525714</td>\n",
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              "      <th>2</th>\n",
              "      <td>3001884</td>\n",
              "      <td>1306820956</td>\n",
              "      <td>DUROCHER</td>\n",
              "      <td>RICHARD</td>\n",
              "      <td>W</td>\n",
              "      <td>DPM</td>\n",
              "      <td>M</td>\n",
              "      <td>I</td>\n",
              "      <td>20 WASHINGTON AVE</td>\n",
              "      <td>STE 212</td>\n",
              "      <td>...</td>\n",
              "      <td>99348</td>\n",
              "      <td>Established patient home visit, typically 25 m...</td>\n",
              "      <td>N</td>\n",
              "      <td>32</td>\n",
              "      <td>13</td>\n",
              "      <td>32</td>\n",
              "      <td>90.65</td>\n",
              "      <td>155</td>\n",
              "      <td>64.4396875</td>\n",
              "      <td>60.5959375</td>\n",
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              "      <th>3</th>\n",
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              "      <td>FULLARD</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>5746 N BROADWAY ST</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>81002</td>\n",
              "      <td>Urinalysis, manual test</td>\n",
              "      <td>N</td>\n",
              "      <td>20</td>\n",
              "      <td>18</td>\n",
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              "      <th>4</th>\n",
              "      <td>746159</td>\n",
              "      <td>1073627758</td>\n",
              "      <td>PERROTTI</td>\n",
              "      <td>ANTHONY</td>\n",
              "      <td>E</td>\n",
              "      <td>DO</td>\n",
              "      <td>M</td>\n",
              "      <td>I</td>\n",
              "      <td>875 MILITARY TRL</td>\n",
              "      <td>SUITE 200</td>\n",
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              "      <td>96372</td>\n",
              "      <td>Injection beneath the skin or into muscle for ...</td>\n",
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          "metadata": {},
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    {
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      "source": [
        "df.info() ## getting the info about the dataset"
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      "execution_count": 28,
      "outputs": [
        {
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100000 entries, 0 to 99999\n",
            "Data columns (total 27 columns):\n",
            " #   Column                                                    Non-Null Count   Dtype  \n",
            "---  ------                                                    --------------   -----  \n",
            " 0   index                                                     100000 non-null  int64  \n",
            " 1   National Provider Identifier                              100000 non-null  int64  \n",
            " 2   Last Name/Organization Name of the Provider               100000 non-null  object \n",
            " 3   First Name of the Provider                                95745 non-null   object \n",
            " 4   Middle Initial of the Provider                            70669 non-null   object \n",
            " 5   Credentials of the Provider                               92791 non-null   object \n",
            " 6   Gender of the Provider                                    95746 non-null   object \n",
            " 7   Entity Type of the Provider                               100000 non-null  object \n",
            " 8   Street Address 1 of the Provider                          100000 non-null  object \n",
            " 9   Street Address 2 of the Provider                          40637 non-null   object \n",
            " 10  City of the Provider                                      100000 non-null  object \n",
            " 11  Zip Code of the Provider                                  100000 non-null  float64\n",
            " 12  State Code of the Provider                                100000 non-null  object \n",
            " 13  Country Code of the Provider                              100000 non-null  object \n",
            " 14  Provider Type                                             100000 non-null  object \n",
            " 15  Medicare Participation Indicator                          100000 non-null  object \n",
            " 16  Place of Service                                          100000 non-null  object \n",
            " 17  HCPCS Code                                                100000 non-null  object \n",
            " 18  HCPCS Description                                         100000 non-null  object \n",
            " 19  HCPCS Drug Indicator                                      100000 non-null  object \n",
            " 20  Number of Services                                        100000 non-null  object \n",
            " 21  Number of Medicare Beneficiaries                          100000 non-null  object \n",
            " 22  Number of Distinct Medicare Beneficiary/Per Day Services  100000 non-null  object \n",
            " 23  Average Medicare Allowed Amount                           100000 non-null  object \n",
            " 24  Average Submitted Charge Amount                           100000 non-null  object \n",
            " 25  Average Medicare Payment Amount                           100000 non-null  object \n",
            " 26  Average Medicare Standardized Amount                      100000 non-null  object \n",
            "dtypes: float64(1), int64(2), object(24)\n",
            "memory usage: 20.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "columns = [ \"Number of Services\", \"Number of Medicare Beneficiaries\",\"Number of Distinct Medicare Beneficiary/Per Day Services\",\"Average Medicare Allowed Amount\",\"Average Submitted Charge Amount\",\"Average Medicare Payment Amount\",\"Average Medicare Standardized Amount\"]"
      ],
      "metadata": {
        "id": "1W22_I6tf8b5"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df[columns]  ## dropping the unnecessary columns"
      ],
      "metadata": {
        "id": "9-6-mBD4giPb"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df ##  refined dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "hkLiFgjqglpO",
        "outputId": "6dff108c-2763-489d-c83a-fe6c8378cf43"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Number of Services Number of Medicare Beneficiaries  \\\n",
              "0                     27                               24   \n",
              "1                    175                              175   \n",
              "2                     32                               13   \n",
              "3                     20                               18   \n",
              "4                     33                               24   \n",
              "...                  ...                              ...   \n",
              "99995                 20                               20   \n",
              "99996                136                              107   \n",
              "99997                 11                               11   \n",
              "99998                 12                               12   \n",
              "99999                589                               76   \n",
              "\n",
              "      Number of Distinct Medicare Beneficiary/Per Day Services  \\\n",
              "0                                                     27         \n",
              "1                                                    175         \n",
              "2                                                     32         \n",
              "3                                                     20         \n",
              "4                                                     31         \n",
              "...                                                  ...         \n",
              "99995                                                 20         \n",
              "99996                                                136         \n",
              "99997                                                 11         \n",
              "99998                                                 12         \n",
              "99999                                                587         \n",
              "\n",
              "      Average Medicare Allowed Amount Average Submitted Charge Amount  \\\n",
              "0                        200.58777778                    305.21111111   \n",
              "1                              123.73                           548.8   \n",
              "2                               90.65                             155   \n",
              "3                                 3.5                               5   \n",
              "4                               26.52                              40   \n",
              "...                               ...                             ...   \n",
              "99995                         85.3725                             214   \n",
              "99996                           61.27                    144.05147059   \n",
              "99997                           17.98                    109.54545455   \n",
              "99998                           30.54                              65   \n",
              "99999                    38.601052632                    46.867572156   \n",
              "\n",
              "      Average Medicare Payment Amount Average Medicare Standardized Amount  \n",
              "0                        157.26222222                         160.90888889  \n",
              "1                              118.83                         135.31525714  \n",
              "2                          64.4396875                           60.5959375  \n",
              "3                                3.43                                 3.43  \n",
              "4                        19.539393939                         19.057575758  \n",
              "...                               ...                                  ...  \n",
              "99995                         60.7255                              62.2485  \n",
              "99996                    30.006176471                         37.040220588  \n",
              "99997                           14.09                                14.62  \n",
              "99998                           29.93                                25.32  \n",
              "99999                    29.736876061                         25.912139219  \n",
              "\n",
              "[100000 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-198eb619-8fe7-44ad-9ff8-767726364a79\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Number of Services</th>\n",
              "      <th>Number of Medicare Beneficiaries</th>\n",
              "      <th>Number of Distinct Medicare Beneficiary/Per Day Services</th>\n",
              "      <th>Average Medicare Allowed Amount</th>\n",
              "      <th>Average Submitted Charge Amount</th>\n",
              "      <th>Average Medicare Payment Amount</th>\n",
              "      <th>Average Medicare Standardized Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27</td>\n",
              "      <td>24</td>\n",
              "      <td>27</td>\n",
              "      <td>200.58777778</td>\n",
              "      <td>305.21111111</td>\n",
              "      <td>157.26222222</td>\n",
              "      <td>160.90888889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>175</td>\n",
              "      <td>175</td>\n",
              "      <td>175</td>\n",
              "      <td>123.73</td>\n",
              "      <td>548.8</td>\n",
              "      <td>118.83</td>\n",
              "      <td>135.31525714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32</td>\n",
              "      <td>13</td>\n",
              "      <td>32</td>\n",
              "      <td>90.65</td>\n",
              "      <td>155</td>\n",
              "      <td>64.4396875</td>\n",
              "      <td>60.5959375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20</td>\n",
              "      <td>18</td>\n",
              "      <td>20</td>\n",
              "      <td>3.5</td>\n",
              "      <td>5</td>\n",
              "      <td>3.43</td>\n",
              "      <td>3.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33</td>\n",
              "      <td>24</td>\n",
              "      <td>31</td>\n",
              "      <td>26.52</td>\n",
              "      <td>40</td>\n",
              "      <td>19.539393939</td>\n",
              "      <td>19.057575758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99995</th>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>85.3725</td>\n",
              "      <td>214</td>\n",
              "      <td>60.7255</td>\n",
              "      <td>62.2485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99996</th>\n",
              "      <td>136</td>\n",
              "      <td>107</td>\n",
              "      <td>136</td>\n",
              "      <td>61.27</td>\n",
              "      <td>144.05147059</td>\n",
              "      <td>30.006176471</td>\n",
              "      <td>37.040220588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99997</th>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>11</td>\n",
              "      <td>17.98</td>\n",
              "      <td>109.54545455</td>\n",
              "      <td>14.09</td>\n",
              "      <td>14.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99998</th>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>30.54</td>\n",
              "      <td>65</td>\n",
              "      <td>29.93</td>\n",
              "      <td>25.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99999</th>\n",
              "      <td>589</td>\n",
              "      <td>76</td>\n",
              "      <td>587</td>\n",
              "      <td>38.601052632</td>\n",
              "      <td>46.867572156</td>\n",
              "      <td>29.736876061</td>\n",
              "      <td>25.912139219</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>100000 rows × 7 columns</p>\n",
              "</div>\n",
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              "\n",
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              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "    .colab-df-buttons div {\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-198eb619-8fe7-44ad-9ff8-767726364a79 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-198eb619-8fe7-44ad-9ff8-767726364a79');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d15f3ee1-9499-4236-844d-1e9a2384dd40 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Data Preprocessing"
      ],
      "metadata": {
        "id": "dj6xeuqP7VxI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()  ## checking for null values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZdiL3RbzgoMi",
        "outputId": "73056d8b-510e-4980-81de-821b4ef4839e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Number of Services                                          0\n",
              "Number of Medicare Beneficiaries                            0\n",
              "Number of Distinct Medicare Beneficiary/Per Day Services    0\n",
              "Average Medicare Allowed Amount                             0\n",
              "Average Submitted Charge Amount                             0\n",
              "Average Medicare Payment Amount                             0\n",
              "Average Medicare Standardized Amount                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(how=\"any\", inplace=True)  ## removing the null rows"
      ],
      "metadata": {
        "id": "oGb4vevmgw4V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbdf5ef3-2648-409d-d588-fbff151c0013"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-33-4bcc0e0bbaae>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df.dropna(how=\"any\", inplace=True)  ## removing the null rows\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7p2NZcVg6fz",
        "outputId": "ebac80e2-e681-4c02-df19-89380559d624"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Number of Services                                          0\n",
              "Number of Medicare Beneficiaries                            0\n",
              "Number of Distinct Medicare Beneficiary/Per Day Services    0\n",
              "Average Medicare Allowed Amount                             0\n",
              "Average Submitted Charge Amount                             0\n",
              "Average Medicare Payment Amount                             0\n",
              "Average Medicare Standardized Amount                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()  ## checking for duplicated rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UK2Njox_hEPl",
        "outputId": "3b34b64a-9d94-43de-86d2-1606074fcd8c"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "230"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop_duplicates()"
      ],
      "metadata": {
        "id": "UMKyW8we7Eu_"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.duplicated().sum()  ## duplicate rows removed successfully"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cwKXKEvR7IP0",
        "outputId": "ad8c2122-f1a3-44fa-a883-18f8dde9894a"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head() ## new_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "If9PG1kZhKsf",
        "outputId": "c94720d3-0679-450b-aaf1-2783f9ce9c24"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Number of Services Number of Medicare Beneficiaries  \\\n",
              "0                 27                               24   \n",
              "1                175                              175   \n",
              "2                 32                               13   \n",
              "3                 20                               18   \n",
              "4                 33                               24   \n",
              "\n",
              "  Number of Distinct Medicare Beneficiary/Per Day Services  \\\n",
              "0                                                 27         \n",
              "1                                                175         \n",
              "2                                                 32         \n",
              "3                                                 20         \n",
              "4                                                 31         \n",
              "\n",
              "  Average Medicare Allowed Amount Average Submitted Charge Amount  \\\n",
              "0                    200.58777778                    305.21111111   \n",
              "1                          123.73                           548.8   \n",
              "2                           90.65                             155   \n",
              "3                             3.5                               5   \n",
              "4                           26.52                              40   \n",
              "\n",
              "  Average Medicare Payment Amount Average Medicare Standardized Amount  \n",
              "0                    157.26222222                         160.90888889  \n",
              "1                          118.83                         135.31525714  \n",
              "2                      64.4396875                           60.5959375  \n",
              "3                            3.43                                 3.43  \n",
              "4                    19.539393939                         19.057575758  "
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## function to remove commas from  the numerical dataset\n",
        "def remove_comma(x):\n",
        "   st = str(x)\n",
        "   val = \"\"\n",
        "   for i in st:\n",
        "    if(i != ','):\n",
        "      val = val + i\n",
        "\n",
        "   return float(val)"
      ],
      "metadata": {
        "id": "qc4_9Qjio6fh"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "col_list = df.columns"
      ],
      "metadata": {
        "id": "K3KnU9hwpr-i"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "col_list"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pj8CkK5OpwL3",
        "outputId": "6d44bcaa-ad17-4538-e8c6-968171a17c10"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Number of Services', 'Number of Medicare Beneficiaries',\n",
              "       'Number of Distinct Medicare Beneficiary/Per Day Services',\n",
              "       'Average Medicare Allowed Amount', 'Average Submitted Charge Amount',\n",
              "       'Average Medicare Payment Amount',\n",
              "       'Average Medicare Standardized Amount'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in col_list:\n",
        "  df[col] = df[col].apply(remove_comma)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8piWfdSpzBR",
        "outputId": "748401d2-aff1-42c8-887f-9c3cf736cc7d"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-42-2da5d9c74175>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df[col] = df[col].apply(remove_comma)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()  ## dataset after removing commas"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "rzndvwUtwqiw",
        "outputId": "0cf75dfd-cc0c-49a5-fefb-38d4cb2ee24f"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Number of Services  Number of Medicare Beneficiaries  \\\n",
              "0                27.0                              24.0   \n",
              "1               175.0                             175.0   \n",
              "2                32.0                              13.0   \n",
              "3                20.0                              18.0   \n",
              "4                33.0                              24.0   \n",
              "\n",
              "   Number of Distinct Medicare Beneficiary/Per Day Services  \\\n",
              "0                                               27.0          \n",
              "1                                              175.0          \n",
              "2                                               32.0          \n",
              "3                                               20.0          \n",
              "4                                               31.0          \n",
              "\n",
              "   Average Medicare Allowed Amount  Average Submitted Charge Amount  \\\n",
              "0                       200.587778                       305.211111   \n",
              "1                       123.730000                       548.800000   \n",
              "2                        90.650000                       155.000000   \n",
              "3                         3.500000                         5.000000   \n",
              "4                        26.520000                        40.000000   \n",
              "\n",
              "   Average Medicare Payment Amount  Average Medicare Standardized Amount  \n",
              "0                       157.262222                            160.908889  \n",
              "1                       118.830000                            135.315257  \n",
              "2                        64.439688                             60.595937  \n",
              "3                         3.430000                              3.430000  \n",
              "4                        19.539394                             19.057576  "
            ],
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              "\n",
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              "      <th>Number of Services</th>\n",
              "      <th>Number of Medicare Beneficiaries</th>\n",
              "      <th>Number of Distinct Medicare Beneficiary/Per Day Services</th>\n",
              "      <th>Average Medicare Allowed Amount</th>\n",
              "      <th>Average Submitted Charge Amount</th>\n",
              "      <th>Average Medicare Payment Amount</th>\n",
              "      <th>Average Medicare Standardized Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>200.587778</td>\n",
              "      <td>305.211111</td>\n",
              "      <td>157.262222</td>\n",
              "      <td>160.908889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>175.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>175.0</td>\n",
              "      <td>123.730000</td>\n",
              "      <td>548.800000</td>\n",
              "      <td>118.830000</td>\n",
              "      <td>135.315257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>90.650000</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>64.439688</td>\n",
              "      <td>60.595937</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>20.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>3.430000</td>\n",
              "      <td>3.430000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>33.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>31.0</td>\n",
              "      <td>26.520000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>19.539394</td>\n",
              "      <td>19.057576</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8d2d23da-76ed-41a2-b53e-28ca50e9760f')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8d2d23da-76ed-41a2-b53e-28ca50e9760f button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8d2d23da-76ed-41a2-b53e-28ca50e9760f');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-177a7e50-1059-4d85-a966-17025a9297f1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-177a7e50-1059-4d85-a966-17025a9297f1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-177a7e50-1059-4d85-a966-17025a9297f1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ISOLATION FOREST"
      ],
      "metadata": {
        "id": "4P1Mh5_Wt33k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## importing StandardScaler\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "std = StandardScaler()\n",
        "new_df = std.fit_transform(df)"
      ],
      "metadata": {
        "id": "05hBVC9ehPSw"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df"
      ],
      "metadata": {
        "id": "n433gb_Zo4F1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4db6f7e9-a835-40c5-a09b-cffecc3f722f"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.08540599, -0.05938359, -0.07027278, ..., -0.0470639 ,\n",
              "         0.3991462 ,  0.41335085],\n",
              "       [-0.0261117 ,  0.07654416,  0.01985582, ...,  0.1819339 ,\n",
              "         0.20691402,  0.2855445 ],\n",
              "       [-0.0834028 , -0.06928561, -0.0672279 , ..., -0.1882773 ,\n",
              "        -0.06513811, -0.08757971],\n",
              "       ...,\n",
              "       [-0.09181618, -0.07108598, -0.08001642, ..., -0.23100909,\n",
              "        -0.31697964, -0.31716873],\n",
              "       [-0.09141555, -0.07018579, -0.07940744, ..., -0.27288625,\n",
              "        -0.23775035, -0.26373638],\n",
              "       [ 0.13975206, -0.01257403,  0.27075437, ..., -0.28993254,\n",
              "        -0.23871633, -0.26077942]])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import IsolationForest"
      ],
      "metadata": {
        "id": "-ilCa4CGwtS3"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf = IsolationForest(max_samples = 2000,random_state=42)"
      ],
      "metadata": {
        "id": "tOBQD5E1wyIJ"
      },
      "execution_count": 283,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = clf.fit(new_df)"
      ],
      "metadata": {
        "id": "az-AclDWxPJw"
      },
      "execution_count": 284,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf.predict(new_df)"
      ],
      "metadata": {
        "id": "Ng-PPTqqxRoW"
      },
      "execution_count": 285,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## function to count the anamoly\n",
        "isolation_anamoly = 0\n",
        "def count_anamoly(x):\n",
        "  anamoly = 0\n",
        "  points = 0\n",
        "  for i in x:\n",
        "    if i == -1:\n",
        "     anamoly = anamoly + 1\n",
        "    else:\n",
        "      points = points + 1\n",
        "\n",
        "  print(\"Anamoly : \", anamoly)\n",
        "  print(\"Normal : \", points)\n",
        "  return anamoly"
      ],
      "metadata": {
        "id": "49Jcg_S7yIkm"
      },
      "execution_count": 292,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "isolation_anamoly = count_anamoly(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y8k2eiO6ynxd",
        "outputId": "8d5b9f96-6ab8-4c32-da0d-677fcafaded5"
      },
      "execution_count": 293,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Anamoly :  2682\n",
            "Normal :  97088\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "w8UVb1Yh8j_8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## taking a small dataset to perform EDA\n",
        "df1 = df.iloc[100 : 1100]"
      ],
      "metadata": {
        "id": "XB5p1TyL0J_1"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "LLJ1pP9YjV9Y",
        "outputId": "2d2d975a-f22c-481d-8305-3a22da75d45f"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Number of Services  Number of Medicare Beneficiaries  \\\n",
              "100                 15.0                              13.0   \n",
              "101                 89.0                              87.0   \n",
              "102                212.0                              33.0   \n",
              "103                145.0                             144.0   \n",
              "104                110.0                             110.0   \n",
              "...                  ...                               ...   \n",
              "1095               849.0                             523.0   \n",
              "1096                12.0                              12.0   \n",
              "1097              1778.0                             355.0   \n",
              "1098                18.0                              15.0   \n",
              "1099                33.0                              33.0   \n",
              "\n",
              "      Number of Distinct Medicare Beneficiary/Per Day Services  \\\n",
              "100                                                15.0          \n",
              "101                                                89.0          \n",
              "102                                               212.0          \n",
              "103                                               145.0          \n",
              "104                                               110.0          \n",
              "...                                                 ...          \n",
              "1095                                              849.0          \n",
              "1096                                               12.0          \n",
              "1097                                             1776.0          \n",
              "1098                                               18.0          \n",
              "1099                                               33.0          \n",
              "\n",
              "      Average Medicare Allowed Amount  Average Submitted Charge Amount  \\\n",
              "100                         49.480000                       120.000000   \n",
              "101                        172.677191                       182.652022   \n",
              "102                         29.730000                        42.000000   \n",
              "103                        223.485310                       400.000000   \n",
              "104                         72.370000                       143.000000   \n",
              "...                               ...                              ...   \n",
              "1095                        22.360000                        91.500000   \n",
              "1096                       326.890000                      1225.000000   \n",
              "1097                       109.921024                       566.366704   \n",
              "1098                       467.600000                      1000.000000   \n",
              "1099                        71.710000                       159.000000   \n",
              "\n",
              "      Average Medicare Payment Amount  Average Medicare Standardized Amount  \n",
              "100                         33.618000                             29.995333  \n",
              "101                        128.851910                            136.278427  \n",
              "102                         19.476085                             19.194009  \n",
              "103                        166.448069                            172.422414  \n",
              "104                         52.875000                             55.835455  \n",
              "...                               ...                                   ...  \n",
              "1095                        21.868292                             21.910000  \n",
              "1096                       153.871667                            298.998333  \n",
              "1097                        85.076406                             86.267379  \n",
              "1098                       362.168333                            297.506111  \n",
              "1099                        54.516364                             59.393030  \n",
              "\n",
              "[1000 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-85da3c19-2356-49bf-8be6-487e2fb51fa0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\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>Number of Services</th>\n",
              "      <th>Number of Medicare Beneficiaries</th>\n",
              "      <th>Number of Distinct Medicare Beneficiary/Per Day Services</th>\n",
              "      <th>Average Medicare Allowed Amount</th>\n",
              "      <th>Average Submitted Charge Amount</th>\n",
              "      <th>Average Medicare Payment Amount</th>\n",
              "      <th>Average Medicare Standardized Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>15.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>49.480000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>33.618000</td>\n",
              "      <td>29.995333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>89.0</td>\n",
              "      <td>87.0</td>\n",
              "      <td>89.0</td>\n",
              "      <td>172.677191</td>\n",
              "      <td>182.652022</td>\n",
              "      <td>128.851910</td>\n",
              "      <td>136.278427</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>212.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>212.0</td>\n",
              "      <td>29.730000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>19.476085</td>\n",
              "      <td>19.194009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>103</th>\n",
              "      <td>145.0</td>\n",
              "      <td>144.0</td>\n",
              "      <td>145.0</td>\n",
              "      <td>223.485310</td>\n",
              "      <td>400.000000</td>\n",
              "      <td>166.448069</td>\n",
              "      <td>172.422414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>104</th>\n",
              "      <td>110.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>110.0</td>\n",
              "      <td>72.370000</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>52.875000</td>\n",
              "      <td>55.835455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1095</th>\n",
              "      <td>849.0</td>\n",
              "      <td>523.0</td>\n",
              "      <td>849.0</td>\n",
              "      <td>22.360000</td>\n",
              "      <td>91.500000</td>\n",
              "      <td>21.868292</td>\n",
              "      <td>21.910000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1096</th>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>326.890000</td>\n",
              "      <td>1225.000000</td>\n",
              "      <td>153.871667</td>\n",
              "      <td>298.998333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1097</th>\n",
              "      <td>1778.0</td>\n",
              "      <td>355.0</td>\n",
              "      <td>1776.0</td>\n",
              "      <td>109.921024</td>\n",
              "      <td>566.366704</td>\n",
              "      <td>85.076406</td>\n",
              "      <td>86.267379</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1098</th>\n",
              "      <td>18.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>467.600000</td>\n",
              "      <td>1000.000000</td>\n",
              "      <td>362.168333</td>\n",
              "      <td>297.506111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1099</th>\n",
              "      <td>33.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>71.710000</td>\n",
              "      <td>159.000000</td>\n",
              "      <td>54.516364</td>\n",
              "      <td>59.393030</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 7 columns</p>\n",
              "</div>\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "      margin-bottom: 4px;\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-85da3c19-2356-49bf-8be6-487e2fb51fa0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  .colab-df-spinner {\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Visualizing the histogram and density plots for the outlier intuition\n",
        "## Bins having less height are assumed to be outlier\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create histograms for each feature in df1 with zoomed-in views\n",
        "plt.figure(figsize=(15, 10))  # Adjust the figure size as needed\n",
        "\n",
        "\n",
        "num_features = len(df1.columns)\n",
        "num_rows = (num_features + 1) // 2\n",
        "num_cols = 2\n",
        "\n",
        "# Loop through each feature and create a histogram in a separate subplot\n",
        "for idx, column in enumerate(df1.columns):\n",
        "    plt.subplot(num_rows, num_cols, idx + 1)\n",
        "    sns.histplot(df1[column], kde=True, bins=20, color='skyblue')\n",
        "    plt.title(f'Histogram + Distribution - {column}')\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plots\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "raQfaOBziMXm",
        "outputId": "a2bfa706-e017-4f08-ddeb-11c6fc2df305"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 7 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Visualizing the histogram and density plots for the outlier intuition\n",
        "## Bins having less height are assumed to be outlier\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Create histograms for each feature in df1 with zoomed-in views\n",
        "plt.figure(figsize=(15, 10))  # Adjust the figure size as needed\n",
        "\n",
        "\n",
        "num_features = len(df1.columns)\n",
        "num_rows = (num_features + 1) // 2\n",
        "num_cols = 2\n",
        "\n",
        "# Loop through each feature and create a histogram in a separate subplot\n",
        "for idx, column in enumerate(df1.columns):\n",
        "    plt.subplot(num_rows, num_cols, idx + 1)\n",
        "    sns.boxplot(df1[column] , color='skyblue')\n",
        "    plt.title(f'Boxplot - {column}')\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Frequency')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "# Show the plots\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        },
        "id": "xT5870Bdve9f",
        "outputId": "85e72dbb-43e9-4554-c6a2-272a271199c9"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 7 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df1,x = \"Average Submitted Charge Amount\")\n",
        "sns.set_palette(\"Set2\")\n",
        "sns.set_style(\"darkgrid\")\n",
        "sns.set_context(\"paper\")\n",
        "plt.title(\"Anamoly in Average Submitted Charge Amount\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "NeNfKa9Xyqne",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "outputId": "5a844605-9b1a-46d5-b491-b419236c9e75"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot of two features\n",
        "plt.scatter(new_df[: , 3], new_df[: , 4], cmap='viridis', s=10, alpha=0.8)\n",
        "plt.plot([0,1],[0,1], color=\"red\")\n",
        "plt.title('Anamoly in Allowed and Charged Amount')\n",
        "plt.xlabel('Average Allowed Amount (scaled)')\n",
        "plt.ylabel('Average Charged Amount (scaled)')\n",
        "plt.show()\n",
        "## line shows the normal behaviour. Points should be around the line . If it's not, there might be an anamoly"
      ],
      "metadata": {
        "id": "lwPQTWV6zll4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "outputId": "5f8648cf-e964-4bcf-948c-d9d4f08b3968"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-59-ea4201ce7118>:2: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(new_df[: , 3], new_df[: , 4], cmap='viridis', s=10, alpha=0.8)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot of two features\n",
        "plt.scatter(new_df[: , 3], new_df[: , 5], cmap='viridis', s=10, alpha=0.8)\n",
        "plt.plot([0,1],[0,1], color=\"red\")\n",
        "plt.title('Anamoly in Allowed and Payment Amount')\n",
        "plt.xlabel('Average Allowed Amount (scaled)')\n",
        "plt.ylabel('Average Payment Amount (scaled)')\n",
        "plt.show()\n",
        "## line shows the normal behaviour. Points should be around the line . If it's not, there might be an anamoly"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "r73yDRVLCs-M",
        "outputId": "38b7ae24-73b9-4487-8ef6-831c61b2e320"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-60-c5e6c4f6fa54>:2: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(new_df[: , 3], new_df[: , 5], cmap='viridis', s=10, alpha=0.8)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Scatter plot of two features\n",
        "plt.scatter(new_df[:, 0], new_df[:, 1], cmap='viridis', s=10, alpha=0.8)\n",
        "plt.plot([0,1],[0,1], color=\"red\")\n",
        "plt.title('Anamoly in Number of Services')\n",
        "plt.xlabel('Number of Services (scaled)')\n",
        "plt.ylabel('Number of Medicare Beneficiaries (scaled)')\n",
        "plt.show()\n",
        "## line shows the normal behaviour. Points should be around the line . If it's not, there might be an anamoly"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 503
        },
        "id": "rcAQgWJzBgRw",
        "outputId": "c2dbf4db-704b-407d-b921-233c83c34246"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-61-bb4fa152d160>:2: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
            "  plt.scatter(new_df[:, 0], new_df[:, 1], cmap='viridis', s=10, alpha=0.8)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K7t3rNxOTJ6M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_curve, average_precision_score\n",
        "# Assuming 'y_scores' is the anomaly scores from the isolation forest\n",
        "# If y_pred contains only -1 and 1, convert it to probability scores for the anomaly class\n",
        "y_scores = clf.decision_function(new_df)\n",
        "\n",
        "precision, recall, _ = precision_recall_curve(y_pred, -y_scores)\n",
        "average_precision = average_precision_score(y_pred, -y_scores)\n",
        "\n",
        "plt.step(recall, precision, color='b', alpha=0.2, where='post')\n",
        "plt.fill_between(recall, precision, step='post', alpha=0.2, color='b')\n",
        "plt.xlabel('Recall')\n",
        "plt.ylabel('Precision')\n",
        "plt.title('Precision-Recall curve: AP={0:0.2f}'.format(average_precision))\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "oH3AdTnRxMno",
        "outputId": "d325c027-ecfa-43eb-f17e-4b4fa764e669"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Local Outlier Factor"
      ],
      "metadata": {
        "id": "5PF8LTbXFo2F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import LocalOutlierFactor"
      ],
      "metadata": {
        "id": "2T1svKbJGDd1"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## neighbours vs anamoly relationship\n",
        "# 1000 - 4735\n",
        "# 2000 - 7404\n",
        "# 2500 - 8290\n",
        "# 2700 - 8628\n",
        "# 2900 - 8968\n",
        "# 3100 - 9275"
      ],
      "metadata": {
        "id": "8vvv9r1TAYEJ"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Assuming you have data for number of neighbors and number of outliers\n",
        "neighbors = [1000,2000,2500,2700,2900,3100]\n",
        "outliers = [4735,7404,8290,8628,8968,9275]\n",
        "\n",
        "# Plotting the relationship between neighbors and outliers\n",
        "plt.plot(neighbors, outliers, marker='o', linestyle='-', color='b')\n",
        "plt.title('Relationship between Number of Neighbors and Number of Outliers')\n",
        "plt.xlabel('Number of Neighbors')\n",
        "plt.ylabel('Number of Outliers')\n",
        "plt.grid(True)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "rr4EZDNuXtTz",
        "outputId": "5d5a79c4-8af5-404d-ec1c-057f5b78bccf"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from scipy.stats import linregress\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Assuming you have data for number of neighbors and number of outliers\n",
        "neighbors = np.array([1000,2000,2500,2700,2900,3100])  # Replace with your actual data\n",
        "outliers = np.array([4735,7404,8290,8628,8968,9275])      # Replace with your actual data\n",
        "\n",
        "# Perform linear regression\n",
        "slope, intercept, r_value, p_value, std_err = linregress(neighbors, outliers)\n",
        "\n",
        "# Plot the data and regression line\n",
        "plt.scatter(neighbors, outliers, color='blue', label='Data points')\n",
        "plt.plot(neighbors, slope * neighbors + intercept, color='red', label='Regression line')\n",
        "plt.title('Linear Regression: Number of Neighbors vs. Number of Outliers')\n",
        "plt.xlabel('Number of Neighbors')\n",
        "plt.ylabel('Number of Outliers')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Print the regression equation\n",
        "print(f\"Regression Equation: y = {slope:.2f} * x + {intercept:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 485
        },
        "id": "L62ESjBvYjPe",
        "outputId": "c5a4fe71-43d1-4168-bb20-b0cdce7c8700"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Regression Equation: y = 2.16 * x + 2770.72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lof = LocalOutlierFactor(contamination = 'auto', n_neighbors=500)"
      ],
      "metadata": {
        "id": "J-8YZJbtsow8"
      },
      "execution_count": 269,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred1 = lof.fit_predict(new_df)"
      ],
      "metadata": {
        "id": "5yAGfDq7sx_2"
      },
      "execution_count": 270,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "num_anomalies = len(y_pred1[y_pred1 == -1])"
      ],
      "metadata": {
        "id": "61XpzI9Ps4gf"
      },
      "execution_count": 271,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of Anamolies using LOF : \",num_anomalies)"
      ],
      "metadata": {
        "id": "Kt2Jr_ogtKrF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f2ab971-ba6e-4b75-b672-b74a8c308e86"
      },
      "execution_count": 272,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Anamolies using LOF :  3707\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lof_scores = lof.negative_outlier_factor_"
      ],
      "metadata": {
        "id": "BuZ-1mEXvcRK"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "# Plot LOF scores\n",
        "plt.scatter(range(len(lof_scores)), lof_scores)\n",
        "plt.xlabel('Data Point Index')\n",
        "plt.ylabel('LOF Score')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "siD9jop2vmkr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "2275c8dd-b925-4459-a8bf-07743a77a8b0"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AUTOENCODER"
      ],
      "metadata": {
        "id": "Co5hNYxTzHJ6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## applying autoencoders to check for anamoly"
      ],
      "metadata": {
        "id": "HGWpNAGRzL_-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow\n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "metadata": {
        "id": "OWzs5eJua6jk"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "from tensorflow.keras.layers import BatchNormalization\n",
        "from sklearn.metrics import accuracy_score\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from tensorflow.keras import Model, Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.losses import MeanSquaredLogarithmicError, MeanSquaredError"
      ],
      "metadata": {
        "id": "XkbXaSDaOEax"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## scaling the data using MinMax Scaling\n",
        "minmaxscaler = StandardScaler()\n",
        "minmaxscaler.fit(df)\n",
        "x_train_scaled = minmaxscaler.transform(df)"
      ],
      "metadata": {
        "id": "APSC378FPyBf"
      },
      "execution_count": 193,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# create a model by subclassing Model class in tensorflow\n",
        "class AutoEncoder(Model):\n",
        "  def __init__(self, output_units, code_size = 8):\n",
        "    super().__init__()\n",
        "    self.encoder = Sequential([\n",
        "      Dense(256, activation='relu'),\n",
        "      Dropout(0.1),\n",
        "      Dense(128, activation='relu'),\n",
        "      Dropout(0.1),\n",
        "      Dense(64, activation='relu'),\n",
        "      Dropout(0.1),\n",
        "      Dense(64, activation='relu'),\n",
        "      Dropout(0.1),\n",
        "      Dense(32, activation='relu'),\n",
        "      Dropout(0.1),\n",
        "      Dense(16, activation='relu'),\n",
        "      BatchNormalization(),\n",
        "      Dense(code_size, activation='relu')\n",
        "    ])\n",
        "    self.decoder = Sequential([\n",
        "      Dense(16, activation='relu'),\n",
        "      Dropout(0.1),\n",
        "      Dense(32, activation='relu'),\n",
        "      Dropout(0.1),\n",
        "      Dense(64, activation='relu'),\n",
        "      Dropout(0.1),\n",
        "      Dense(128, activation='relu'),\n",
        "      Dropout(0.1),\n",
        "      Dense(256, activation='relu'),\n",
        "      Dropout(0.3),\n",
        "      Dense(output_units, activation = 'sigmoid')\n",
        "    ])\n",
        "\n",
        "  def call(self, inputs):\n",
        "    encoded = self.encoder(inputs)\n",
        "    decoded = self.decoder(encoded)\n",
        "    return decoded\n"
      ],
      "metadata": {
        "id": "Qku_7arTQ-Vn"
      },
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model = AutoEncoder(output_units = x_train_scaled.shape[1])\n",
        "# configurations of model\n",
        "model.compile(loss = 'msle', metrics = ['mse'], optimizer = 'adam')\n",
        "\n",
        "history = model.fit(\n",
        "    x_train_scaled,\n",
        "    x_train_scaled,\n",
        "    epochs = 20,\n",
        "    batch_size = x_train_scaled.shape[0]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "035HRmmTVV1j",
        "outputId": "41a9afa7-33ae-483e-f5a6-31cdef5d5462"
      },
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1/1 [==============================] - 4s 4s/step - loss: 0.1647 - mse: 1.2549\n",
            "Epoch 2/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1628 - mse: 1.2482\n",
            "Epoch 3/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1608 - mse: 1.2450\n",
            "Epoch 4/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1579 - mse: 1.2378\n",
            "Epoch 5/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1539 - mse: 1.2289\n",
            "Epoch 6/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1482 - mse: 1.2169\n",
            "Epoch 7/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1411 - mse: 1.2021\n",
            "Epoch 8/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1320 - mse: 1.1834\n",
            "Epoch 9/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1220 - mse: 1.1626\n",
            "Epoch 10/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.1121 - mse: 1.1441\n",
            "Epoch 11/20\n",
            "1/1 [==============================] - 3s 3s/step - loss: 0.1018 - mse: 1.1229\n",
            "Epoch 12/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0911 - mse: 1.1001\n",
            "Epoch 13/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0815 - mse: 1.0810\n",
            "Epoch 14/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0726 - mse: 1.0621\n",
            "Epoch 15/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0646 - mse: 1.0452\n",
            "Epoch 16/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0580 - mse: 1.0300\n",
            "Epoch 17/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0529 - mse: 1.0182\n",
            "Epoch 18/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0488 - mse: 1.0084\n",
            "Epoch 19/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0460 - mse: 1.0007\n",
            "Epoch 20/20\n",
            "1/1 [==============================] - 2s 2s/step - loss: 0.0446 - mse: 0.9960\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_threshold(model, x_train_scaled):\n",
        "  reconstructions = model.predict(x_train_scaled)\n",
        "  # provides losses of individual instances\n",
        "  reconstruction_errors = tensorflow.keras.losses.msle(reconstructions, x_train_scaled)\n",
        "  # threshold for anomaly scores\n",
        "  threshold = np.mean(reconstruction_errors.numpy()) + np.std(reconstruction_errors.numpy())\n",
        "  return threshold\n",
        "\n",
        "def get_predictions(model, x_train_scaled, threshold):\n",
        "  predictions = model.predict(x_train_scaled)\n",
        "  # provides losses of individual instances\n",
        "  errors = tensorflow.keras.losses.msle(predictions, x_train_scaled)\n",
        "  # 0 = anomaly, 1 = normal\n",
        "  anomaly_mask = pd.Series(errors) > threshold\n",
        "  preds = anomaly_mask.map(lambda x: 0.0 if x == True else 1.0)\n",
        "  return preds\n",
        "\n",
        "threshold = find_threshold(model, x_train_scaled)\n",
        "print(f\"Threshold: {threshold}\")\n",
        "predictions = get_predictions(model, x_train_scaled, threshold)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jpP8-cDeS5PB",
        "outputId": "0490e5de-c6ae-44ae-d9a0-29e298cfec17"
      },
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3118/3118 [==============================] - 4s 1ms/step\n",
            "Threshold: 0.27871311941282384\n",
            "3118/3118 [==============================] - 4s 1ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count = 0\n",
        "for i in predictions:\n",
        "  if i == 0:\n",
        "    count = count + 1"
      ],
      "metadata": {
        "id": "_M_SJyZPTajW"
      },
      "execution_count": 258,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Number of Anamolies : \", count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vkf0YVrJThoa",
        "outputId": "70607140-55cf-48c8-a85a-f7254c1812a6"
      },
      "execution_count": 288,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Anamolies :  2505\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Final Observation"
      ],
      "metadata": {
        "id": "K3HNLZ_YDFRy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## plot to compare the anamoly for different plots\n",
        "anamolies = [isolation_anamoly, num_anomalies, count]\n",
        "algo = [\"Isolation Forest\", \"LOF\" ,\"AutoEnoders\"]\n",
        "sns.barplot(x = algo, y = anamolies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "hXOzdLZQ_ulm",
        "outputId": "cface78f-bf34-4906-ac17-2041fbea8b10"
      },
      "execution_count": 295,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 295
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## All the above algorithms are very close to 3% i.e. around 3000 anamoly points"
      ],
      "metadata": {
        "id": "7H2cc_8gD2KO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Completed"
      ],
      "metadata": {
        "id": "8cQ8g3ioEsD6"
      }
    }
  ]
}