Credit_Card_Fraud_Prediction.ipynb

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      "metadata": {
        "id": "uhLgCLN0I1jn"
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      "source": [
        "import pandas as pd"
      ]
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      "source": [
        "credit=pd.read_csv('/content/dataset.csv')"
      ]
    },
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              "   Unnamed: 0 trans_date_trans_time        cc_num  \\\n",
              "0           0      21/06/2020 12:14  2.291160e+15   \n",
              "1           1      21/06/2020 12:14  3.573030e+15   \n",
              "2           2      21/06/2020 12:14  3.598220e+15   \n",
              "3           3      21/06/2020 12:15  3.591920e+15   \n",
              "4           4      21/06/2020 12:15  3.526830e+15   \n",
              "\n",
              "                               merchant        category    amt   first  \\\n",
              "0                 fraud_Kirlin and Sons   personal_care   2.86    Jeff   \n",
              "1                  fraud_Sporer-Keebler   personal_care  29.84  Joanne   \n",
              "2  fraud_Swaniawski, Nitzsche and Welch  health_fitness  41.28  Ashley   \n",
              "3                     fraud_Haley Group        misc_pos  60.05   Brian   \n",
              "4                 fraud_Johnston-Casper          travel   3.19  Nathan   \n",
              "\n",
              "       last gender                       street  ...      lat      long  \\\n",
              "0   Elliott      M            351 Darlene Green  ...  33.9659  -80.9355   \n",
              "1  Williams      F             3638 Marsh Union  ...  40.3207 -110.4360   \n",
              "2     Lopez      F         9333 Valentine Point  ...  40.6729  -73.5365   \n",
              "3  Williams      M  32941 Krystal Mill Apt. 552  ...  28.5697  -80.8191   \n",
              "4    Massey      M     5783 Evan Roads Apt. 465  ...  44.2529  -85.0170   \n",
              "\n",
              "   city_pop                     job         dob  \\\n",
              "0  333497.0     Mechanical engineer  19/03/1968   \n",
              "1     302.0  Sales professional, IT  17/01/1990   \n",
              "2   34496.0       Librarian, public  21/10/1970   \n",
              "3   54767.0            Set designer  25/07/1987   \n",
              "4    1126.0      Furniture designer  06/07/1955   \n",
              "\n",
              "                          trans_num     unix_time  merch_lat  merch_long  \\\n",
              "0  2da90c7d74bd46a0caf3777415b3ebd3  1.371817e+09  33.986391  -81.200714   \n",
              "1  324cc204407e99f51b0d6ca0055005e7  1.371817e+09  39.450498 -109.960431   \n",
              "2  c81755dbbbea9d5c77f094348a7579be  1.371817e+09  40.495810  -74.196111   \n",
              "3  2159175b9efe66dc301f149d3d5abf8c  1.371817e+09  28.812398  -80.883061   \n",
              "4  57ff021bd3f328f8738bb535c302a31b  1.371817e+09  44.959148  -85.884734   \n",
              "\n",
              "   is_fraud  \n",
              "0       0.0  \n",
              "1       0.0  \n",
              "2       0.0  \n",
              "3       0.0  \n",
              "4       0.0  \n",
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              "[5 rows x 23 columns]"
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        "credit.head()"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 61081 entries, 0 to 61080\n",
            "Data columns (total 23 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Unnamed: 0             61081 non-null  int64  \n",
            " 1   trans_date_trans_time  61081 non-null  object \n",
            " 2   cc_num                 61081 non-null  float64\n",
            " 3   merchant               61080 non-null  object \n",
            " 4   category               61080 non-null  object \n",
            " 5   amt                    61080 non-null  float64\n",
            " 6   first                  61080 non-null  object \n",
            " 7   last                   61080 non-null  object \n",
            " 8   gender                 61080 non-null  object \n",
            " 9   street                 61080 non-null  object \n",
            " 10  city                   61080 non-null  object \n",
            " 11  state                  61080 non-null  object \n",
            " 12  zip                    61080 non-null  float64\n",
            " 13  lat                    61080 non-null  float64\n",
            " 14  long                   61080 non-null  float64\n",
            " 15  city_pop               61080 non-null  float64\n",
            " 16  job                    61080 non-null  object \n",
            " 17  dob                    61080 non-null  object \n",
            " 18  trans_num              61080 non-null  object \n",
            " 19  unix_time              61080 non-null  float64\n",
            " 20  merch_lat              61080 non-null  float64\n",
            " 21  merch_long             61080 non-null  float64\n",
            " 22  is_fraud               61080 non-null  float64\n",
            "dtypes: float64(10), int64(1), object(12)\n",
            "memory usage: 10.7+ MB\n"
          ]
        }
      ],
      "source": [
        "credit.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "Ct6jpd_bJWA_",
        "outputId": "c2c6b574-1050-4398-cfcc-01d6197cb230"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Unnamed: 0        cc_num           amt           zip           lat  \\\n",
              "count  61081.000000  6.108100e+04  61080.000000  61080.000000  61080.000000   \n",
              "mean   30540.000000  4.140732e+17     69.337574  48671.094745     38.549021   \n",
              "std    17632.710233  1.304504e+18    145.648981  26824.565958      5.075186   \n",
              "min        0.000000  6.041621e+10      1.000000   1257.000000     20.027100   \n",
              "25%    15270.000000  1.800400e+14      9.700000  26041.000000     34.668900   \n",
              "50%    30540.000000  3.518760e+15     47.190000  48088.000000     39.376400   \n",
              "75%    45810.000000  4.633070e+15     82.860000  72011.000000     42.015800   \n",
              "max    61080.000000  4.992350e+18  13149.150000  99783.000000     65.689900   \n",
              "\n",
              "               long      city_pop     unix_time     merch_lat    merch_long  \\\n",
              "count  61080.000000  6.108000e+04  6.108000e+04  61080.000000  61080.000000   \n",
              "mean     -90.129790  8.959556e+04  1.372705e+09     38.545558    -90.129082   \n",
              "std       13.662924  3.028899e+05  5.353135e+05      5.109413     13.675209   \n",
              "min     -165.672300  2.300000e+01  1.371817e+09     19.042320   -166.646251   \n",
              "25%      -96.786900  7.410000e+02  1.372260e+09     34.755402    -96.825653   \n",
              "50%      -87.410100  2.408000e+03  1.372665e+09     39.379905    -87.361054   \n",
              "75%      -80.158000  1.968500e+04  1.373149e+09     41.978248    -80.203944   \n",
              "max      -67.950300  2.906700e+06  1.373651e+09     66.669356    -66.955602   \n",
              "\n",
              "           is_fraud  \n",
              "count  61080.000000  \n",
              "mean       0.004158  \n",
              "std        0.064353  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        0.000000  \n",
              "75%        0.000000  \n",
              "max        1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d0f9fa7d-4d50-4fd7-a5a9-7ad29468f854\" class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>cc_num</th>\n",
              "      <th>amt</th>\n",
              "      <th>zip</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>city_pop</th>\n",
              "      <th>unix_time</th>\n",
              "      <th>merch_lat</th>\n",
              "      <th>merch_long</th>\n",
              "      <th>is_fraud</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>61081.000000</td>\n",
              "      <td>6.108100e+04</td>\n",
              "      <td>61080.000000</td>\n",
              "      <td>61080.000000</td>\n",
              "      <td>61080.000000</td>\n",
              "      <td>61080.000000</td>\n",
              "      <td>6.108000e+04</td>\n",
              "      <td>6.108000e+04</td>\n",
              "      <td>61080.000000</td>\n",
              "      <td>61080.000000</td>\n",
              "      <td>61080.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>30540.000000</td>\n",
              "      <td>4.140732e+17</td>\n",
              "      <td>69.337574</td>\n",
              "      <td>48671.094745</td>\n",
              "      <td>38.549021</td>\n",
              "      <td>-90.129790</td>\n",
              "      <td>8.959556e+04</td>\n",
              "      <td>1.372705e+09</td>\n",
              "      <td>38.545558</td>\n",
              "      <td>-90.129082</td>\n",
              "      <td>0.004158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>17632.710233</td>\n",
              "      <td>1.304504e+18</td>\n",
              "      <td>145.648981</td>\n",
              "      <td>26824.565958</td>\n",
              "      <td>5.075186</td>\n",
              "      <td>13.662924</td>\n",
              "      <td>3.028899e+05</td>\n",
              "      <td>5.353135e+05</td>\n",
              "      <td>5.109413</td>\n",
              "      <td>13.675209</td>\n",
              "      <td>0.064353</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.041621e+10</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1257.000000</td>\n",
              "      <td>20.027100</td>\n",
              "      <td>-165.672300</td>\n",
              "      <td>2.300000e+01</td>\n",
              "      <td>1.371817e+09</td>\n",
              "      <td>19.042320</td>\n",
              "      <td>-166.646251</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>15270.000000</td>\n",
              "      <td>1.800400e+14</td>\n",
              "      <td>9.700000</td>\n",
              "      <td>26041.000000</td>\n",
              "      <td>34.668900</td>\n",
              "      <td>-96.786900</td>\n",
              "      <td>7.410000e+02</td>\n",
              "      <td>1.372260e+09</td>\n",
              "      <td>34.755402</td>\n",
              "      <td>-96.825653</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>30540.000000</td>\n",
              "      <td>3.518760e+15</td>\n",
              "      <td>47.190000</td>\n",
              "      <td>48088.000000</td>\n",
              "      <td>39.376400</td>\n",
              "      <td>-87.410100</td>\n",
              "      <td>2.408000e+03</td>\n",
              "      <td>1.372665e+09</td>\n",
              "      <td>39.379905</td>\n",
              "      <td>-87.361054</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>45810.000000</td>\n",
              "      <td>4.633070e+15</td>\n",
              "      <td>82.860000</td>\n",
              "      <td>72011.000000</td>\n",
              "      <td>42.015800</td>\n",
              "      <td>-80.158000</td>\n",
              "      <td>1.968500e+04</td>\n",
              "      <td>1.373149e+09</td>\n",
              "      <td>41.978248</td>\n",
              "      <td>-80.203944</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>61080.000000</td>\n",
              "      <td>4.992350e+18</td>\n",
              "      <td>13149.150000</td>\n",
              "      <td>99783.000000</td>\n",
              "      <td>65.689900</td>\n",
              "      <td>-67.950300</td>\n",
              "      <td>2.906700e+06</td>\n",
              "      <td>1.373651e+09</td>\n",
              "      <td>66.669356</td>\n",
              "      <td>-66.955602</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      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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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"credit\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Unnamed: 0\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22013.6689453612,\n        \"min\": 0.0,\n        \"max\": 61081.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          61081.0,\n          30540.0,\n          45810.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.73835818630155e+18,\n        \"min\": 61081.0,\n        \"max\": 4.99235e+18,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.140732184738888e+17,\n          3518760000000000.0,\n          61081.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21408.738509619696,\n        \"min\": 1.0,\n        \"max\": 61080.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          69.33757432874918,\n          47.19,\n          61080.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30577.88415625406,\n        \"min\": 1257.0,\n        \"max\": 99783.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          48671.09474459725,\n          48088.0,\n          61080.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21582.653500227054,\n        \"min\": 5.075186085640654,\n        \"max\": 61080.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.54902109201048,\n          39.3764,\n          61080.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21624.109979474608,\n        \"min\": -165.6723,\n        \"max\": 61080.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -90.12979019810085,\n          -87.4101,\n          61080.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1008684.3955829033,\n        \"min\": 23.0,\n        \"max\": 2906700.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          89595.56177144728,\n          2408.0,\n          61080.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 635302492.0485228,\n        \"min\": 61080.0,\n        \"max\": 1373650803.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1372705426.8162575,\n          1372665420.5,\n          61080.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21582.64986016699,\n        \"min\": 5.109412599108237,\n        \"max\": 61080.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          38.5455583485429,\n          39.379905,\n          61080.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21624.11072230932,\n        \"min\": -166.646251,\n        \"max\": 61080.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          -90.12908177062869,\n          -87.3610545,\n          61080.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"is_fraud\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21594.987132260358,\n        \"min\": 0.0,\n        \"max\": 61080.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.004158480681074001,\n          1.0,\n          0.06435258751608344\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "credit.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RyKO-iV9JO9g",
        "outputId": "7aa161c2-254d-4f9a-ee71-a20ca8b75ffc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'trans_date_trans_time', 'cc_num', 'merchant', 'category',\n",
              "       'amt', 'first', 'last', 'gender', 'street', 'city', 'state', 'zip',\n",
              "       'lat', 'long', 'city_pop', 'job', 'dob', 'trans_num', 'unix_time',\n",
              "       'merch_lat', 'merch_long', 'is_fraud'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "credit.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pc7nCluCJiI6",
        "outputId": "f5159b81-765b-446e-83da-4df4a455cd4a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0               0\n",
              "trans_date_trans_time    0\n",
              "cc_num                   0\n",
              "merchant                 1\n",
              "category                 1\n",
              "amt                      1\n",
              "first                    1\n",
              "last                     1\n",
              "gender                   1\n",
              "street                   1\n",
              "city                     1\n",
              "state                    1\n",
              "zip                      1\n",
              "lat                      1\n",
              "long                     1\n",
              "city_pop                 1\n",
              "job                      1\n",
              "dob                      1\n",
              "trans_num                1\n",
              "unix_time                1\n",
              "merch_lat                1\n",
              "merch_long               1\n",
              "is_fraud                 1\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "credit.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aLs9MAmEJmM7"
      },
      "outputs": [],
      "source": [
        "credit.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wf5nZbOXJpa7",
        "outputId": "2a1757f4-979b-4b11-f4c9-bd28b87dd733"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0               0\n",
              "trans_date_trans_time    0\n",
              "cc_num                   0\n",
              "merchant                 0\n",
              "category                 0\n",
              "amt                      0\n",
              "first                    0\n",
              "last                     0\n",
              "gender                   0\n",
              "street                   0\n",
              "city                     0\n",
              "state                    0\n",
              "zip                      0\n",
              "lat                      0\n",
              "long                     0\n",
              "city_pop                 0\n",
              "job                      0\n",
              "dob                      0\n",
              "trans_num                0\n",
              "unix_time                0\n",
              "merch_lat                0\n",
              "merch_long               0\n",
              "is_fraud                 0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "credit.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f91pBzZsJvIb",
        "outputId": "461a1486-8180-4ed9-aa98-9b915d990ab0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'trans_date_trans_time', 'cc_num', 'merchant', 'category',\n",
              "       'amt', 'first', 'last', 'gender', 'street', 'city', 'state', 'zip',\n",
              "       'lat', 'long', 'city_pop', 'job', 'dob', 'trans_num', 'unix_time',\n",
              "       'merch_lat', 'merch_long', 'is_fraud'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "credit.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGdJLNRvLYuH",
        "outputId": "d551ecce-0bd5-4777-ff8a-1b74473199b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61080, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "credit.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 524
        },
        "id": "1Dutwb7yF-tO",
        "outputId": "1161d211-29f7-4409-eb10-1ef0f569a2cf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "dataset_path = \"/content/dataset.csv\"\n",
        "data = pd.read_csv(dataset_path)\n",
        "categorical_column = 'category'\n",
        "category_counts = data[categorical_column].value_counts()\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', startangle=140)\n",
        "plt.title(f'Distribution of {categorical_column}')\n",
        "plt.axis('equal')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "dataset_path = \"/content/dataset.csv\"\n",
        "data = pd.read_csv(dataset_path)\n",
        "categorical_column = 'amt'\n",
        "category_counts = data[categorical_column].value_counts()\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.pie(category_counts, labels=category_counts.index, autopct='%1.1f%%', startangle=140)\n",
        "plt.title(f'Distribution of {categorical_column}')\n",
        "plt.axis('equal')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "buVbbKOiS9FJ",
        "outputId": "d43e841d-e09e-4c2c-8217-c3b4b7384c28"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3AbVGWNkMRso"
      },
      "outputs": [],
      "source": [
        "X = credit[['cc_num', 'amt', 'zip', 'lat', 'long', 'city_pop', 'unix_time', 'merch_lat', 'merch_long']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "uLvquSsFMTx9",
        "outputId": "f13cc433-1004-4bf7-87d4-83181924ecc0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         cc_num    amt      zip      lat      long  city_pop     unix_time  \\\n",
              "0  2.291160e+15   2.86  29209.0  33.9659  -80.9355  333497.0  1.371817e+09   \n",
              "1  3.573030e+15  29.84  84002.0  40.3207 -110.4360     302.0  1.371817e+09   \n",
              "2  3.598220e+15  41.28  11710.0  40.6729  -73.5365   34496.0  1.371817e+09   \n",
              "3  3.591920e+15  60.05  32780.0  28.5697  -80.8191   54767.0  1.371817e+09   \n",
              "4  3.526830e+15   3.19  49632.0  44.2529  -85.0170    1126.0  1.371817e+09   \n",
              "\n",
              "   merch_lat  merch_long  \n",
              "0  33.986391  -81.200714  \n",
              "1  39.450498 -109.960431  \n",
              "2  40.495810  -74.196111  \n",
              "3  28.812398  -80.883061  \n",
              "4  44.959148  -85.884734  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53da68bc-3e8a-41b2-8cd1-4ae576339cc9\" 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>cc_num</th>\n",
              "      <th>amt</th>\n",
              "      <th>zip</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>city_pop</th>\n",
              "      <th>unix_time</th>\n",
              "      <th>merch_lat</th>\n",
              "      <th>merch_long</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.291160e+15</td>\n",
              "      <td>2.86</td>\n",
              "      <td>29209.0</td>\n",
              "      <td>33.9659</td>\n",
              "      <td>-80.9355</td>\n",
              "      <td>333497.0</td>\n",
              "      <td>1.371817e+09</td>\n",
              "      <td>33.986391</td>\n",
              "      <td>-81.200714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3.573030e+15</td>\n",
              "      <td>29.84</td>\n",
              "      <td>84002.0</td>\n",
              "      <td>40.3207</td>\n",
              "      <td>-110.4360</td>\n",
              "      <td>302.0</td>\n",
              "      <td>1.371817e+09</td>\n",
              "      <td>39.450498</td>\n",
              "      <td>-109.960431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.598220e+15</td>\n",
              "      <td>41.28</td>\n",
              "      <td>11710.0</td>\n",
              "      <td>40.6729</td>\n",
              "      <td>-73.5365</td>\n",
              "      <td>34496.0</td>\n",
              "      <td>1.371817e+09</td>\n",
              "      <td>40.495810</td>\n",
              "      <td>-74.196111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.591920e+15</td>\n",
              "      <td>60.05</td>\n",
              "      <td>32780.0</td>\n",
              "      <td>28.5697</td>\n",
              "      <td>-80.8191</td>\n",
              "      <td>54767.0</td>\n",
              "      <td>1.371817e+09</td>\n",
              "      <td>28.812398</td>\n",
              "      <td>-80.883061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.526830e+15</td>\n",
              "      <td>3.19</td>\n",
              "      <td>49632.0</td>\n",
              "      <td>44.2529</td>\n",
              "      <td>-85.0170</td>\n",
              "      <td>1126.0</td>\n",
              "      <td>1.371817e+09</td>\n",
              "      <td>44.959148</td>\n",
              "      <td>-85.884734</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-53da68bc-3e8a-41b2-8cd1-4ae576339cc9');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      border-color: transparent;\n",
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              "      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",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X",
              "summary": "{\n  \"name\": \"X\",\n  \"rows\": 61080,\n  \"fields\": [\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3045136295975485e+18,\n        \"min\": 60416207185.0,\n        \"max\": 4.99235e+18,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          4800400000000000.0,\n          4.98032e+18,\n          30596500000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 145.64898052378717,\n        \"min\": 1.0,\n        \"max\": 13149.15,\n        \"num_unique_values\": 17395,\n        \"samples\": [\n          102.72,\n          32.52,\n          126.49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26824.56595797012,\n        \"min\": 1257.0,\n        \"max\": 99783.0,\n        \"num_unique_values\": 900,\n        \"samples\": [\n          56029.0,\n          1612.0,\n          46765.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.075186085640654,\n        \"min\": 20.0271,\n        \"max\": 65.6899,\n        \"num_unique_values\": 898,\n        \"samples\": [\n          32.2768,\n          47.7068,\n          41.4682\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.662924380559417,\n        \"min\": -165.6723,\n        \"max\": -67.9503,\n        \"num_unique_values\": 898,\n        \"samples\": [\n          -95.3031,\n          -122.6847,\n          -72.5751\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 302889.86560516694,\n        \"min\": 23.0,\n        \"max\": 2906700.0,\n        \"num_unique_values\": 825,\n        \"samples\": [\n          254.0,\n          20478.0,\n          124967.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 535313.4904080923,\n        \"min\": 1371816865.0,\n        \"max\": 1373650803.0,\n        \"num_unique_values\": 59941,\n        \"samples\": [\n          1373110945.0,\n          1372416634.0,\n          1373566202.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.109412599108237,\n        \"min\": 19.04232,\n        \"max\": 66.669356,\n        \"num_unique_values\": 60963,\n        \"samples\": [\n          37.586158,\n          32.999756,\n          35.677546\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.675209016939077,\n        \"min\": -166.646251,\n        \"max\": -66.955602,\n        \"num_unique_values\": 61022,\n        \"samples\": [\n          -97.937608,\n          -111.817914,\n          -94.881264\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "POVVG0hXMYWE",
        "outputId": "e3a6000e-a290-4606-b50e-47d66fece469"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61080, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U1JFj8K2K8ND"
      },
      "outputs": [],
      "source": [
        "y=credit['is_fraud']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLEGYSDzMav3",
        "outputId": "1874542b-a21a-48b9-ac2a-782e17eab4e5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.0\n",
              "1    0.0\n",
              "2    0.0\n",
              "3    0.0\n",
              "4    0.0\n",
              "Name: is_fraud, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_c-Lz3yDL3la",
        "outputId": "593e9739-f80d-4a07-a85c-e6043a5eb97a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61080,)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XGkiWQmzMfAH"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bWUvnDfDMnz_"
      },
      "outputs": [],
      "source": [
        "X_train,X_test,y_train,y_test=train_test_split(X,y,train_size=0.8,random_state=2529)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "FrMa0whZM3-T",
        "outputId": "3f15971f-146b-41cb-dea3-ad5d538bbc55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             cc_num     amt      zip      lat      long  city_pop  \\\n",
              "18209  3.596220e+15   11.19  52535.0  40.8626  -91.9534     888.0   \n",
              "15477  3.483790e+14    8.79  32935.0  28.1384  -80.6524  139650.0   \n",
              "60751  1.800140e+14    4.30  36009.0  31.5710  -86.2743    2566.0   \n",
              "3731   3.573030e+15    5.57  84002.0  40.3207 -110.4360     302.0   \n",
              "2207   6.304250e+11  240.16  26292.0  39.1505  -79.5030     836.0   \n",
              "...             ...     ...      ...      ...       ...       ...   \n",
              "3656   3.586010e+15   15.18  98027.0  47.4974 -122.0107   50489.0   \n",
              "22927  4.223710e+18   32.20  45051.0  39.0965  -84.6431     177.0   \n",
              "35644  3.518760e+15    4.82  84532.0  38.5677 -109.5271    9772.0   \n",
              "7730   2.235610e+15   20.41  82227.0  42.7300 -104.7024     241.0   \n",
              "13664  3.513290e+15   16.74  25832.0  37.7256  -81.1087    3994.0   \n",
              "\n",
              "          unix_time  merch_lat  merch_long  \n",
              "18209  1.372364e+09  40.543464  -91.183282  \n",
              "15477  1.372266e+09  29.100059  -80.930959  \n",
              "60751  1.373644e+09  31.921644  -85.486267  \n",
              "3731   1.371905e+09  40.848808 -109.693167  \n",
              "2207   1.371865e+09  38.991707  -79.369797  \n",
              "...             ...        ...         ...  \n",
              "3656   1.371903e+09  48.244457 -122.173741  \n",
              "22927  1.372486e+09  38.941572  -84.009725  \n",
              "35644  1.372878e+09  38.016136 -110.225980  \n",
              "7730   1.371991e+09  43.179440 -104.165832  \n",
              "13664  1.372197e+09  37.499901  -81.980382  \n",
              "\n",
              "[48864 rows x 9 columns]"
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              "      <th>merch_long</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>18209</th>\n",
              "      <td>3.596220e+15</td>\n",
              "      <td>11.19</td>\n",
              "      <td>52535.0</td>\n",
              "      <td>40.8626</td>\n",
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              "      <td>31.921644</td>\n",
              "      <td>-85.486267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3731</th>\n",
              "      <td>3.573030e+15</td>\n",
              "      <td>5.57</td>\n",
              "      <td>84002.0</td>\n",
              "      <td>40.3207</td>\n",
              "      <td>-110.4360</td>\n",
              "      <td>302.0</td>\n",
              "      <td>1.371905e+09</td>\n",
              "      <td>40.848808</td>\n",
              "      <td>-109.693167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2207</th>\n",
              "      <td>6.304250e+11</td>\n",
              "      <td>240.16</td>\n",
              "      <td>26292.0</td>\n",
              "      <td>39.1505</td>\n",
              "      <td>-79.5030</td>\n",
              "      <td>836.0</td>\n",
              "      <td>1.371865e+09</td>\n",
              "      <td>38.991707</td>\n",
              "      <td>-79.369797</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3656</th>\n",
              "      <td>3.586010e+15</td>\n",
              "      <td>15.18</td>\n",
              "      <td>98027.0</td>\n",
              "      <td>47.4974</td>\n",
              "      <td>-122.0107</td>\n",
              "      <td>50489.0</td>\n",
              "      <td>1.371903e+09</td>\n",
              "      <td>48.244457</td>\n",
              "      <td>-122.173741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22927</th>\n",
              "      <td>4.223710e+18</td>\n",
              "      <td>32.20</td>\n",
              "      <td>45051.0</td>\n",
              "      <td>39.0965</td>\n",
              "      <td>-84.6431</td>\n",
              "      <td>177.0</td>\n",
              "      <td>1.372486e+09</td>\n",
              "      <td>38.941572</td>\n",
              "      <td>-84.009725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35644</th>\n",
              "      <td>3.518760e+15</td>\n",
              "      <td>4.82</td>\n",
              "      <td>84532.0</td>\n",
              "      <td>38.5677</td>\n",
              "      <td>-109.5271</td>\n",
              "      <td>9772.0</td>\n",
              "      <td>1.372878e+09</td>\n",
              "      <td>38.016136</td>\n",
              "      <td>-110.225980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7730</th>\n",
              "      <td>2.235610e+15</td>\n",
              "      <td>20.41</td>\n",
              "      <td>82227.0</td>\n",
              "      <td>42.7300</td>\n",
              "      <td>-104.7024</td>\n",
              "      <td>241.0</td>\n",
              "      <td>1.371991e+09</td>\n",
              "      <td>43.179440</td>\n",
              "      <td>-104.165832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13664</th>\n",
              "      <td>3.513290e+15</td>\n",
              "      <td>16.74</td>\n",
              "      <td>25832.0</td>\n",
              "      <td>37.7256</td>\n",
              "      <td>-81.1087</td>\n",
              "      <td>3994.0</td>\n",
              "      <td>1.372197e+09</td>\n",
              "      <td>37.499901</td>\n",
              "      <td>-81.980382</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>48864 rows × 9 columns</p>\n",
              "</div>\n",
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              "\n",
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              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    border-color: transparent;\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\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",
              "      } 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",
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              "    (() => {\n",
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              "        document.querySelector('#df-083d023d-dcc6-4a71-bccc-d7854ebc66f5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7f8f8b66-7d77-4be1-a088-81f8c65ee26a\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        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-generate: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",
              "      [theme=dark] .colab-df-generate {\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_train')\"\n",
              "            title=\"Generate code using this dataframe.\"\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",
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              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7f8f8b66-7d77-4be1-a088-81f8c65ee26a button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('X_train');\n",
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              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "X_train",
              "summary": "{\n  \"name\": \"X_train\",\n  \"rows\": 48864,\n  \"fields\": [\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.30364197344685e+18,\n        \"min\": 60416207185.0,\n        \"max\": 4.99235e+18,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          38057500000000.0,\n          3591920000000000.0,\n          30560600000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135.41000943683872,\n        \"min\": 1.0,\n        \"max\": 8021.63,\n        \"num_unique_values\": 15971,\n        \"samples\": [\n          556.59,\n          536.83,\n          74.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26795.908401824465,\n        \"min\": 1257.0,\n        \"max\": 99783.0,\n        \"num_unique_values\": 900,\n        \"samples\": [\n          28117.0,\n          20882.0,\n          92561.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.080036377944316,\n        \"min\": 20.0271,\n        \"max\": 65.6899,\n        \"num_unique_values\": 898,\n        \"samples\": [\n          46.1664,\n          33.4235,\n          47.5375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.637318857835556,\n        \"min\": -165.6723,\n        \"max\": -67.9503,\n        \"num_unique_values\": 898,\n        \"samples\": [\n          -103.7079,\n          -96.3398,\n          -95.9941\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 301565.75103238504,\n        \"min\": 23.0,\n        \"max\": 2906700.0,\n        \"num_unique_values\": 825,\n        \"samples\": [\n          73.0,\n          1131.0,\n          13061.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 536007.07817396,\n        \"min\": 1371816873.0,\n        \"max\": 1373650803.0,\n        \"num_unique_values\": 48131,\n        \"samples\": [\n          1373562313.0,\n          1372834557.0,\n          1373199444.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.1121514026247805,\n        \"min\": 19.04232,\n        \"max\": 66.669356,\n        \"num_unique_values\": 48790,\n        \"samples\": [\n          31.328991,\n          36.383511,\n          33.870957\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.647536597029013,\n        \"min\": -166.646251,\n        \"max\": -66.955602,\n        \"num_unique_values\": 48833,\n        \"samples\": [\n          -82.367103,\n          -90.057274,\n          -94.514128\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Y-SS6BXgM6MT",
        "outputId": "a9f0e783-0b8d-4213-a2ce-d0696962da93"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             cc_num     amt      zip      lat      long  city_pop  \\\n",
              "46719  4.170690e+15   82.00  73034.0  35.6665  -97.4798  116001.0   \n",
              "39321  3.026350e+13  158.41  84735.0  37.7175 -112.4777     258.0   \n",
              "37472  6.540980e+15   95.59  48221.0  42.4260  -83.1500  673342.0   \n",
              "10433  3.450600e+14    5.44   1330.0  42.5232  -72.8110    1506.0   \n",
              "51923  3.782790e+14  112.71  97403.0  44.0385 -123.0614  191096.0   \n",
              "...             ...     ...      ...      ...       ...       ...   \n",
              "1820   4.926380e+15  141.81  72529.0  36.3011  -91.5281    4726.0   \n",
              "44880  3.710090e+14   42.37  31771.0  31.2462  -83.6549    5666.0   \n",
              "5585   4.653880e+15    1.82  42171.0  37.0581  -86.1938    6841.0   \n",
              "31402  2.252060e+15   89.00  54441.0  44.6437  -90.1031     828.0   \n",
              "60451  4.658490e+15    4.66  15449.0  39.9636  -79.7853     184.0   \n",
              "\n",
              "          unix_time  merch_lat  merch_long  \n",
              "46719  1.373171e+09  36.359754  -96.679931  \n",
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              "summary": "{\n  \"name\": \"X_test\",\n  \"rows\": 12216,\n  \"fields\": [\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.3080446349283464e+18,\n        \"min\": 60416207185.0,\n        \"max\": 4.99235e+18,\n        \"num_unique_values\": 890,\n        \"samples\": [\n          3542160000000000.0,\n          3560800000000000.0,\n          2296010000000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 180.90322523846635,\n        \"min\": 1.0,\n        \"max\": 13149.15,\n        \"num_unique_values\": 7630,\n        \"samples\": [\n          28.51,\n          1.85,\n          114.49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26938.24755349507,\n        \"min\": 1257.0,\n        \"max\": 99783.0,\n        \"num_unique_values\": 899,\n        \"samples\": [\n          3905.0,\n          51003.0,\n          52563.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.05592222282846,\n        \"min\": 20.0271,\n        \"max\": 65.6899,\n        \"num_unique_values\": 897,\n        \"samples\": [\n          40.2967,\n          38.4121,\n          65.6899\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.76385622217843,\n        \"min\": -165.6723,\n        \"max\": -67.9503,\n        \"num_unique_values\": 897,\n        \"samples\": [\n          -98.737,\n          -82.8823,\n          -156.292\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 308141.27783994004,\n        \"min\": 23.0,\n        \"max\": 2906700.0,\n        \"num_unique_values\": 824,\n        \"samples\": [\n          36438.0,\n          3807.0,\n          237282.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 532523.6790987063,\n        \"min\": 1371816865.0,\n        \"max\": 1373650715.0,\n        \"num_unique_values\": 12162,\n        \"samples\": [\n          1372457437.0,\n          1372694588.0,\n          1372563836.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.0986475504049995,\n        \"min\": 19.147292,\n        \"max\": 66.609866,\n        \"num_unique_values\": 12208,\n        \"samples\": [\n          33.757358,\n          39.387984,\n          38.339295\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.7843411978936,\n        \"min\": -166.547963,\n        \"max\": -67.122946,\n        \"num_unique_values\": 12215,\n        \"samples\": [\n          -92.930413,\n          -83.071135,\n          -99.667001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "X_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IxGl9ctaM728",
        "outputId": "91871005-da3f-4492-9f21-108a046a6084"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18209    0.0\n",
              "15477    0.0\n",
              "60751    0.0\n",
              "3731     0.0\n",
              "2207     0.0\n",
              "        ... \n",
              "3656     0.0\n",
              "22927    0.0\n",
              "35644    0.0\n",
              "7730     0.0\n",
              "13664    0.0\n",
              "Name: is_fraud, Length: 48864, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "y_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdXGdzgaM9dp",
        "outputId": "18c5fad0-e786-4ec1-b5ac-e337a88e27dd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "46719    0.0\n",
              "39321    0.0\n",
              "37472    0.0\n",
              "10433    0.0\n",
              "51923    0.0\n",
              "        ... \n",
              "1820     0.0\n",
              "44880    0.0\n",
              "5585     0.0\n",
              "31402    0.0\n",
              "60451    0.0\n",
              "Name: is_fraud, Length: 12216, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "y_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EfxNuX8dNE8q"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vAM1NGobNRzx"
      },
      "outputs": [],
      "source": [
        "RF=RandomForestClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "0FNUquVkNWYx",
        "outputId": "4fe7e29e-ca10-478b-d4e7-bcfbb5b6bc55"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "RF"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "7EEL1DFaNc_M",
        "outputId": "266369ef-8e5e-472d-9774-b81155717814"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "RF.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "qa2GOerFN2y2",
        "outputId": "9ff95074-4a2c-4e64-f751-b495ad74cf06"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "RandomForestClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBQz5D-COUnH",
        "outputId": "74528090-2e02-432e-b16e-6f3ccf7b0462"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "y_pred=RF.predict(X_test)\n",
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DSK-gptOehk",
        "outputId": "306a673b-ec85-4035-b1a6-c3e2d5fd5c48"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12216,)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "y_pred.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uG-NTPAIOhAM"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OnfG-G_FO3hV",
        "outputId": "a696402f-8c31-474e-870f-6f2577f9bc02"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12165,     1],\n",
              "       [   21,    29]])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "cm=confusion_matrix(y_test,y_pred)\n",
        "cm"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.heatmap(cm, annot=True, fmt='g')\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "L4QQbLpkZAV6",
        "outputId": "df3f5353-3322-4700-9d63-d43f9ac4a3f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NIGsu7qlPChK"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6CztsEFgPIf_",
        "outputId": "cb97c135-0aa9-4b4a-820a-9e5a46347a7d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9981990831696136"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "Accuracy=accuracy_score(y_test,y_pred)\n",
        "Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jS4hxHXJPSnY"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VWbVz_8jPdeI"
      },
      "outputs": [],
      "source": [
        "CR=classification_report(y_test,y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "S4zzzznqPkbC",
        "outputId": "70be8343-43de-405d-ec60-7e4cb4729e2c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n         0.0       1.00      1.00      1.00     12166\\n         1.0       0.97      0.58      0.72        50\\n\\n    accuracy                           1.00     12216\\n   macro avg       0.98      0.79      0.86     12216\\nweighted avg       1.00      1.00      1.00     12216\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "CR"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score, f1_score"
      ],
      "metadata": {
        "id": "SUoFwtiM_Kc_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "f1score = f1_score(y_test, y_pred)\n",
        "print(f\"Precision = {precision:.4f}\")\n",
        "print(f\"Recall = {recall:.4f}\")\n",
        "print(f\"F1 Score = {f1score:.4f}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSq4XV2p_EDp",
        "outputId": "7936c84e-509a-4a48-e7a7-482cabc65347"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision = 0.9667\n",
            "Recall = 0.5800\n",
            "F1 Score = 0.7250\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uzyriz9pSzQR"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "u4BILet0S_Dp",
        "outputId": "594daba1-47b2-4b55-da2e-a055251f705c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "SVM=SVC()\n",
        "SVM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "GbhaTM_WTCI1",
        "outputId": "a7eba503-9d37-4a74-a144-5c1165462d70"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "SVM.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "MMbhAUzCTKDz",
        "outputId": "22787a34-dea1-4bb8-9ed3-0af0b5d44f6e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "SVC()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbgPwjs_TLnY",
        "outputId": "65cec39b-67de-49ed-8c6d-cdd68514e04c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "y_pred1=SVM.predict(X_test)\n",
        "y_pred1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "O8KhvmUjToGr"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xt5c5z3jTzLi",
        "outputId": "1fd81c3a-080a-4745-d109-9cda9e37bdf2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12166,     0],\n",
              "       [   50,     0]])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "cm1=confusion_matrix(y_test,y_pred1)\n",
        "cm1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.heatmap(cm1, annot=True, fmt='g')\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "I4yQSCKoZR9U",
        "outputId": "8f22944a-747f-493d-976b-4f61da210c47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0GM-XrlbT67_",
        "outputId": "bbfb6186-7262-4dd3-9525-2cecda2a835d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9959070072036673"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "Accuracy=accuracy_score(y_test,y_pred1)\n",
        "Accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "uoo3evXQUE0Y",
        "outputId": "1165f0db-b77f-4e47-dbe9-ea9ab98e69b5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n         0.0       1.00      1.00      1.00     12166\\n         1.0       0.00      0.00      0.00        50\\n\\n    accuracy                           1.00     12216\\n   macro avg       0.50      0.50      0.50     12216\\nweighted avg       0.99      1.00      0.99     12216\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "CR1=classification_report(y_test,y_pred1)\n",
        "CR1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "precision = precision_score(y_test, y_pred1, average='weighted')\n",
        "recall = recall_score(y_test, y_pred1, average='weighted')\n",
        "f1score = f1_score(y_test, y_pred1, average='weighted')\n",
        "\n",
        "print(f\"Precision = {precision:.4f}\")\n",
        "print(f\"Recall = {recall:.4f}\")\n",
        "print(f\"F1 Score = {f1score:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MVm00qPd_lVY",
        "outputId": "bdd8958f-5221-41f8-b9d6-518e928bb1e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision = 0.9918\n",
            "Recall = 0.9959\n",
            "F1 Score = 0.9939\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "0FB3KgG1eLCy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LR=LogisticRegression()\n",
        "LR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "gs8DFVsdeWIM",
        "outputId": "cb842e12-6ca6-4e34-b111-717fa3cb4924"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LR.fit(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 214
        },
        "id": "yvVdavSsebth",
        "outputId": "9a983549-5f97-4a61-b754-fc24952eb239"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: ConvergenceWarning: lbfgs failed to converge (status=2):\n",
            "ABNORMAL_TERMINATION_IN_LNSRCH.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "source": [
        "LR.set_params(max_iter=500)"
      ],
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "svkN2ePce4wE",
        "outputId": "c7e793f8-c561-4136-f350-1f58fb38f36f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(max_iter=500)"
            ],
            "text/html": [
              "<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" checked><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=500)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred2=LR.predict(X_test)\n",
        "y_pred2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WmBinmbLe7xY",
        "outputId": "765ff0b7-57f2-4257-ca70-4f92e862914a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report"
      ],
      "metadata": {
        "id": "tfGeEasVfEUT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm2=confusion_matrix(y_test,y_pred2)\n",
        "cm2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ISc_X7m8fhI4",
        "outputId": "bcfb5a66-bec7-4760-d2e6-dfd1802603e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12166,     0],\n",
              "       [   50,     0]])"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "sns.heatmap(cm2, annot=True, fmt='g')\n",
        "plt.xlabel('Predicted labels')\n",
        "plt.ylabel('True labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "EsyBqeq5ftaL",
        "outputId": "1a73a3d5-08d1-4a5f-89b3-b46aa51957f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy=accuracy_score(y_test,y_pred2)\n",
        "Accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e4Q9mQ4ZgAiJ",
        "outputId": "a9061f58-7c8b-4fff-8e39-aa1a50de924f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9959070072036673"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CR2=classification_report(y_test,y_pred2)\n",
        "CR2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "D4-4hIcOgT0v",
        "outputId": "01dc398a-08b2-4745-a3d0-f2e413f49987"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n         0.0       1.00      1.00      1.00     12166\\n         1.0       0.00      0.00      0.00        50\\n\\n    accuracy                           1.00     12216\\n   macro avg       0.50      0.50      0.50     12216\\nweighted avg       0.99      1.00      0.99     12216\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision = precision_score(y_test, y_pred2, average='weighted')\n",
        "recall = recall_score(y_test, y_pred2, average='weighted')\n",
        "f1score = f1_score(y_test, y_pred2, average='weighted')\n",
        "print(f\"Precision = {precision:.4f}\")\n",
        "print(f\"Recall = {recall:.4f}\")\n",
        "print(f\"F1 Score = {f1score:.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tc-T-_LCAFaO",
        "outputId": "80f0560f-a117-4d6c-92d7-c5509f62d589"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision = 0.9918\n",
            "Recall = 0.9959\n",
            "F1 Score = 0.9939\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n"
      ],
      "metadata": {
        "id": "_V3vvrrIgmod"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "NB=GaussianNB()\n",
        "NB"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "dYsRZTf5gwnt",
        "outputId": "1a026d33-4f0c-4204-ba3a-35dbd04f6bdb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {color: black;background-color: white;}#sk-container-id-10 pre{padding: 0;}#sk-container-id-10 div.sk-toggleable {background-color: white;}#sk-container-id-10 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-10 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-10 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-10 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-10 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-10 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-10 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-10 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-10 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-10 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-10 div.sk-item {position: relative;z-index: 1;}#sk-container-id-10 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-10 div.sk-item::before, #sk-container-id-10 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-10 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-10 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-10 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-10 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-10 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-10 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-10 div.sk-label-container {text-align: center;}#sk-container-id-10 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-10 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "NB.fit(X_train,y_train)\n",
        "NB"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "crWKEwpRg1Mx",
        "outputId": "14b4f2a4-e6cc-4878-e75e-2f6f9ec1d477"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB()"
            ],
            "text/html": [
              "<style>#sk-container-id-11 {color: black;background-color: white;}#sk-container-id-11 pre{padding: 0;}#sk-container-id-11 div.sk-toggleable {background-color: white;}#sk-container-id-11 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-11 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-11 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-11 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-11 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-11 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-11 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-11 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-11 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-11 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-11 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-11 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-11 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-11 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-11 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-11 div.sk-item {position: relative;z-index: 1;}#sk-container-id-11 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-11 div.sk-item::before, #sk-container-id-11 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-11 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-11 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-11 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-11 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-11 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-11 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-11 div.sk-label-container {text-align: center;}#sk-container-id-11 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-11 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-11\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred3=NB.predict(X_test)\n",
        "y_pred3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOikrpFlg_f8",
        "outputId": "073666e8-93e9-459a-d71c-b5778548052c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report"
      ],
      "metadata": {
        "id": "oNQ-ySoPhG_I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CM3=confusion_matrix(y_test,y_pred3)\n",
        "CM3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vB_NHqIIhlsS",
        "outputId": "ef701c41-86af-4111-dff1-a7f9f65f4e3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12166,     0],\n",
              "       [   50,     0]])"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy=accuracy_score(y_test,y_pred)\n",
        "Accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfzEpNvlhsnR",
        "outputId": "7461f221-8c90-4592-deb4-29eedbb98d74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9981990831696136"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CR3=classification_report(y_test,y_pred3)\n",
        "CR3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "Rm7JDl0Mh09g",
        "outputId": "235aaaca-f4f3-4510-ef7f-f5d9cf8ae295"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n         0.0       1.00      1.00      1.00     12166\\n         1.0       0.00      0.00      0.00        50\\n\\n    accuracy                           1.00     12216\\n   macro avg       0.50      0.50      0.50     12216\\nweighted avg       0.99      1.00      0.99     12216\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision = precision_score(y_test, y_pred3, average='weighted')\n",
        "recall = recall_score(y_test, y_pred3, average='weighted')\n",
        "f1score = f1_score(y_test, y_pred3, average='weighted')\n",
        "print(f\"Precision = {precision:.4f}\")\n",
        "print(f\"Recall = {recall:.4f}\")\n",
        "print(f\"F1 Score = {f1score:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmCVUsiKAZ_c",
        "outputId": "3848c3ce-6824-4f15-a61a-3baa7413ec43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision = 0.9918\n",
            "Recall = 0.9959\n",
            "F1 Score = 0.9939\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import SGDClassifier"
      ],
      "metadata": {
        "id": "pJGl9w9XiZVh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "SGD=SGDClassifier()\n",
        "SGD"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "OyEROYYeiiRo",
        "outputId": "f049dfce-c685-45b8-c532-82be08b493d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-12 {color: black;background-color: white;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SGD.fit(X_train,y_train)\n",
        "SGD"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "0Se6qy8Ginoy",
        "outputId": "d5e182be-75c4-4646-df1a-8a2f4ab69f5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SGDClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-13 {color: black;background-color: white;}#sk-container-id-13 pre{padding: 0;}#sk-container-id-13 div.sk-toggleable {background-color: white;}#sk-container-id-13 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-13 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-13 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-13 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-13 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-13 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-13 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-13 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-13 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-13 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-13 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-13 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-13 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-13 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-13 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-13 div.sk-item {position: relative;z-index: 1;}#sk-container-id-13 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-13 div.sk-item::before, #sk-container-id-13 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-13 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-13 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-13 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-13 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-13 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-13 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-13 div.sk-label-container {text-align: center;}#sk-container-id-13 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-13 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-13\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SGDClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" checked><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SGDClassifier</label><div class=\"sk-toggleable__content\"><pre>SGDClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred4=SGD.predict(X_test)\n",
        "y_pred4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9zW7sz8aiyDp",
        "outputId": "2c2b7e9d-da69-4a7a-963a-2fd0eceb846b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report"
      ],
      "metadata": {
        "id": "F4u0_tCDi6os"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm4=confusion_matrix(y_test,y_pred4)\n",
        "cm4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLHyowG2jEOB",
        "outputId": "a8f61b09-9b0a-4f24-e04c-20fd5c08d0d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12166,     0],\n",
              "       [   50,     0]])"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy=accuracy_score(y_test,y_pred4)\n",
        "Accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B6m2SJpijJ5P",
        "outputId": "c88ee9da-f2c1-4ecf-c2a1-2ade4c327d93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9959070072036673"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CR4=classification_report(y_test,y_pred4)\n",
        "CR4"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "wI0zVErdjVTy",
        "outputId": "7af364ef-8042-4209-e2c4-47e1fa2ba5aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n         0.0       1.00      1.00      1.00     12166\\n         1.0       0.00      0.00      0.00        50\\n\\n    accuracy                           1.00     12216\\n   macro avg       0.50      0.50      0.50     12216\\nweighted avg       0.99      1.00      0.99     12216\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision = precision_score(y_test, y_pred4, average='weighted')\n",
        "recall = recall_score(y_test, y_pred4, average='weighted')\n",
        "f1score = f1_score(y_test, y_pred4, average='weighted')\n",
        "print(f\"Precision = {precision:.4f}\")\n",
        "print(f\"Recall = {recall:.4f}\")\n",
        "print(f\"F1 Score = {f1score:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHu5dwVNAh1J",
        "outputId": "9c1c73bd-24e6-4c04-f7e6-ba4216ddbe11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision = 0.9918\n",
            "Recall = 0.9959\n",
            "F1 Score = 0.9939\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import HistGradientBoostingClassifier"
      ],
      "metadata": {
        "id": "hDfs652Ljf2X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "HG=HistGradientBoostingClassifier()\n",
        "HG"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "w6yNICS_j9kA",
        "outputId": "e270f9c4-8933-443e-d5d0-454c7fedb756"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HistGradientBoostingClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-14 {color: black;background-color: white;}#sk-container-id-14 pre{padding: 0;}#sk-container-id-14 div.sk-toggleable {background-color: white;}#sk-container-id-14 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-14 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-14 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-14 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-14 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-14 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-14 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-14 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-14 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-14 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-14 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-14 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-14 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-14 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-14 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-14 div.sk-item {position: relative;z-index: 1;}#sk-container-id-14 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-14 div.sk-item::before, #sk-container-id-14 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-14 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-14 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-14 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-14 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-14 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-14 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-14 div.sk-label-container {text-align: center;}#sk-container-id-14 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-14 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-14\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>HistGradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" checked><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HistGradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HG.fit(X_train,y_train)\n",
        "HG"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "cB9bwlb6kD0Z",
        "outputId": "154ba8cd-6a01-4c1a-bc22-af3b9cec8aa0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HistGradientBoostingClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-15 {color: black;background-color: white;}#sk-container-id-15 pre{padding: 0;}#sk-container-id-15 div.sk-toggleable {background-color: white;}#sk-container-id-15 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-15 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-15 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-15 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-15 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-15 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-15 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-15 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-15 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-15 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-15 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-15 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-15 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-15 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-15 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-15 div.sk-item {position: relative;z-index: 1;}#sk-container-id-15 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-15 div.sk-item::before, #sk-container-id-15 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-15 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-15 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-15 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-15 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-15 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-15 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-15 div.sk-label-container {text-align: center;}#sk-container-id-15 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-15 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-15\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>HistGradientBoostingClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" checked><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">HistGradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>HistGradientBoostingClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred5=HG.predict(X_test)\n",
        "y_pred5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXgB0xd_klZR",
        "outputId": "b7fe8792-e9a4-4f3a-f636-0b138e4ee498"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score,classification_report"
      ],
      "metadata": {
        "id": "otDjmazKkuvN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CM5=confusion_matrix(y_test,y_pred5)\n",
        "CM5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ejgzKrNlFtm",
        "outputId": "f0006c4a-a004-4014-df95-50824050996d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12150,    16],\n",
              "       [   24,    26]])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Accuracy=accuracy_score(y_test,y_pred5)\n",
        "Accuracy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6aqtTs6alMOG",
        "outputId": "36ecd179-76f7-4d22-8165-13ab09b00652"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9967256057629339"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "CR5=classification_report(y_test,y_pred5)\n",
        "CR5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "pS8jMMrDlW1f",
        "outputId": "c90566fb-3109-4bd0-a187-c528c78cca1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'              precision    recall  f1-score   support\\n\\n         0.0       1.00      1.00      1.00     12166\\n         1.0       0.62      0.52      0.57        50\\n\\n    accuracy                           1.00     12216\\n   macro avg       0.81      0.76      0.78     12216\\nweighted avg       1.00      1.00      1.00     12216\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "precision = precision_score(y_test, y_pred, average='weighted')\n",
        "recall = recall_score(y_test, y_pred, average='weighted')\n",
        "f1score = f1_score(y_test, y_pred, average='weighted')\n",
        "print(f\"Precision = {precision:.4f}\")\n",
        "print(f\"Recall = {recall:.4f}\")\n",
        "print(f\"F1 Score = {f1score:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iEND_pdeAnvZ",
        "outputId": "ee67931d-1c3b-41e5-de17-7f1d3923b5d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision = 0.9981\n",
            "Recall = 0.9982\n",
            "F1 Score = 0.9980\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Accuracy of Random Forest Classifier:\",0.9981990831696136)\n",
        "print(\"Accuracy of SUPPORT VECTOR MACHINE:\",0.9959070072036673)\n",
        "print(\"Accuracy of LOGISTIC REGRESSION:\",0.9959070072036673)\n",
        "print(\"Accuracy of NAIVE BAYES:\",0.9981990831696136)\n",
        "print(\"Accuracy of Stochastic Gradient Descent:\",0.9959070072036673)\n",
        "print(\"Accuracy of Histogram-Based Gradient Boosting:\",0.9967256057629339)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y36YJAmglWyy",
        "outputId": "40cbaac3-ef4b-4213-b26e-afa25bf2c488"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Random Forest Classifier: 0.9981990831696136\n",
            "Accuracy of SUPPORT VECTOR MACHINE: 0.9959070072036673\n",
            "Accuracy of LOGISTIC REGRESSION: 0.9959070072036673\n",
            "Accuracy of NAIVE BAYES: 0.9981990831696136\n",
            "Accuracy of Stochastic Gradient Descent: 0.9959070072036673\n",
            "Accuracy of Histogram-Based Gradient Boosting: 0.9967256057629339\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "models = ['RANDOM FOREST CLASSIFIER','SUPPORT VECTOR MACHINE','LOGISTIC REGRESSION','NAIVE BAYES','Stochastic Gradient Descent','Histogram-Based Gradient Boosting']\n",
        "accuracy_scores = [0.9981990831696136,0.9959070072036673,0.9959070072036673,0.9981990831696136,0.9959070072036673,0.9967256057629339]\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(models, accuracy_scores, marker='o', linestyle='-')\n",
        "plt.xlabel('Models')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Accuracy Comparison of Models')\n",
        "plt.ylim(0.995, 0.999)\n",
        "plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "dpyJoNS4hHWi",
        "outputId": "8b414c63-ba69-4c7f-820c-0f0df42accb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XB4oxCfiPmSN"
      },
      "outputs": [],
      "source": [
        "X_new=X.sample(1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "Z9zxtb6LQJvi",
        "outputId": "e2b1a4b7-4a4f-465c-e671-3f218ed98a31"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             cc_num    amt      zip      lat     long  city_pop     unix_time  \\\n",
              "49977  3.044240e+13  93.94  18433.0  41.5744 -75.5881    6508.0  1.373239e+09   \n",
              "\n",
              "       merch_lat  merch_long  \n",
              "49977  41.327085  -75.787648  "
            ],
            "text/html": [
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cc_num</th>\n",
              "      <th>amt</th>\n",
              "      <th>zip</th>\n",
              "      <th>lat</th>\n",
              "      <th>long</th>\n",
              "      <th>city_pop</th>\n",
              "      <th>unix_time</th>\n",
              "      <th>merch_lat</th>\n",
              "      <th>merch_long</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>49977</th>\n",
              "      <td>3.044240e+13</td>\n",
              "      <td>93.94</td>\n",
              "      <td>18433.0</td>\n",
              "      <td>41.5744</td>\n",
              "      <td>-75.5881</td>\n",
              "      <td>6508.0</td>\n",
              "      <td>1.373239e+09</td>\n",
              "      <td>41.327085</td>\n",
              "      <td>-75.787648</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4d210c47-3137-4f4d-816d-c04e0550cc61')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      cursor: pointer;\n",
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              "\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4d210c47-3137-4f4d-816d-c04e0550cc61 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-4d210c47-3137-4f4d-816d-c04e0550cc61');\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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              "        display: none;\n",
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              "        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",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('X_new')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
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            }
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "X_new"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xIfKkXWGQOBh"
      },
      "outputs": [],
      "source": [
        "y_new_pred=RF.predict(X_new)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RjZAPvRVQcUP",
        "outputId": "6be19bf3-f683-4adc-b8e5-caeb88ad0f10"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.])"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ],
      "source": [
        "y_new_pred"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new1=X.sample(1)"
      ],
      "metadata": {
        "id": "SiviAzWJWFme"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "id": "2m7idlDyWPxb",
        "outputId": "eb2d2bc9-571e-4a8e-9b3a-c4dfd7e23b0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             cc_num   amt      zip      lat      long  city_pop     unix_time  \\\n",
              "27574  2.719500e+15  9.29  57717.0  44.6723 -103.8396    8007.0  1.372591e+09   \n",
              "\n",
              "       merch_lat  merch_long  \n",
              "27574  43.924906 -103.934081  "
            ],
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              "type": "dataframe",
              "variable_name": "X_new1",
              "summary": "{\n  \"name\": \"X_new1\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"cc_num\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 2719500000000000.0,\n        \"max\": 2719500000000000.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2719500000000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"amt\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 9.29,\n        \"max\": 9.29,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          9.29\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"zip\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 57717.0,\n        \"max\": 57717.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          57717.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 44.6723,\n        \"max\": 44.6723,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          44.6723\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -103.8396,\n        \"max\": -103.8396,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -103.8396\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"city_pop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 8007.0,\n        \"max\": 8007.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          8007.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unix_time\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 1372591011.0,\n        \"max\": 1372591011.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1372591011.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_lat\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 43.924906,\n        \"max\": 43.924906,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          43.924906\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"merch_long\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": -103.934081,\n        \"max\": -103.934081,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          -103.934081\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_new_pred1=RF.predict(X_new1)"
      ],
      "metadata": {
        "id": "LN_NxKqTWXC-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_new_pred1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlLcarKcWbQF",
        "outputId": "81f33ee1-5b6e-4cae-8626-97841436ec14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.])"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uLSD_dEwQfLv",
        "outputId": "a4235605-5834-43e0-e480-ee0421d7f4a1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The given transaction is Legitimate\n"
          ]
        }
      ],
      "source": [
        "if y_new_pred==0:\n",
        "  print(\"The given transaction is Legitimate\")\n",
        "else:\n",
        "  print(\"The transactiion is Fraud\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}