Medium_CKD_SHAP.ipynb

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        "# **KIDNEY DISEASE DATASET**\n",
        "- binary classification problem - predicting chronic kidney disease\n",
        "- data from 400 patients\n",
        "- 24 features + 1 target column (0 = CKD absent, 1 = CKD present) + 1 id column"
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        "# **Variables description:**\n",
        "\n",
        "- Age: The age of the patient.\n",
        "- Blood Pressure: The blood pressure of the patient, in mm/Hg\n",
        "- Specific Gravity: The specific gravity of urine, an indicator of kidney function.\n",
        "- Albumin: The albumin level in urine, which can be an indicator of kidney damage.\n",
        "- Sugar: The sugar level in urine.\n",
        "- Red Blood Cells: The presence of red blood cells in urine, which can indicate kidney problems.\n",
        "- Pus Cells: The presence of pus cells (white blood cells) in urine.\n",
        "- Pus Cell Clumps: The presence of clumps of pus cells in urine.\n",
        "- Bacteria: The presence of bacteria in urine.\n",
        "- Blood Glucose Random: Random blood glucose levels, in mgs/dl\n",
        "- Blood Urea: Blood urea levels, an indicator of kidney and liver function, in mgs/dl\n",
        "- Serum Creatinine: Serum creatinine levels, an indicator of kidney function, in mgs/dl\n",
        "- Sodium: Sodium levels in blood, in mEq/L\n",
        "- Potassium: Potassium levels in blood, in mEq/L\n",
        "- Hemoglobin: Hemoglobin levels in blood, in gms\n",
        "- Packed Cell Volume: The volume percentage of red blood cells in blood.\n",
        "- White Blood Cell Count: The count of white blood cells in blood, in cells/cumm\n",
        "- Red Blood Cell Count: The count of red blood cells in blood, in millions/cumm\n",
        "- Hypertension: Whether the patient has hypertension (yes/no).\n",
        "- Diabetes: Whether the patient has diabetes (yes/no).\n",
        "- Coronary Artery Disease: Whether the patient has coronary artery disease (yes/no).\n",
        "- Appetite: The patient's appetite level (poor/good).\n",
        "- Pedal Edema: The presence of pedal edema (swelling of feet and ankles).\n",
        "- Anemia: Whether the patient has anemia (yes/no).\n",
        "- Class: The target variable indicating whether the patient has chronic kidney disease (ckd) or not (notckd).\n"
      ]
    },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
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      "source": [
        "# IMPORT DATASET\n",
        "\n",
        "ckd = pd.read_csv(\"kidney_disease.csv\")"
      ]
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      "source": [
        "ckd.head()"
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              "   id   age    bp     sg   al   su     rbc        pc         pcc          ba  \\\n",
              "0   0  48.0  80.0  1.020  1.0  0.0     NaN    normal  notpresent  notpresent   \n",
              "1   1   7.0  50.0  1.020  4.0  0.0     NaN    normal  notpresent  notpresent   \n",
              "2   2  62.0  80.0  1.010  2.0  3.0  normal    normal  notpresent  notpresent   \n",
              "3   3  48.0  70.0  1.005  4.0  0.0  normal  abnormal     present  notpresent   \n",
              "4   4  51.0  80.0  1.010  2.0  0.0  normal    normal  notpresent  notpresent   \n",
              "\n",
              "   ...   pcv      wc   rc  htn   dm  cad  appet   pe  ane classification  \n",
              "0  ...  44.0  7800.0  5.2  yes  yes   no   good   no   no            ckd  \n",
              "1  ...  38.0  6000.0  NaN   no   no   no   good   no   no            ckd  \n",
              "2  ...  31.0  7500.0  NaN   no  yes   no   poor   no  yes            ckd  \n",
              "3  ...  32.0  6700.0  3.9  yes   no   no   poor  yes  yes            ckd  \n",
              "4  ...  35.0  7300.0  4.6   no   no   no   good   no   no            ckd  \n",
              "\n",
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              "(400, 26)"
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              "id                  int64\n",
              "age               float64\n",
              "bp                float64\n",
              "sg                float64\n",
              "al                float64\n",
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              "dm                 object\n",
              "cad                object\n",
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              "               id         age          bp          sg          al          su  \\\n",
              "count  400.000000  391.000000  388.000000  353.000000  354.000000  351.000000   \n",
              "mean   199.500000   51.483376   76.469072    1.017408    1.016949    0.450142   \n",
              "std    115.614301   17.169714   13.683637    0.005717    1.352679    1.099191   \n",
              "min      0.000000    2.000000   50.000000    1.005000    0.000000    0.000000   \n",
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              "max    399.000000   90.000000  180.000000    1.025000    5.000000    5.000000   \n",
              "\n",
              "              bgr          bu          sc         sod         pot        hemo  \\\n",
              "count  356.000000  381.000000  383.000000  313.000000  312.000000  348.000000   \n",
              "mean   148.036517   57.425722    3.072454  137.528754    4.627244   12.526437   \n",
              "std     79.281714   50.503006    5.741126   10.408752    3.193904    2.912587   \n",
              "min     22.000000    1.500000    0.400000    4.500000    2.500000    3.100000   \n",
              "25%     99.000000   27.000000    0.900000  135.000000    3.800000   10.300000   \n",
              "50%    121.000000   42.000000    1.300000  138.000000    4.400000   12.650000   \n",
              "75%    163.000000   66.000000    2.800000  142.000000    4.900000   15.000000   \n",
              "max    490.000000  391.000000   76.000000  163.000000   47.000000   17.800000   \n",
              "\n",
              "              pcv            wc          rc  \n",
              "count  329.000000    294.000000  269.000000  \n",
              "mean    38.884498   8406.122449    4.707435  \n",
              "std      8.990105   2944.474190    1.025323  \n",
              "min      9.000000   2200.000000    2.100000  \n",
              "25%     32.000000   6500.000000    3.900000  \n",
              "50%     40.000000   8000.000000    4.800000  \n",
              "75%     45.000000   9800.000000    5.400000  \n",
              "max     54.000000  26400.000000    8.000000  "
            ],
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>bgr</th>\n",
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              "      <th>rc</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>400.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>388.000000</td>\n",
              "      <td>353.000000</td>\n",
              "      <td>354.000000</td>\n",
              "      <td>351.000000</td>\n",
              "      <td>356.000000</td>\n",
              "      <td>381.000000</td>\n",
              "      <td>383.000000</td>\n",
              "      <td>313.000000</td>\n",
              "      <td>312.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>329.000000</td>\n",
              "      <td>294.000000</td>\n",
              "      <td>269.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>199.500000</td>\n",
              "      <td>51.483376</td>\n",
              "      <td>76.469072</td>\n",
              "      <td>1.017408</td>\n",
              "      <td>1.016949</td>\n",
              "      <td>0.450142</td>\n",
              "      <td>148.036517</td>\n",
              "      <td>57.425722</td>\n",
              "      <td>3.072454</td>\n",
              "      <td>137.528754</td>\n",
              "      <td>4.627244</td>\n",
              "      <td>12.526437</td>\n",
              "      <td>38.884498</td>\n",
              "      <td>8406.122449</td>\n",
              "      <td>4.707435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>115.614301</td>\n",
              "      <td>17.169714</td>\n",
              "      <td>13.683637</td>\n",
              "      <td>0.005717</td>\n",
              "      <td>1.352679</td>\n",
              "      <td>1.099191</td>\n",
              "      <td>79.281714</td>\n",
              "      <td>50.503006</td>\n",
              "      <td>5.741126</td>\n",
              "      <td>10.408752</td>\n",
              "      <td>3.193904</td>\n",
              "      <td>2.912587</td>\n",
              "      <td>8.990105</td>\n",
              "      <td>2944.474190</td>\n",
              "      <td>1.025323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>1.005000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>2.500000</td>\n",
              "      <td>3.100000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>2200.000000</td>\n",
              "      <td>2.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>99.750000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>1.010000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>135.000000</td>\n",
              "      <td>3.800000</td>\n",
              "      <td>10.300000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>6500.000000</td>\n",
              "      <td>3.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>199.500000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>121.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>1.300000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>12.650000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>8000.000000</td>\n",
              "      <td>4.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>299.250000</td>\n",
              "      <td>64.500000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>1.020000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>4.900000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>9800.000000</td>\n",
              "      <td>5.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>399.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>1.025000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>490.000000</td>\n",
              "      <td>391.000000</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>163.000000</td>\n",
              "      <td>47.000000</td>\n",
              "      <td>17.800000</td>\n",
              "      <td>54.000000</td>\n",
              "      <td>26400.000000</td>\n",
              "      <td>8.000000</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check for missing data\n",
        "\n",
        "ckd.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNtcOJsRwjVT",
        "outputId": "04bf34c7-6eac-47b7-b28f-6cd69e36b528"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                  0\n",
              "age                 9\n",
              "bp                 12\n",
              "sg                 47\n",
              "al                 46\n",
              "su                 49\n",
              "rbc               152\n",
              "pc                 65\n",
              "pcc                 4\n",
              "ba                  4\n",
              "bgr                44\n",
              "bu                 19\n",
              "sc                 17\n",
              "sod                87\n",
              "pot                88\n",
              "hemo               52\n",
              "pcv                71\n",
              "wc                106\n",
              "rc                131\n",
              "htn                 2\n",
              "dm                  2\n",
              "cad                 2\n",
              "appet               1\n",
              "pe                  1\n",
              "ane                 1\n",
              "classification      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize how much data is missing\n",
        "\n",
        "sns.heatmap(ckd.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "7CA66WwGZawr",
        "outputId": "995ada87-23d6-4b73-ceab-635c6dc4ff0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,10))\n",
        "sns.heatmap(ckd.corr(), annot= True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 908
        },
        "id": "-ZeISvqag4Mv",
        "outputId": "c347dce0-56df-4493-d99e-1e71e21be95b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-5f35b89f67fe>:5: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(ckd.corr(), annot= True)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# replacing nan in continuous columns with their respective medians\n",
        "\n",
        "# List of columns with NaN values to replace\n",
        "columns_with_nans = ['age', 'bp', 'sg', 'al', 'su', 'bgr', 'bu', 'sc','sod', 'pot', 'hemo', 'pcv', 'wc', 'rc']\n",
        "\n",
        "# Calculate the median for each column and fill NaN values with the mean\n",
        "for column in columns_with_nans:\n",
        "    median_value = ckd[column].median()\n",
        "    ckd[column].fillna(median_value, inplace=True)\n"
      ],
      "metadata": {
        "id": "8R4GndlI0k4W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# replacing nan with 'unknown' in rbc and pc columns\n",
        "\n",
        "ckd['rbc'].fillna('unknown', inplace=True)\n",
        "\n",
        "ckd['pc'].fillna('unknown', inplace=True)"
      ],
      "metadata": {
        "id": "nvPJlwEh2QyF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# replacing nan in rest of the categorical columns with mode\n",
        "\n",
        "# List of columns with NaN values to replace\n",
        "columns_with_nans = ['pcc', 'ba', 'htn', 'dm', 'cad', 'appet', 'pe', 'ane']\n",
        "\n",
        "# Calculate the mode for each column and fill NaN values with the mode\n",
        "for column in columns_with_nans:\n",
        "    mode_value = ckd[column].mode()[0]  # Using [0] to get the first mode if multiple modes exist\n",
        "    ckd[column].fillna(mode_value, inplace=True)\n"
      ],
      "metadata": {
        "id": "_XtJYTgT3kJM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# classification column has issues - fixing\n",
        "\n",
        "ckd['classification'] = np.where(ckd['classification'] == 'notckd', 'notckd', 'ckd')"
      ],
      "metadata": {
        "id": "WSITl9CN5zjE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ckd.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r4jkLnzD1FUB",
        "outputId": "06ea9272-55fd-42ed-a660-e487bcd02331"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                0\n",
              "age               0\n",
              "bp                0\n",
              "sg                0\n",
              "al                0\n",
              "su                0\n",
              "rbc               0\n",
              "pc                0\n",
              "pcc               0\n",
              "ba                0\n",
              "bgr               0\n",
              "bu                0\n",
              "sc                0\n",
              "sod               0\n",
              "pot               0\n",
              "hemo              0\n",
              "pcv               0\n",
              "wc                0\n",
              "rc                0\n",
              "htn               0\n",
              "dm                0\n",
              "cad               0\n",
              "appet             0\n",
              "pe                0\n",
              "ane               0\n",
              "classification    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Model- running simple random forest classification**"
      ],
      "metadata": {
        "id": "YK1CX1LYZuIw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# dummy creation\n",
        "\n",
        "df = pd.get_dummies(ckd, columns=['rbc', 'pc', 'pcc', 'ba', 'htn', 'dm', 'cad', 'appet', 'pe', 'ane'], drop_first=True)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "yyjfTCQ0Ckby",
        "outputId": "2146bf19-8d06-403b-8408-cdc4fc90a0e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id   age    bp     sg   al   su    bgr    bu   sc    sod  ...  pc_normal  \\\n",
              "0   0  48.0  80.0  1.020  1.0  0.0  121.0  36.0  1.2  138.0  ...          1   \n",
              "1   1   7.0  50.0  1.020  4.0  0.0  121.0  18.0  0.8  138.0  ...          1   \n",
              "2   2  62.0  80.0  1.010  2.0  3.0  423.0  53.0  1.8  138.0  ...          1   \n",
              "3   3  48.0  70.0  1.005  4.0  0.0  117.0  56.0  3.8  111.0  ...          0   \n",
              "4   4  51.0  80.0  1.010  2.0  0.0  106.0  26.0  1.4  138.0  ...          1   \n",
              "\n",
              "   pc_unknown  pcc_present  ba_present  htn_yes dm_yes  cad_yes  appet_poor  \\\n",
              "0           0            0           0        1      1        0           0   \n",
              "1           0            0           0        0      0        0           0   \n",
              "2           0            0           0        0      1        0           1   \n",
              "3           0            1           0        1      0        0           1   \n",
              "4           0            0           0        0      0        0           0   \n",
              "\n",
              "   pe_yes  ane_yes  \n",
              "0       0        0  \n",
              "1       0        0  \n",
              "2       0        1  \n",
              "3       1        1  \n",
              "4       0        0  \n",
              "\n",
              "[5 rows x 28 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-061b5fe5-6a9d-4fc4-bbfb-0fdf1d9700bb\">\n",
              "    <div 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>id</th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>bgr</th>\n",
              "      <th>bu</th>\n",
              "      <th>sc</th>\n",
              "      <th>sod</th>\n",
              "      <th>...</th>\n",
              "      <th>pc_normal</th>\n",
              "      <th>pc_unknown</th>\n",
              "      <th>pcc_present</th>\n",
              "      <th>ba_present</th>\n",
              "      <th>htn_yes</th>\n",
              "      <th>dm_yes</th>\n",
              "      <th>cad_yes</th>\n",
              "      <th>appet_poor</th>\n",
              "      <th>pe_yes</th>\n",
              "      <th>ane_yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>138.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>138.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>423.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>138.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>111.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>138.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 28 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-061b5fe5-6a9d-4fc4-bbfb-0fdf1d9700bb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "\n",
              "\n",
              "    <div id=\"df-f371aeac-21a0-43b7-bb3a-b229b135c14d\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f371aeac-21a0-43b7-bb3a-b229b135c14d')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
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              "      </button>\n",
              "    </div>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\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",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f371aeac-21a0-43b7-bb3a-b229b135c14d button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-061b5fe5-6a9d-4fc4-bbfb-0fdf1d9700bb 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-061b5fe5-6a9d-4fc4-bbfb-0fdf1d9700bb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# convert target column to 1/0\n",
        "\n",
        "df['classification'] = np.where(df['classification'] == 'ckd', 1, 0)\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "ZLIkMafBEPBC",
        "outputId": "1b760a1b-6370-4f59-cca4-f0ff2d172060"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id   age    bp     sg   al   su    bgr    bu   sc    sod  ...  pc_normal  \\\n",
              "0   0  48.0  80.0  1.020  1.0  0.0  121.0  36.0  1.2  138.0  ...          1   \n",
              "1   1   7.0  50.0  1.020  4.0  0.0  121.0  18.0  0.8  138.0  ...          1   \n",
              "2   2  62.0  80.0  1.010  2.0  3.0  423.0  53.0  1.8  138.0  ...          1   \n",
              "3   3  48.0  70.0  1.005  4.0  0.0  117.0  56.0  3.8  111.0  ...          0   \n",
              "4   4  51.0  80.0  1.010  2.0  0.0  106.0  26.0  1.4  138.0  ...          1   \n",
              "\n",
              "   pc_unknown  pcc_present  ba_present  htn_yes  dm_yes  cad_yes  appet_poor  \\\n",
              "0           0            0           0        1       1        0           0   \n",
              "1           0            0           0        0       0        0           0   \n",
              "2           0            0           0        0       1        0           1   \n",
              "3           0            1           0        1       0        0           1   \n",
              "4           0            0           0        0       0        0           0   \n",
              "\n",
              "   pe_yes  ane_yes  \n",
              "0       0        0  \n",
              "1       0        0  \n",
              "2       0        1  \n",
              "3       1        1  \n",
              "4       0        0  \n",
              "\n",
              "[5 rows x 28 columns]"
            ],
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              "      <th>0</th>\n",
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              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
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              "      <td>36.0</td>\n",
              "      <td>1.2</td>\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-39bef445-4254-47a8-9361-5147d0889552 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-28207aaa-f143-4c62-b5ca-33d519cff4ba 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-28207aaa-f143-4c62-b5ca-33d519cff4ba');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7xVVl6m9zyd"
      },
      "source": [
        "X= df.drop(['classification', 'id'], axis= 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IJYIiUGx-vQh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "outputId": "7eb48d84-9daa-4782-bbca-08c027e4b3f2"
      },
      "source": [
        "X.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age    bp     sg   al   su    bgr    bu   sc    sod  pot  ...  pc_normal  \\\n",
              "0  48.0  80.0  1.020  1.0  0.0  121.0  36.0  1.2  138.0  4.4  ...          1   \n",
              "1   7.0  50.0  1.020  4.0  0.0  121.0  18.0  0.8  138.0  4.4  ...          1   \n",
              "2  62.0  80.0  1.010  2.0  3.0  423.0  53.0  1.8  138.0  4.4  ...          1   \n",
              "3  48.0  70.0  1.005  4.0  0.0  117.0  56.0  3.8  111.0  2.5  ...          0   \n",
              "4  51.0  80.0  1.010  2.0  0.0  106.0  26.0  1.4  138.0  4.4  ...          1   \n",
              "\n",
              "   pc_unknown  pcc_present  ba_present  htn_yes  dm_yes  cad_yes  appet_poor  \\\n",
              "0           0            0           0        1       1        0           0   \n",
              "1           0            0           0        0       0        0           0   \n",
              "2           0            0           0        0       1        0           1   \n",
              "3           0            1           0        1       0        0           1   \n",
              "4           0            0           0        0       0        0           0   \n",
              "\n",
              "   pe_yes  ane_yes  \n",
              "0       0        0  \n",
              "1       0        0  \n",
              "2       0        1  \n",
              "3       1        1  \n",
              "4       0        0  \n",
              "\n",
              "[5 rows x 26 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-203518fd-94a5-4d05-b3ad-a4e5674844e7\">\n",
              "    <div 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>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
              "      <th>bgr</th>\n",
              "      <th>bu</th>\n",
              "      <th>sc</th>\n",
              "      <th>sod</th>\n",
              "      <th>pot</th>\n",
              "      <th>...</th>\n",
              "      <th>pc_normal</th>\n",
              "      <th>pc_unknown</th>\n",
              "      <th>pcc_present</th>\n",
              "      <th>ba_present</th>\n",
              "      <th>htn_yes</th>\n",
              "      <th>dm_yes</th>\n",
              "      <th>cad_yes</th>\n",
              "      <th>appet_poor</th>\n",
              "      <th>pe_yes</th>\n",
              "      <th>ane_yes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>138.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>121.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>138.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>423.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>138.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>117.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>111.0</td>\n",
              "      <td>2.5</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>106.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>138.0</td>\n",
              "      <td>4.4</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-203518fd-94a5-4d05-b3ad-a4e5674844e7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-610de733-c683-49bd-bd84-9f4e3c889441\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-610de733-c683-49bd-bd84-9f4e3c889441')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-610de733-c683-49bd-bd84-9f4e3c889441 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-203518fd-94a5-4d05-b3ad-a4e5674844e7 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-203518fd-94a5-4d05-b3ad-a4e5674844e7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "guywcd1f-zNH"
      },
      "source": [
        "# making classfication the y var (output)\n",
        "\n",
        "y= df['classification']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxRpju0F-5YN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0fded7e5-671e-4a87-a257-edbe9e09fe05"
      },
      "source": [
        "y.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: classification, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TaxMZV7X-6lN"
      },
      "source": [
        "# splitting into train and test-70/30\n",
        "\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "951xPdrl-_wL"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.3, random_state=10)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b_X8Gxu4_Ag3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d16a30e-cb69-4c3b-c7ba-d989b57a1b72"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(280, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wZ_7DJkR_ApZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "696971f0-0407-4126-911c-33840d79f758"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(120, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "phFiLhEN_A2-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "df12a213-9794-48ac-be7e-2e7b79aa56e2"
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(280,)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWvtijxF_A_K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b31d087-2a90-4713-9633-56715edf63fa"
      },
      "source": [
        "y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(120,)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5T8waIC_IOw"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# set seed\n",
        "\n",
        "np.random.seed(123)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hGU4qiRc_IUY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "85d674fc-4bbe-4bcc-943a-7f4675bbaac3"
      },
      "source": [
        "# fit train data to random forest model\n",
        "\n",
        "rf = RandomForestClassifier(n_estimators = 100)\n",
        "rf.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "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": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zzjRN1Bx_IYo"
      },
      "source": [
        "# now apply predict function to predict for X_test\n",
        "\n",
        "predictions = rf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-KqNHncn_IfE"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kdgt1hKDCySE"
      },
      "source": [
        "accuracy = confusion_matrix(y_test, predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TiBecoh2C7Qw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "122b1fcc-b02a-49e0-a63a-12eabd3f911d"
      },
      "source": [
        "accuracy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[37,  0],\n",
              "       [ 0, 83]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXUHhj5QUgwa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "b8299121-e622-4dee-a711-6c4f058a8c52"
      },
      "source": [
        "# generate confusion matrix\n",
        "\n",
        "from sklearn.metrics import confusion_matrix\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.heatmap(accuracy, annot= True)\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.show()"
      ],
      "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",
      "metadata": {
        "id": "QVLF88XXDINx"
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "55XxzYdoDIZW"
      },
      "source": [
        "accuracy = accuracy_score(y_test, predictions)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "epElRmrMDIjI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd0d998d-7418-45c0-8396-bfa67d62efa4"
      },
      "source": [
        "accuracy\n",
        "\n",
        "# model accuracy is 100% on test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **SHAP**"
      ],
      "metadata": {
        "id": "YXm4a-U0dXCG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install shap\n",
        "\n",
        "import shap\n",
        "import random\n",
        "\n",
        "# set seed\n",
        "\n",
        "random.seed(123)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "srOYxW5GdWdS",
        "outputId": "91a705b2-6483-4a4b-9d10-f270e1639444"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fits the explainer\n",
        "\n",
        "explainer = shap.Explainer(rf.predict, X_test)\n",
        "\n",
        "shap_values = explainer(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6XDQi_DdCwB",
        "outputId": "ec70d13b-2ada-4fd4-a5bc-c77a5760281d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Permutation explainer: 121it [00:35,  2.35it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Global effect of features on prediction\n",
        "\n",
        "shap.plots.bar(shap_values, max_display = 10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 585
        },
        "id": "QSN4YqvOdzjn",
        "outputId": "1e1e2c77-06db-4978-c0cc-e3f5a2812c91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x650 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# BEESWARM PLOT\n",
        "\n",
        "shap.plots.beeswarm(shap_values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "id": "VthTAzHXfaP2",
        "outputId": "7fda68f0-6082-403d-dff4-cc11fce78165"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}