Risk_Modeling_MAJM.ipynb

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    {
      "cell_type": "code",
      "source": [
        "!pip install shap"
      ],
      "metadata": {
        "id": "Mzl90TCGnLMf",
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          "base_uri": "https://localhost:8080/"
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        "outputId": "1126740d-3e85-4e6e-ae31-391858ffde75"
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      "execution_count": null,
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting shap\n",
            "  Downloading shap-0.41.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl (575 kB)\n",
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            "Installing collected packages: slicer, shap\n",
            "Successfully installed shap-0.41.0 slicer-0.0.7\n"
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    {
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      "execution_count": null,
      "metadata": {
        "id": "xUzDzYkhtLhV"
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      "outputs": [],
      "source": [
        "#Import packages\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from google.colab import files\n",
        "import io\n",
        "from google.colab import drive\n",
        "\n",
        "#Metrics/processing imports\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import metrics\n",
        "import imblearn\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.metrics import confusion_matrix, classification_report, plot_confusion_matrix\n",
        "from collections import Counter\n",
        "\n",
        "#Model imports\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.ensemble import VotingClassifier\n",
        "from sklearn import svm\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "#Neural net imports\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.utils import to_categorical\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import models\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten\n",
        "import tensorflow.keras.backend as k\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "\n",
        "#Visualization imports\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import missingno as msno\n",
        "\n",
        "#Shap values\n",
        "import shap\n",
        "from scipy.special import softmax\n",
        "\n",
        "#Pickle\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Set seed\n",
        "np.random.seed(1234)"
      ],
      "metadata": {
        "id": "wUCk8DbvqyaM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Load data\n",
        "drive.mount('/content/drive')\n",
        "df =pd.read_csv(\"drive/My Drive/Capstone Project/Cervical_Cancer/kag_risk_factors_cervical_cancer.csv\")\n",
        "df.head()"
      ],
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          "height": 386
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      "execution_count": null,
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
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              "   Age Number of sexual partners First sexual intercourse Num of pregnancies  \\\n",
              "0   18                       4.0                     15.0                1.0   \n",
              "1   15                       1.0                     14.0                1.0   \n",
              "2   34                       1.0                        ?                1.0   \n",
              "3   52                       5.0                     16.0                4.0   \n",
              "4   46                       3.0                     21.0                4.0   \n",
              "\n",
              "  Smokes Smokes (years) Smokes (packs/year) Hormonal Contraceptives  \\\n",
              "0    0.0            0.0                 0.0                     0.0   \n",
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              "\n",
              "  Hormonal Contraceptives (years)  IUD  ... STDs: Time since first diagnosis  \\\n",
              "0                             0.0  0.0  ...                                ?   \n",
              "1                             0.0  0.0  ...                                ?   \n",
              "2                             0.0  0.0  ...                                ?   \n",
              "3                             3.0  0.0  ...                                ?   \n",
              "4                            15.0  0.0  ...                                ?   \n",
              "\n",
              "  STDs: Time since last diagnosis Dx:Cancer Dx:CIN Dx:HPV Dx Hinselmann  \\\n",
              "0                               ?         0      0      0  0          0   \n",
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              "              Age  STDs: Number of diagnosis   Dx:Cancer      Dx:CIN  \\\n",
              "count  858.000000                 858.000000  858.000000  858.000000   \n",
              "mean    26.820513                   0.087413    0.020979    0.010490   \n",
              "std      8.497948                   0.302545    0.143398    0.101939   \n",
              "min     13.000000                   0.000000    0.000000    0.000000   \n",
              "25%     20.000000                   0.000000    0.000000    0.000000   \n",
              "50%     25.000000                   0.000000    0.000000    0.000000   \n",
              "75%     32.000000                   0.000000    0.000000    0.000000   \n",
              "max     84.000000                   3.000000    1.000000    1.000000   \n",
              "\n",
              "           Dx:HPV          Dx  Hinselmann    Schiller    Citology      Biopsy  \n",
              "count  858.000000  858.000000  858.000000  858.000000  858.000000  858.000000  \n",
              "mean     0.020979    0.027972    0.040793    0.086247    0.051282    0.064103  \n",
              "std      0.143398    0.164989    0.197925    0.280892    0.220701    0.245078  \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000  \n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>STDs: Number of diagnosis</th>\n",
              "      <th>Dx:Cancer</th>\n",
              "      <th>Dx:CIN</th>\n",
              "      <th>Dx:HPV</th>\n",
              "      <th>Dx</th>\n",
              "      <th>Hinselmann</th>\n",
              "      <th>Schiller</th>\n",
              "      <th>Citology</th>\n",
              "      <th>Biopsy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>858.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>858.000000</td>\n",
              "      <td>858.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>26.820513</td>\n",
              "      <td>0.087413</td>\n",
              "      <td>0.020979</td>\n",
              "      <td>0.010490</td>\n",
              "      <td>0.020979</td>\n",
              "      <td>0.027972</td>\n",
              "      <td>0.040793</td>\n",
              "      <td>0.086247</td>\n",
              "      <td>0.051282</td>\n",
              "      <td>0.064103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.497948</td>\n",
              "      <td>0.302545</td>\n",
              "      <td>0.143398</td>\n",
              "      <td>0.101939</td>\n",
              "      <td>0.143398</td>\n",
              "      <td>0.164989</td>\n",
              "      <td>0.197925</td>\n",
              "      <td>0.280892</td>\n",
              "      <td>0.220701</td>\n",
              "      <td>0.245078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>13.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>25.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>84.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's inspect the data\n",
        "print(df.columns)\n",
        "\n",
        "#Let's rename the columns so they are usable \n",
        "df.rename({\n",
        "           'Age': 'age', \n",
        "           'Number of sexual partners': 'num_sex_partners',\n",
        "           'First sexual intercourse': 'first_sex',\n",
        "           'Num of pregnancies': 'num_preg',\n",
        "           'Smokes': 'smoker',\n",
        "           'Smokes (years)': 'smoke_years',\n",
        "           'Smokes (packs/year)': 'pack_years',\n",
        "           'Hormonal Contraceptives': 'contracept', \n",
        "           'Hormonal Contraceptives (years)': 'contracept_years',\n",
        "           'IUD': 'iud',\n",
        "           'IUD (years)': 'iud_years',\n",
        "           'STDs': 'stds',\n",
        "           'STDs (number)': 'stds_num',\n",
        "           'STDs:condylomatosis': 'std_condy', \n",
        "           'STDs:cervical condylomatosis': 'std_cerv_condy',\n",
        "           'STDs:vaginal condylomatosis': 'std_vag_condy',\n",
        "           'STDs:vulvo-perineal condylomatosis': 'std_vulvperi_condy',\n",
        "           'STDs:syphilis': 'std_syphilis',\n",
        "           'STDs:pelvic inflammatory disease': 'std_pid',\n",
        "           'STDs:genital herpes': 'std_herpes',\n",
        "           'STDs:molluscum contagiosum': 'std_molluscum', \n",
        "           'STDs:AIDS': 'std_aids',\n",
        "           'STDs:HIV': 'std_hiv',\n",
        "           'STDs:Hepatitis B': 'std_hep_b',\n",
        "           'STDs:HPV': 'std_hpv',\n",
        "           'STDs: Number of diagnosis': 'std_num_dx',\n",
        "           'STDs: Time since first diagnosis': 'std_time_first_dx',\n",
        "           'STDs: Time since last diagnosis': 'std_time_last_dx',\n",
        "           'Dx:Cancer': 'cancer', \n",
        "           'Dx:CIN': 'cin', \n",
        "           'Dx:HPV': 'hpv', \n",
        "           'Dx': 'dx', \n",
        "           'Hinselmann': 'hinselmann',\n",
        "           'Schiller': 'schiller', \n",
        "           'Citology': 'cytology',\n",
        "           'Biopsy': 'biopsy'\n",
        "           }, \n",
        "          axis=1, \n",
        "          inplace=True)\n"
      ],
      "metadata": {
        "id": "A9uTcMsxtZYd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de137c48-ebf2-488d-c206-535abfd31590"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Age', 'Number of sexual partners', 'First sexual intercourse',\n",
            "       'Num of pregnancies', 'Smokes', 'Smokes (years)', 'Smokes (packs/year)',\n",
            "       'Hormonal Contraceptives', 'Hormonal Contraceptives (years)', 'IUD',\n",
            "       'IUD (years)', 'STDs', 'STDs (number)', 'STDs:condylomatosis',\n",
            "       'STDs:cervical condylomatosis', 'STDs:vaginal condylomatosis',\n",
            "       'STDs:vulvo-perineal condylomatosis', 'STDs:syphilis',\n",
            "       'STDs:pelvic inflammatory disease', 'STDs:genital herpes',\n",
            "       'STDs:molluscum contagiosum', 'STDs:AIDS', 'STDs:HIV',\n",
            "       'STDs:Hepatitis B', 'STDs:HPV', 'STDs: Number of diagnosis',\n",
            "       'STDs: Time since first diagnosis', 'STDs: Time since last diagnosis',\n",
            "       'Dx:Cancer', 'Dx:CIN', 'Dx:HPV', 'Dx', 'Hinselmann', 'Schiller',\n",
            "       'Citology', 'Biopsy'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.columns)"
      ],
      "metadata": {
        "id": "ql8MoVbLtZbo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7a24fef0-1ab6-4092-b018-bd2535752c18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['age', 'num_sex_partners', 'first_sex', 'num_preg', 'smoker',\n",
            "       'smoke_years', 'pack_years', 'contracept', 'contracept_years', 'iud',\n",
            "       'iud_years', 'stds', 'stds_num', 'std_condy', 'std_cerv_condy',\n",
            "       'std_vag_condy', 'std_vulvperi_condy', 'std_syphilis', 'std_pid',\n",
            "       'std_herpes', 'std_molluscum', 'std_aids', 'std_hiv', 'std_hep_b',\n",
            "       'std_hpv', 'std_num_dx', 'std_time_first_dx', 'std_time_last_dx',\n",
            "       'cancer', 'cin', 'hpv', 'dx', 'hinselmann', 'schiller', 'cytology',\n",
            "       'biopsy'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape  "
      ],
      "metadata": {
        "id": "27gj4pkqtZer",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6bfb023d-5e50-47c4-cf87-4fe3e4ddccb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(858, 36)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Describing numeric values\n",
        "df.describe().T"
      ],
      "metadata": {
        "id": "IF_9R_7_tZvF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "69f6fcd5-5a9d-4c93-a8a6-b2327ba97fd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            count       mean       std   min   25%   50%   75%   max\n",
              "age         858.0  26.820513  8.497948  13.0  20.0  25.0  32.0  84.0\n",
              "std_num_dx  858.0   0.087413  0.302545   0.0   0.0   0.0   0.0   3.0\n",
              "cancer      858.0   0.020979  0.143398   0.0   0.0   0.0   0.0   1.0\n",
              "cin         858.0   0.010490  0.101939   0.0   0.0   0.0   0.0   1.0\n",
              "hpv         858.0   0.020979  0.143398   0.0   0.0   0.0   0.0   1.0\n",
              "dx          858.0   0.027972  0.164989   0.0   0.0   0.0   0.0   1.0\n",
              "hinselmann  858.0   0.040793  0.197925   0.0   0.0   0.0   0.0   1.0\n",
              "schiller    858.0   0.086247  0.280892   0.0   0.0   0.0   0.0   1.0\n",
              "cytology    858.0   0.051282  0.220701   0.0   0.0   0.0   0.0   1.0\n",
              "biopsy      858.0   0.064103  0.245078   0.0   0.0   0.0   0.0   1.0"
            ],
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              "    <tr>\n",
              "      <th>age</th>\n",
              "      <td>858.0</td>\n",
              "      <td>26.820513</td>\n",
              "      <td>8.497948</td>\n",
              "      <td>13.0</td>\n",
              "      <td>20.0</td>\n",
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              "      <td>32.0</td>\n",
              "      <td>84.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std_num_dx</th>\n",
              "      <td>858.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>cancer</th>\n",
              "      <td>858.0</td>\n",
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              "      <td>0.143398</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>cin</th>\n",
              "      <td>858.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>hpv</th>\n",
              "      <td>858.0</td>\n",
              "      <td>0.020979</td>\n",
              "      <td>0.143398</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dx</th>\n",
              "      <td>858.0</td>\n",
              "      <td>0.027972</td>\n",
              "      <td>0.164989</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hinselmann</th>\n",
              "      <td>858.0</td>\n",
              "      <td>0.040793</td>\n",
              "      <td>0.197925</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>schiller</th>\n",
              "      <td>858.0</td>\n",
              "      <td>0.086247</td>\n",
              "      <td>0.280892</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cytology</th>\n",
              "      <td>858.0</td>\n",
              "      <td>0.051282</td>\n",
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          "metadata": {},
          "execution_count": 10
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Describing categorical values\n",
        "df.describe(include='all').T"
      ],
      "metadata": {
        "id": "SAaDOcXMtZyO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "b4c8ddcc-1d58-417b-8d7b-4e6c8c3a9777"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    count unique   top freq       mean       std   min   25%  \\\n",
              "age                 858.0    NaN   NaN  NaN  26.820513  8.497948  13.0  20.0   \n",
              "num_sex_partners      858     13   2.0  272        NaN       NaN   NaN   NaN   \n",
              "first_sex             858     22  15.0  163        NaN       NaN   NaN   NaN   \n",
              "num_preg              858     12   1.0  270        NaN       NaN   NaN   NaN   \n",
              "smoker                858      3   0.0  722        NaN       NaN   NaN   NaN   \n",
              "smoke_years           858     31   0.0  722        NaN       NaN   NaN   NaN   \n",
              "pack_years            858     63   0.0  722        NaN       NaN   NaN   NaN   \n",
              "contracept            858      3   1.0  481        NaN       NaN   NaN   NaN   \n",
              "contracept_years      858     41   0.0  269        NaN       NaN   NaN   NaN   \n",
              "iud                   858      3   0.0  658        NaN       NaN   NaN   NaN   \n",
              "iud_years             858     27   0.0  658        NaN       NaN   NaN   NaN   \n",
              "stds                  858      3   0.0  674        NaN       NaN   NaN   NaN   \n",
              "stds_num              858      6   0.0  674        NaN       NaN   NaN   NaN   \n",
              "std_condy             858      3   0.0  709        NaN       NaN   NaN   NaN   \n",
              "std_cerv_condy        858      2   0.0  753        NaN       NaN   NaN   NaN   \n",
              "std_vag_condy         858      3   0.0  749        NaN       NaN   NaN   NaN   \n",
              "std_vulvperi_condy    858      3   0.0  710        NaN       NaN   NaN   NaN   \n",
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              "std_num_dx          858.0    NaN   NaN  NaN   0.087413  0.302545   0.0   0.0   \n",
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              "std_time_last_dx      858     19     ?  787        NaN       NaN   NaN   NaN   \n",
              "cancer              858.0    NaN   NaN  NaN   0.020979  0.143398   0.0   0.0   \n",
              "cin                 858.0    NaN   NaN  NaN    0.01049  0.101939   0.0   0.0   \n",
              "hpv                 858.0    NaN   NaN  NaN   0.020979  0.143398   0.0   0.0   \n",
              "dx                  858.0    NaN   NaN  NaN   0.027972  0.164989   0.0   0.0   \n",
              "hinselmann          858.0    NaN   NaN  NaN   0.040793  0.197925   0.0   0.0   \n",
              "schiller            858.0    NaN   NaN  NaN   0.086247  0.280892   0.0   0.0   \n",
              "cytology            858.0    NaN   NaN  NaN   0.051282  0.220701   0.0   0.0   \n",
              "biopsy              858.0    NaN   NaN  NaN   0.064103  0.245078   0.0   0.0   \n",
              "\n",
              "                     50%   75%   max  \n",
              "age                 25.0  32.0  84.0  \n",
              "num_sex_partners     NaN   NaN   NaN  \n",
              "first_sex            NaN   NaN   NaN  \n",
              "num_preg             NaN   NaN   NaN  \n",
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              "smoke_years          NaN   NaN   NaN  \n",
              "pack_years           NaN   NaN   NaN  \n",
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              "iud                  NaN   NaN   NaN  \n",
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              "stds                 NaN   NaN   NaN  \n",
              "stds_num             NaN   NaN   NaN  \n",
              "std_condy            NaN   NaN   NaN  \n",
              "std_cerv_condy       NaN   NaN   NaN  \n",
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              "std_vulvperi_condy   NaN   NaN   NaN  \n",
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              "std_herpes           NaN   NaN   NaN  \n",
              "std_molluscum        NaN   NaN   NaN  \n",
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              "std_hiv              NaN   NaN   NaN  \n",
              "std_hep_b            NaN   NaN   NaN  \n",
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              "std_num_dx           0.0   0.0   3.0  \n",
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              "std_time_last_dx     NaN   NaN   NaN  \n",
              "cancer               0.0   0.0   1.0  \n",
              "cin                  0.0   0.0   1.0  \n",
              "hpv                  0.0   0.0   1.0  \n",
              "dx                   0.0   0.0   1.0  \n",
              "hinselmann           0.0   0.0   1.0  \n",
              "schiller             0.0   0.0   1.0  \n",
              "cytology             0.0   0.0   1.0  \n",
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              "      <th>std_herpes</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>std_aids</th>\n",
              "      <td>858</td>\n",
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              "      <td>753</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>std_hiv</th>\n",
              "      <td>858</td>\n",
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              "      <td>735</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>std_hep_b</th>\n",
              "      <td>858</td>\n",
              "      <td>3</td>\n",
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              "      <td>752</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>std_hpv</th>\n",
              "      <td>858</td>\n",
              "      <td>3</td>\n",
              "      <td>0.0</td>\n",
              "      <td>751</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>std_num_dx</th>\n",
              "      <td>858.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.087413</td>\n",
              "      <td>0.302545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>std_time_first_dx</th>\n",
              "      <td>858</td>\n",
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              "      <td>787</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.020979</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0.01049</td>\n",
              "      <td>0.101939</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0.020979</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>858.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>0.027972</td>\n",
              "      <td>0.164989</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>hinselmann</th>\n",
              "      <td>858.0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.040793</td>\n",
              "      <td>0.197925</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>schiller</th>\n",
              "      <td>858.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.086247</td>\n",
              "      <td>0.280892</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>cytology</th>\n",
              "      <td>858.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.051282</td>\n",
              "      <td>0.220701</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>biopsy</th>\n",
              "      <td>858.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.064103</td>\n",
              "      <td>0.245078</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlations\n",
        "df.corr()"
      ],
      "metadata": {
        "id": "kmhvbrPRtZ1W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 427
        },
        "outputId": "eeb41dc3-4e59-40d0-aa39-35e3d6b3f7dc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 age  std_num_dx    cancer       cin       hpv        dx  \\\n",
              "age         1.000000   -0.001606  0.110340  0.061443  0.101722  0.092635   \n",
              "std_num_dx -0.001606    1.000000 -0.015423  0.008070 -0.015423 -0.002289   \n",
              "cancer      0.110340   -0.015423  1.000000 -0.015072  0.886508  0.665647   \n",
              "cin         0.061443    0.008070 -0.015072  1.000000 -0.015072  0.606939   \n",
              "hpv         0.101722   -0.015423  0.886508 -0.015072  1.000000  0.616327   \n",
              "dx          0.092635   -0.002289  0.665647  0.606939  0.616327  1.000000   \n",
              "hinselmann -0.003967    0.076787  0.134264 -0.021233  0.134264  0.072215   \n",
              "schiller    0.103283    0.130873  0.157812  0.009119  0.157812  0.098952   \n",
              "cytology   -0.016862    0.055114  0.113446 -0.023938  0.113446  0.088740   \n",
              "biopsy      0.055956    0.097449  0.160905  0.113172  0.160905  0.157607   \n",
              "\n",
              "            hinselmann  schiller  cytology    biopsy  \n",
              "age          -0.003967  0.103283 -0.016862  0.055956  \n",
              "std_num_dx    0.076787  0.130873  0.055114  0.097449  \n",
              "cancer        0.134264  0.157812  0.113446  0.160905  \n",
              "cin          -0.021233  0.009119 -0.023938  0.113172  \n",
              "hpv           0.134264  0.157812  0.113446  0.160905  \n",
              "dx            0.072215  0.098952  0.088740  0.157607  \n",
              "hinselmann    1.000000  0.650249  0.192467  0.547417  \n",
              "schiller      0.650249  1.000000  0.361486  0.733204  \n",
              "cytology      0.192467  0.361486  1.000000  0.327466  \n",
              "biopsy        0.547417  0.733204  0.327466  1.000000  "
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              "      <th></th>\n",
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              "      <th>hinselmann</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>0.361486</td>\n",
              "      <td>0.733204</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cytology</th>\n",
              "      <td>-0.016862</td>\n",
              "      <td>0.055114</td>\n",
              "      <td>0.113446</td>\n",
              "      <td>-0.023938</td>\n",
              "      <td>0.113446</td>\n",
              "      <td>0.088740</td>\n",
              "      <td>0.192467</td>\n",
              "      <td>0.361486</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.327466</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>biopsy</th>\n",
              "      <td>0.055956</td>\n",
              "      <td>0.097449</td>\n",
              "      <td>0.160905</td>\n",
              "      <td>0.113172</td>\n",
              "      <td>0.160905</td>\n",
              "      <td>0.157607</td>\n",
              "      <td>0.547417</td>\n",
              "      <td>0.733204</td>\n",
              "      <td>0.327466</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b458f061-f115-4daf-831f-5b3f106c4cf1')\"\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",
              "  <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-b458f061-f115-4daf-831f-5b3f106c4cf1 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-b458f061-f115-4daf-831f-5b3f106c4cf1');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Looks like some cells have \"?\" which is NA\n",
        "#We have to convert these to NAs\n",
        "df = df.replace('?', np.nan)"
      ],
      "metadata": {
        "id": "XMo6sMEFtonr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Looking at missing values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "fdHqmleEtoqi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77144e94-a258-41b6-870f-350074b7f3de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                     0\n",
              "num_sex_partners       26\n",
              "first_sex               7\n",
              "num_preg               56\n",
              "smoker                 13\n",
              "smoke_years            13\n",
              "pack_years             13\n",
              "contracept            108\n",
              "contracept_years      108\n",
              "iud                   117\n",
              "iud_years             117\n",
              "stds                  105\n",
              "stds_num              105\n",
              "std_condy             105\n",
              "std_cerv_condy        105\n",
              "std_vag_condy         105\n",
              "std_vulvperi_condy    105\n",
              "std_syphilis          105\n",
              "std_pid               105\n",
              "std_herpes            105\n",
              "std_molluscum         105\n",
              "std_aids              105\n",
              "std_hiv               105\n",
              "std_hep_b             105\n",
              "std_hpv               105\n",
              "std_num_dx              0\n",
              "std_time_first_dx     787\n",
              "std_time_last_dx      787\n",
              "cancer                  0\n",
              "cin                     0\n",
              "hpv                     0\n",
              "dx                      0\n",
              "hinselmann              0\n",
              "schiller                0\n",
              "cytology                0\n",
              "biopsy                  0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualize missing values\n",
        "msno.matrix(df)"
      ],
      "metadata": {
        "id": "YcS8XzO5totp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "c433efce-6425-469e-8062-a6a865b2487f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7ff300c85b50>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Two columns have 787 missing values, let's drop those\n",
        "df.drop(['std_time_first_dx', 'std_time_last_dx'], axis=1, inplace = True)"
      ],
      "metadata": {
        "id": "GRisaLWBtowR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# fill pack_years and stds null vals with 0, not median\n",
        "df[['pack_years','stds']] = df[['pack_years','stds']].fillna(0)\n",
        "\n",
        "# fill other null values with median\n",
        "df = df.fillna(df.median())\n",
        "df.isnull().sum()\n",
        "df.to_csv(\"cleaned.csv\")"
      ],
      "metadata": {
        "id": "kWK3PCoCtozJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Create some visualizations\n",
        "sns.set(style='whitegrid', palette=\"deep\", font_scale=1.1, rc={\"figure.figsize\": [8, 5]})\n",
        "sns.distplot(\n",
        "    df['age'], norm_hist=False, kde=False, bins=20, hist_kws={\"alpha\": 1}\n",
        ").set(xlabel='Age', ylabel='Count');"
      ],
      "metadata": {
        "id": "UzaccrYHt1w0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "051608e5-ca78-4245-d234-0f3a64ce0c41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "`distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x360 with 1 Axes>"
            ],
            "image/png": 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zRw6Hw3v//PnzOnLkiBYsWNAthQEAAN/rdPCPHj26w/3IyEgtW7bsmr6SFwAA+Feng//BBx/szjoAAIAfdDr4Jam2tlYlJSU6ffq0brzxRk2dOlU33HBDd9UGAAB8rNMn9x06dEh33nmndu/erfr6eu3Zs0ff+ta3dPDgwe6sDwAA+FCn9/iffvppLVmyRLNnz/aObdmyRU8//fQlX9wDAACCU6f3+GtqanTPPfd0GJs1a5Zqamp8XhQAAOgenQ7+6OhoHTt2rMPY8ePHFR0d7fOiAABA9+j0of7c3FwtWLBA3/3ud3XzzTfr5MmTevHFFzVjxozurA8AAPhQp4N//vz56tWrl3784x/rzJkzuuGGG3TPPffovvvu6876AACAD33pof7q6mpt2rRJISEh+u53v6vdu3frD3/4g3bv3q22trZLDv8DAIDg9aXB/9JLL+m666677NyAAQP04osv+rwoAADQPb40+MvLy3XnnXdedm7KlCkqKyvzeVEAAKB7fGnwnz17Vv369bvsXFRUlOrq6nxeFAAA6B5fGvwRERE6ffr0ZedOnz6tPn36+LwoAADQPb40+JOTk/XTn/70snOFhYVKTU31eVEAAKB7fOnlfAsXLtTdd9+t+vp6ZWdn6/rrr1dtba127typX//613xcLwAAPciXBv/IkSO1efNmPfHEE9qxY4ccDocsy9LQoUO1adMmxcbG+qNOAADgA536AJ/Ro0dr9+7dOnHihM6ePasBAwZoyJAhPivC5XJ1uP/555/r29/+tlauXKmTJ0/qjjvuUEREhHd+7ty5euihh3z2+wEAMEWnP7lPkoYMGeLTwL/g0KFD3ttNTU26/fbbL7mE8N1331VYWJjPfzcAACbp9Jf0+MvevXvVv39/JScnB7oUAABsp0t7/P6wY8cO5eTkyOFwdBhPT0+Xw+HQ2LFjtWzZMg0YMCBAFQIA0HMFVfCfOnVKZWVlevLJJ71j1113nX71q18pLi5O9fX1Wr16tf75n/9ZP/nJT7q0dkVFhfd2eXm5z2r2haSkpECXgCsI9Gsl0L/fX0zokx7twQ49BlXwFxcXKykpSYMHD/aORUZGatSoUZKkv/u7v9PKlSt1++2367PPPtNXvvKVTq8dHx+vsLAwlZeXE7TotEC+Vkx5rZrQJz3aQ0/p0e12d9jZvVhQvcdfXFysnJycL3zMhbcALMvyR0kAANhK0AT/wYMHVVtbe8nZ/H/84x/1wQcfqL29XZ9++qn+7d/+TampqYqOjg5QpQAA9FxBc6i/qKhIGRkZ6tu3b4fxjz76SD/4wQ9UV1envn376hvf+IZ+8IMfBKhKAAB6tqAJ/tWrV192PCsrS1lZWX6uBgAAewqaQ/0AAKD7EfzAFbS0tgX1egBwNYLmUD8QbJyhvTT10WKfrbfzmbt8thYAXC32+AEAMAjBDwCAQQh+AAAMQvADAGAQgh8AAIMQ/AAAGITgBwDAIAQ/AAAGIfgBADAIwQ8AgEEIfgAADELwAwBgEIIfAACDEPwAABiE4AcAwCAEPwAABiH4AQAwCMEPAIBBCH4AAAxC8AMAYBCCHwAAgxD8AAAYhOAHAMAgBD8AAAYh+AEAMEjQBP+cOXM0atQouVwuuVwupaene+f279+vrKwsJSQkaNasWaqurg5gpQAA9FxBE/yS9Nhjj+nQoUM6dOiQ3nzzTUnSp59+qry8PM2fP19lZWVKT09XXl6ePB5PgKsFAKDnCargv5w33nhDQ4cOVXZ2tpxOp+bNm6fz58+rrKws0KUBANDj9A50AX9r3bp1Wrdunb72ta/p4Ycf1tixY1VVVaWRI0d6HxMSEqLY2FhVVVVp7NixnV67oqLCe7u8vPya6hwZ9/8UGdHnmtaAmbr62rvW12pPYUKf9GgPdugxaIJ/yZIlGj58uJxOp1577TUtWrRIRUVFam5u1le+8pUOj42KilJTU1OX1o+Pj1dYWJjKy8uVlJR0zfVOfbT4mte4YOczd/lsLQS3rrz2fPVaDXYm9EmP9tBTenS73R12di8WNIf6ExIS1LdvXzmdTk2bNk1JSUl66623FBERocbGxg6PbWhoUGRkZIAqBQCg5wqa4L+Yw+GQJMXExKiystI7blmWjh49qpiYmECVBgBAjxUUwX/u3DmVlpbK7XbL4/GopKREBw4cUFpamjIyMnTs2DHt2rVLLS0teumllxQeHq6UlJRAlw0AQI8TFO/xezwerVu3Th988IF69eqlYcOG6fnnn9fw4cMlSc8//7xWr16txx57TLGxsXrhhRfUu3dQlA4AQI8SFOnZv39/bdu27Yrzo0eP1muvvebHigAAsKegONQPAAD8g+AHAMAgBD8AAAYh+AE/aWlt69Ljv+yDQrq6HgBIQXJyH2ACZ2gvPvERQMCxxw8AgEEIfgAADELwAwBgEIIfAACDEPwAABiE4AcAwCAEPwAABiH4AQAwCMEPAIBBCH4AAAxC8AMAYBCCHwAAgxD8AAAYhOAHAMAgBD8AAAYh+AEAMAjBDwCAQQh+AAAMQvADAGAQgh8AAIMQ/AAAGITgBwDAIL0DXYAktbS0aNWqVfr973+v+vp6DR48WA8//LDuuOMOSVJsbKzCw8PlcDgkSVOmTNHatWsDWTIAAD1SUAS/x+PRjTfeqJ/97Gf66le/qtLSUi1evFjbt2/X1772NUnStm3bNHz48ABXCgBAzxYUh/ojIiKUn5+vm2++WSEhIZowYYKGDh2qw4cPB7o0AABsJSj2+C9WV1enmpoajRgxwjt27733yrIsJSYmqqCgQIMHD+7SmhUVFd7b5eXl11RfUlLSNT0f8JVrfS0HC7v08UXo0R7s0GPQBb/H49HSpUuVmZmpuLg4SVJhYaESEhL0+eefa926dZo/f75KSkoUGhra6XXj4+MVFham8vJyghu2YYfXsgl/JunRHnpKj263u8PO7sWC4lD/Be3t7Vq2bJkkafXq1d7xlJQUOZ1O9evXT48//rhOnz6t6urqQJUJAECPFTR7/JZl6fHHH9fHH3+sH/3oR3I6nVd87IWz+wEAQNcETfA/8cQTqqmp0csvv6zw8HDveHV1tTwej2JiYryH+gcNGqRbb701gNUCANAzBUXwnzp1Sq+88oqcTqfS0tK84wsWLFBCQoJWrVql2tpahYeHy+VyadOmTV94RAAAAFxeUAT/TTfdpKNHj15xfs+ePX6sBgAA+wqqk/sAAED3IvgBADAIwQ8AgEEIfgAADELwAwBgEIIf6KFaWtt6xJoAgktQXM4HoOucob009dFin66585m7fLoegODDHj8AAAYh+AEAMAjBDwCAQQh+AAAMQvADAGAQgh+Al68v5+PyQCD4cDkfAC9fXyLI5YFA8GGPHwAAgxD8AAAYhOAHAMAgBD8AAAYh+AEAMAjBDwCAQQh+AAAMQvAD6Dad+QCfpKQkn64H4IvxAT4Aug0fCAQEH/b4AQAwCMEPAIBBCH4AAAxC8AMAYJAeEfznzp3Tww8/LJfLpbS0NP385z8PdEkAgP/V0trWpaszOrMeuk+POKt/9erVamtrU2lpqT766CPNnTtXw4cP15gxYwJdGgAYj6s3epagD/7m5mbt3r1bRUVF6tu3r+Li4jR9+nRt27atU8FvWZYkqaWlxTvmdruvua7oyF7XvMYFbrfbp+t1x5rBvl53rGnaet2xZnes52utnjaF9vZdjVdarztqDybBvJ19tY3j4+O9tbV42uT0w+vmalzIuwv5dzGHdaWZIPHnP/9Zs2bNUkVFhXds165devHFF1VUVPSlz29oaFBVVVV3lggAQNCJiYlRVFTUJeM9Yo8/MjKyw1hUVJSampo69fzIyEjFxMQoNDRUDoejO0oEACBoWJal1tbWS7LzgqAP/oiIiEtCvrGx8YoNXSwkJOSy/+IBAMCu+vTpc8W5oD+rf+jQoZKkmpoa79iRI0c0YsSIAFUEAEDPFfTBHxERoSlTpujZZ59VY2OjKisrtW3bNk2fPj3QpQEA0OME/cl90l+v4/+Xf/kXlZaWKjIyUosWLdLs2bMDXRYAAD1Ojwh+AADgG0F/qB8AAPgOwQ8AgEEIfgAADELwAwBgENsGf2FhoXJzcxUfH6+CgoIOc1VVVZo1a5YSEhKUlZWlAwcOBKjKq9fS0qLHHntMEydOlMvlUnZ2tvbt2+edt0OPF6xdu1YTJkzQ17/+dU2cOFEbNmzwztmpz7q6Oo0ePVqzZs3yjtmpvzlz5mjUqFFyuVxyuVxKT0/3zu3fv19ZWVlKSEjQrFmzVF1dHcBKr82ePXuUlZWlxMRETZw4UXv37pVkj215Ydtd+ImLi9OaNWu883bZjv/zP/+jBQsWKDU1VWPHjlVBQYH3g+TssB1l2dSePXusN954w/rXf/1Xa/ny5d7xlpYWa+LEidamTZsst9ttFRcXWykpKVZ9fX0Aq+26pqYm67nnnrM++ugjq62tzXrrrbesxMRE64MPPrBNjxe8//77VlNTk2VZlnXmzBkrMzPTeu2112zXZ0FBgfXtb3/bmjlzpmVZ9nmtXvCP//iP1pYtWy4Zr6urs5KSkqzi4mLL7XZbmzZtstLT063W1tYAVHltfve731njx4+3ysrKrLa2Nuvs2bPWhx9+aLttaVmW1djYaCUmJlr79++3LMte23HevHnWkiVLrPPnz1uffvqpNXv2bOvpp5+2zXa07R7/5MmTlZ6erujo6A7j+/fvl9vt1rx58+R0OpWdna0hQ4Z4/1XeU0RERCg/P18333yzQkJCNGHCBA0dOlSHDx+2TY8XDB8+XBEREd77ISEh+vDDD23V5/79+3X8+HHl5uZ2GLNLf1/kjTfe0NChQ5WdnS2n06l58+bp/PnzKisrC3RpXfbcc8/pgQceUHJyskJCQtS/f38NHjzYltty79696t+/v5KTkyXZazuePHlSWVlZ6tOnj6Kjo5WRkaHq6mrbbEfbBv+VVFdXKyYmRiEh/9d6XFxcjz0kdUFdXZ1qamo0YsQIW/a4efNmuVwujR8/Xk1NTcrOzrZNny0tLVqzZo2eeOKJDl8kZZf+/ta6des0evRo3XPPPfr9738v6a+HTkeOHOl9TEhIiGJjY3vct2q2tbXp8OHDqq+v15QpU3T77bdrxYoVamhosOW23LFjh3JycryvWbtsR0m69957tXPnTjU3N6uurk579uzR+PHjbbMdjQv+pqamS760pyvf9heMPB6Pli5dqszMTMXFxdmyx/nz5+vgwYPev2z69etnmz43b96ssWPHdvhLU7Lfa3XJkiXat2+fSktLdffdd2vRokU6fvy4mpubbdHnJ598otbWVr3++uv6yU9+otdff12ffvqpnnrqKdtty1OnTqmsrEw5OTneMbtsR0lKSkrSBx98oOTkZI0dO1b9+vXTP/zDP9hmOxoX/JGRkWpsbOww1tDQ0Olv+ws27e3tWrZsmSRp9erVkuzX4wUOh0N///d/rz59+uiHP/yhLfo8ceKEduzYoYceeuiSOTv097cSEhLUt29fOZ1OTZs2TUlJSXrrrbcUERFhiz7Dw8MlSbNnz9YNN9ygfv36adGiRfrNb35ju21ZXFyspKQkDR482Dtml+3Y1tamefPmadKkSfrDH/6g9957T71799aTTz5pm+1oXPCPGDFCVVVVam9v945VVlb2yG/7syxLjz/+uD7++GOtX79eTqdTkr16vByPx6MPP/zQFn2Wl5frk08+0ZQpUzRu3Dg9+eST+vOf/6xx48Zp+PDhPb6/L3LhEHFMTIwqKyu945Zl6ejRo4qJiQlUaVelX79+uvHGGzu8XXOBHV6rf6u4uLjD3r5kn+342Wef6cyZM5o9e7acTqeio6M1ffp0lZaW2mY72jb4PR6P3G632tvb1dbWJrfbrdbWVqWmpsrpdOrll19WS0uLdu3apePHjysjIyPQJXfZE088oZqaGm3cuNG7tyHJVj22trbq1Vdf1blz59Te3q4//vGP2rJli8aOHWuLPjMzM/Xmm2+qqKhIRUVFeuihhxQTE6OioiKNGTOmx/d3wblz51RaWiq32y2Px6OSkhIdOHBAaWlpysjI0LFjx7Rr1y61tLTopZdeUnh4uFJSUgJddtJw3YwAAARRSURBVJfNmDFDP//5z/WXv/xFjY2N2rhxoyZNmmSL1+oFBw8eVG1tre68884O43bZjhdOyPzFL36h1tZWNTQ0aMeOHYqNjbXPdgzwVQXd5rnnnrNiYmI6/Fy4rK+ystKaMWOGNWrUKCszM9N7OUpPcvLkSSsmJsaKj4+3EhMTvT8bNmywLMsePVqWZbW2tlr33XeflZKSYiUmJlqTJ0+2Nm7caLW3t1uWZZ8+L9i2bZv3cj7Lsk9/Z8+etXJzc63ExEQrKSnJmjlzpvX222975999910rMzPTGjVqlDVjxgyrqqoqgNVevdbWVmvNmjVWSkqKNWbMGKugoMBqaGiwLMs+23LlypXWkiVLLjtnl+145MgRa86cOVZycrKVmppqPfjgg9aZM2csy7LHduTb+QAAMIhtD/UDAIBLEfwAABiE4AcAwCAEPwAABiH4AQAwCMEPAIBBCH4AAAxC8APosq1btyo2NlYvvPBCoEsB0EV8gA+ALsvOzlZtba0iIiK0b9++Dl9TCiC48acVQJccPHhQR48e1TPPPKMzZ87ot7/9rXeuqalJK1as0OjRo5WWlqb//M//1KRJk7R9+3bvY2pqarRgwQJ94xvfUFpamlatWqXm5uZAtAIYieAH0CVbt27V17/+dd1+++1KS0vT1q1bvXPf//739f7776ukpER79+7VsWPHVFtb652vq6vT7NmzNW7cOL311lsqLi7WiRMn9NRTTwWiFcBIBD+ATqurq9Pu3bs1c+ZMSdLMmTNVWlqqU6dOqb29XcXFxXrooYd0/fXXKzw8XMuXL+/w/OLiYg0bNkzf+c535HQ61b9/fy1evFhFRUVqa2sLREuAcXoHugAAPcf27dvldDr1rW99S5I0ceJEDRgwQK+88oq+853vqKWlRTfddJP38ZGRkYqOjvbeP378uP70pz8pOTnZO2ZZlhwOhz755BNdf/31/msGMBTBD6BTLMvSK6+8IrfbrfT0dO/4uXPntG3bNj344INyOp06deqUhg0bJklqbm5WfX2997EDBw5UamqqXn75Zb/XD+CvCH4AnfL222/rww8/1JYtW3TLLbd4x8+ePavp06frzTffVHZ2tn74wx8qNjZWUVFR+vd///cOa+Tm5uqnP/2ptm7dqpycHPXp00dnzpzRf//3f3f4xwSA7sN7/AA6ZevWrRo/frySkpI0cOBA78/IkSOVmZmprVu3asWKFRo2bJiysrI0efJkDR06VP3791dYWJgk6atf/aq2bt2qt99+WxkZGUpOTtb999+vo0ePBrg7wBxcxw+g2zQ0NCg1NVVbtmyRy+UKdDkAxB4/AB86efKkDhw4oLa2NtXX1+t73/uebrnlFo0aNSrQpQH4X7zHD8BnWlpa9L3vfU8nT56U0+nUqFGjtHHjRvXuzV81QLDgUD8AAAbhUD8AAAYh+AEAMAjBDwCAQQh+AAAMQvADAGAQgh8AAIP8f/kouE2EkStKAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"The minimum age is:\", df['age'].min())\n",
        "print(\"Old df shape\", df.shape)\n",
        "#The minimum age is 13, we should get rid of anyone who is below 21\n",
        "\n",
        "df = df.loc[df['age'] >= 21]\n",
        "print(\"New df shape\", df.shape)"
      ],
      "metadata": {
        "id": "yNzQjG-st1z8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f95954c9-a12b-402d-c2d2-d136b8cec008"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The minimum age is: 13\n",
            "Old df shape (858, 34)\n",
            "New df shape (634, 34)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.value_counts('cancer')\n",
        "#We have 617 non cancer and 17 cancer "
      ],
      "metadata": {
        "id": "p_9MhAPTt13F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f4a60680-98c5-4e36-8347-707f22abcc3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cancer\n",
              "0    617\n",
              "1     17\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Some more visualizations\n",
        "df['stds_num'] = df['stds_num'].astype(np.float16)\n",
        "\n",
        "sns.lmplot(x = 'age', y = 'stds_num', data = df, fit_reg=False, hue = 'cancer')\n"
      ],
      "metadata": {
        "id": "OYLMRSNet5Ub",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "outputId": "5e547750-e888-4cd8-bb13-dc3bbe0fa084"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f2a20684d60>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 414.805x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['smoke_years'] = df['smoke_years'].astype(np.float16)\n",
        "sns.lmplot(x = 'age', y = 'smoke_years', data = df, fit_reg=False, hue = 'cancer')\n"
      ],
      "metadata": {
        "id": "6IUf4lGCt5XS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "fd55e89c-790c-4a17-e498-47d8d2a321f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f2a205f4a60>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 414.805x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['num_sex_partners'] = df['num_sex_partners'].astype(np.float16)\n",
        "sns.lmplot(x = 'age', y = 'num_sex_partners', data = df, fit_reg=False, hue = 'cancer')\n"
      ],
      "metadata": {
        "id": "y2JujDykt5aX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 380
        },
        "outputId": "2c9d6cb0-8f87-40d2-b07b-f91a9ca0c709"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f2a20519be0>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 414.805x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Converting them all to floats\n",
        "df = df.astype(np.float16)"
      ],
      "metadata": {
        "id": "FWOc5w6ut5dD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for v in df.columns:\n",
        "    print(df[v].value_counts())"
      ],
      "metadata": {
        "id": "YxKz9B6Wt5hc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "316d56c0-bacb-41b1-9ff3-e5dd31f9f667"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "23.0    54\n",
            "21.0    46\n",
            "24.0    39\n",
            "25.0    39\n",
            "26.0    38\n",
            "28.0    37\n",
            "30.0    35\n",
            "29.0    33\n",
            "27.0    33\n",
            "22.0    30\n",
            "35.0    29\n",
            "33.0    28\n",
            "31.0    27\n",
            "36.0    24\n",
            "34.0    24\n",
            "32.0    18\n",
            "37.0    17\n",
            "41.0    13\n",
            "40.0    12\n",
            "38.0     9\n",
            "39.0     9\n",
            "44.0     6\n",
            "45.0     6\n",
            "42.0     6\n",
            "43.0     5\n",
            "46.0     3\n",
            "48.0     2\n",
            "70.0     2\n",
            "49.0     2\n",
            "52.0     2\n",
            "59.0     1\n",
            "79.0     1\n",
            "84.0     1\n",
            "47.0     1\n",
            "50.0     1\n",
            "51.0     1\n",
            "Name: age, dtype: int64\n",
            "2.0     219\n",
            "3.0     169\n",
            "1.0     128\n",
            "4.0      61\n",
            "5.0      37\n",
            "6.0       9\n",
            "7.0       4\n",
            "8.0       4\n",
            "15.0      1\n",
            "10.0      1\n",
            "9.0       1\n",
            "Name: num_sex_partners, dtype: int64\n",
            "17.0    132\n",
            "18.0    119\n",
            "15.0     95\n",
            "16.0     78\n",
            "19.0     57\n",
            "20.0     36\n",
            "14.0     29\n",
            "21.0     20\n",
            "13.0     15\n",
            "23.0      9\n",
            "22.0      9\n",
            "26.0      7\n",
            "27.0      6\n",
            "24.0      6\n",
            "29.0      5\n",
            "12.0      3\n",
            "28.0      3\n",
            "25.0      2\n",
            "32.0      1\n",
            "11.0      1\n",
            "10.0      1\n",
            "Name: first_sex, dtype: int64\n",
            "2.0     221\n",
            "3.0     134\n",
            "1.0     132\n",
            "4.0      70\n",
            "5.0      35\n",
            "6.0      18\n",
            "0.0      14\n",
            "7.0       6\n",
            "8.0       2\n",
            "11.0      1\n",
            "10.0      1\n",
            "Name: num_preg, dtype: int64\n",
            "0.0    541\n",
            "1.0     93\n",
            "Name: smoker, dtype: int64\n",
            "0.000000     541\n",
            "1.266602      13\n",
            "9.000000       9\n",
            "16.000000      6\n",
            "5.000000       6\n",
            "8.000000       6\n",
            "11.000000      5\n",
            "10.000000      5\n",
            "14.000000      4\n",
            "15.000000      4\n",
            "19.000000      3\n",
            "6.000000       3\n",
            "2.000000       3\n",
            "3.000000       3\n",
            "12.000000      3\n",
            "7.000000       3\n",
            "22.000000      2\n",
            "4.000000       2\n",
            "13.000000      2\n",
            "37.000000      1\n",
            "0.500000       1\n",
            "21.000000      1\n",
            "18.000000      1\n",
            "34.000000      1\n",
            "1.000000       1\n",
            "32.000000      1\n",
            "24.000000      1\n",
            "28.000000      1\n",
            "20.000000      1\n",
            "0.160034       1\n",
            "Name: smoke_years, dtype: int64\n",
            "0.000000     541\n",
            "0.513184      15\n",
            "2.000000       4\n",
            "1.000000       4\n",
            "0.750000       3\n",
            "6.000000       3\n",
            "1.349609       2\n",
            "2.800781       2\n",
            "3.000000       2\n",
            "4.500000       2\n",
            "1.200195       2\n",
            "1.400391       2\n",
            "0.160034       2\n",
            "12.000000      2\n",
            "8.000000       2\n",
            "2.400391       2\n",
            "9.000000       2\n",
            "0.799805       2\n",
            "1.599609       2\n",
            "1.299805       1\n",
            "0.049988       1\n",
            "4.800781       1\n",
            "0.399902       1\n",
            "0.001000       1\n",
            "0.370117       1\n",
            "2.199219       1\n",
            "2.699219       1\n",
            "5.500000       1\n",
            "7.000000       1\n",
            "0.899902       1\n",
            "22.000000      1\n",
            "1.650391       1\n",
            "2.500000       1\n",
            "2.250000       1\n",
            "4.000000       1\n",
            "7.601562       1\n",
            "0.700195       1\n",
            "0.099976       1\n",
            "1.250000       1\n",
            "3.400391       1\n",
            "0.040009       1\n",
            "19.000000      1\n",
            "21.000000      1\n",
            "0.320068       1\n",
            "2.599609       1\n",
            "15.000000      1\n",
            "5.699219       1\n",
            "3.300781       1\n",
            "3.500000       1\n",
            "0.024994       1\n",
            "2.750000       1\n",
            "0.199951       1\n",
            "5.000000       1\n",
            "2.099609       1\n",
            "37.000000      1\n",
            "7.500000       1\n",
            "0.300049       1\n",
            "Name: pack_years, dtype: int64\n",
            "1.0    458\n",
            "0.0    176\n",
            "Name: contracept, dtype: int64\n",
            "0.000000     176\n",
            "0.500000      89\n",
            "1.000000      55\n",
            "3.000000      34\n",
            "2.000000      33\n",
            "5.000000      32\n",
            "0.250000      29\n",
            "6.000000      24\n",
            "7.000000      21\n",
            "8.000000      18\n",
            "4.000000      18\n",
            "0.080017      14\n",
            "9.000000      12\n",
            "10.000000     11\n",
            "0.160034      10\n",
            "15.000000      6\n",
            "0.660156       5\n",
            "0.330078       5\n",
            "12.000000      4\n",
            "20.000000      4\n",
            "0.750000       4\n",
            "0.580078       3\n",
            "0.419922       3\n",
            "1.500000       2\n",
            "16.000000      2\n",
            "0.669922       2\n",
            "19.000000      2\n",
            "2.281250       2\n",
            "13.000000      2\n",
            "11.000000      2\n",
            "22.000000      1\n",
            "14.000000      1\n",
            "2.500000       1\n",
            "4.500000       1\n",
            "6.500000       1\n",
            "0.170044       1\n",
            "3.500000       1\n",
            "0.409912       1\n",
            "30.000000      1\n",
            "17.000000      1\n",
            "Name: contracept_years, dtype: int64\n",
            "0.0    551\n",
            "1.0     83\n",
            "Name: iud, dtype: int64\n",
            "0.000000     551\n",
            "3.000000      11\n",
            "2.000000      10\n",
            "5.000000       9\n",
            "1.000000       8\n",
            "8.000000       7\n",
            "7.000000       7\n",
            "6.000000       5\n",
            "4.000000       5\n",
            "11.000000      3\n",
            "0.080017       2\n",
            "0.500000       2\n",
            "0.330078       1\n",
            "9.000000       1\n",
            "0.409912       1\n",
            "0.160034       1\n",
            "0.910156       1\n",
            "1.500000       1\n",
            "10.000000      1\n",
            "12.000000      1\n",
            "15.000000      1\n",
            "0.250000       1\n",
            "17.000000      1\n",
            "19.000000      1\n",
            "0.580078       1\n",
            "0.170044       1\n",
            "Name: iud_years, dtype: int64\n",
            "0.0    575\n",
            "1.0     59\n",
            "Name: stds, dtype: int64\n",
            "0.0    575\n",
            "1.0     29\n",
            "2.0     26\n",
            "3.0      4\n",
            "Name: stds_num, dtype: int64\n",
            "0.0    604\n",
            "1.0     30\n",
            "Name: std_condy, dtype: int64\n",
            "0.0    634\n",
            "Name: std_cerv_condy, dtype: int64\n",
            "0.0    631\n",
            "1.0      3\n",
            "Name: std_vag_condy, dtype: int64\n",
            "0.0    605\n",
            "1.0     29\n",
            "Name: std_vulvperi_condy, dtype: int64\n",
            "0.0    621\n",
            "1.0     13\n",
            "Name: std_syphilis, dtype: int64\n",
            "0.0    633\n",
            "1.0      1\n",
            "Name: std_pid, dtype: int64\n",
            "0.0    634\n",
            "Name: std_herpes, dtype: int64\n",
            "0.0    633\n",
            "1.0      1\n",
            "Name: std_molluscum, dtype: int64\n",
            "0.0    634\n",
            "Name: std_aids, dtype: int64\n",
            "0.0    620\n",
            "1.0     14\n",
            "Name: std_hiv, dtype: int64\n",
            "0.0    634\n",
            "Name: std_hep_b, dtype: int64\n",
            "0.0    632\n",
            "1.0      2\n",
            "Name: std_hpv, dtype: int64\n",
            "0.0    582\n",
            "1.0     52\n",
            "Name: std_num_dx, dtype: int64\n",
            "0.0    617\n",
            "1.0     17\n",
            "Name: cancer, dtype: int64\n",
            "0.0    625\n",
            "1.0      9\n",
            "Name: cin, dtype: int64\n",
            "0.0    617\n",
            "1.0     17\n",
            "Name: hpv, dtype: int64\n",
            "0.0    611\n",
            "1.0     23\n",
            "Name: dx, dtype: int64\n",
            "0.0    607\n",
            "1.0     27\n",
            "Name: hinselmann, dtype: int64\n",
            "0.0    573\n",
            "1.0     61\n",
            "Name: schiller, dtype: int64\n",
            "0.0    604\n",
            "1.0     30\n",
            "Name: cytology, dtype: int64\n",
            "0.0    589\n",
            "1.0     45\n",
            "Name: biopsy, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df.columns\n",
        "\n",
        "#Drop: std_cerv_condy, std_vag_condy, std_pid, std_herpes, std_molluscum, std_aids, std_hep_b, std_hpv due to lack of observations\n",
        "\n",
        "df.drop('std_cerv_condy', axis = 1, inplace = True)\n",
        "df.drop('std_vag_condy', axis = 1, inplace = True)\n",
        "df.drop('std_pid', axis = 1, inplace = True)\n",
        "df.drop('std_herpes', axis = 1, inplace = True)\n",
        "df.drop('std_molluscum', axis = 1, inplace = True)\n",
        "df.drop('std_aids', axis = 1, inplace = True)\n",
        "df.drop('std_hep_b', axis = 1, inplace = True)\n",
        "df.drop('std_hpv', axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "bkIjN843t5kU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#The columns about how the sample was obtained are useless\n",
        "#Dropping hinselmann, schiller, cytology, biopsy\n",
        "\n",
        "df.drop('hinselmann', axis = 1, inplace = True)\n",
        "df.drop('schiller', axis = 1, inplace = True)\n",
        "df.drop('cytology', axis = 1, inplace = True)\n",
        "df.drop('biopsy', axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "s2fNf50guFgH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#The hpv variable is the exact same as the cancer variable\n",
        "df.drop('hpv', axis = 1, inplace = True)\n",
        "\n",
        "#I looked online and no one could define what the dx variable was so we're getting rid of it \n",
        "df.drop('dx', axis = 1, inplace = True)\n"
      ],
      "metadata": {
        "id": "a9vqOTJ-uFmW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's build a baseline decision tree model\n",
        "\n",
        "#Get labels as an array and not as an array \n",
        "labels = np.array(df['cancer'])\n",
        "columns_labels = df['cancer']\n",
        "\n",
        "#Drop cancer variable (and HPV variable)\n",
        "df.drop('cancer', axis = 1, inplace = True)\n",
        "df_list = list(df.columns)\n",
        "\n",
        "#Create dummies/one hot encoding\n",
        "df_dum = pd.get_dummies(df)\n",
        "df_list_dum = list(df_dum.columns)"
      ],
      "metadata": {
        "id": "1kAhSfNluFpO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split the data into training and testing sets\n",
        "train_df, test_df, train_labels, test_labels = train_test_split(df_dum, labels, test_size = 0.25, random_state = 42)\n",
        "print('Training Features Shape:', train_df.shape)\n",
        "print('Training Labels Shape:', train_labels.shape)\n",
        "print('Testing Features Shape:', test_df.shape)\n",
        "print('Testing Labels Shape:', test_labels.shape)\n"
      ],
      "metadata": {
        "id": "OenucXKJuFsF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12aa37f3-5c19-4034-ab29-3b9c18ce2e0c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Features Shape: (475, 19)\n",
            "Training Labels Shape: (475,)\n",
            "Testing Features Shape: (159, 19)\n",
            "Testing Labels Shape: (159,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Instantiate model with 1000 decision trees\n",
        "rf = RandomForestClassifier(n_estimators = 1000, random_state = 42)\n",
        "\n",
        "# Train the model on training data\n",
        "rf.fit(train_df, train_labels)\n",
        "\n",
        "y_pred=rf.predict(test_df)\n"
      ],
      "metadata": {
        "id": "0HQU-sDduFus"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Test Accuracy:\",metrics.accuracy_score(test_labels, y_pred))"
      ],
      "metadata": {
        "id": "qeeV_9YyuNqw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d0bcafa-9716-4445-e511-9e5b3313ff4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test Accuracy: 0.9811320754716981\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_imp = pd.Series(rf.feature_importances_,index=df_dum.columns).sort_values(ascending=False)\n",
        "feature_imp"
      ],
      "metadata": {
        "id": "FhPA1yKjuNwe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ec689f7-4700-4f6d-b764-8b96e42028b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                   0.186091\n",
              "contracept_years      0.176154\n",
              "first_sex             0.154102\n",
              "num_sex_partners      0.117749\n",
              "num_preg              0.098539\n",
              "iud_years             0.070197\n",
              "pack_years            0.043511\n",
              "smoke_years           0.043052\n",
              "contracept            0.023575\n",
              "iud                   0.021491\n",
              "stds_num              0.017794\n",
              "stds                  0.012771\n",
              "std_num_dx            0.010220\n",
              "smoker                0.007831\n",
              "std_condy             0.006457\n",
              "std_vulvperi_condy    0.004793\n",
              "std_hiv               0.003064\n",
              "std_syphilis          0.001991\n",
              "cin                   0.000617\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "# Creating a bar plot\n",
        "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
        "# Add labels to your graph\n",
        "plt.xlabel('Feature Importance Score')\n",
        "plt.ylabel('Features')\n",
        "plt.title(\"Visualizing Important Features\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "jhroX9GBuNzm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "fde536e3-382d-4b98-cd63-8a25ce47f848"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#This all makes sense\n",
        "#We could drop least important features: cin, std_syphilis, std_hiv, std_condy, std_vulvperi_condy\n",
        "#Let's get a more parsimonius baseline mode\n",
        "df.drop('cin', axis = 1, inplace = True)\n",
        "df.drop('std_syphilis', axis = 1, inplace = True)\n",
        "df.drop('std_hiv', axis = 1, inplace = True)\n",
        "df.drop('std_condy', axis = 1, inplace = True)\n",
        "df.drop('std_vulvperi_condy', axis = 1, inplace = True)\n",
        "\n",
        "#Create dummies/one hot encoding\n",
        "df_dum = pd.get_dummies(df)\n",
        "df_list_dum = list(df_dum.columns)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "train_df, test_df, train_labels, test_labels = train_test_split(df_dum, labels, test_size = 0.25, random_state = 42)\n",
        "print('Training Features Shape:', train_df.shape)\n",
        "print('Training Labels Shape:', train_labels.shape)\n",
        "print('Testing Features Shape:', test_df.shape)\n",
        "print('Testing Labels Shape:', test_labels.shape)\n"
      ],
      "metadata": {
        "id": "E1UReeB3uN2u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbe33251-5ba1-4f5a-a378-f5a12b1a9c6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Features Shape: (475, 14)\n",
            "Training Labels Shape: (475,)\n",
            "Testing Features Shape: (159, 14)\n",
            "Testing Labels Shape: (159,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model on training data\n",
        "rf = RandomForestClassifier(n_estimators = 1000, random_state = 42)\n",
        "\n",
        "rf.fit(train_df, train_labels)\n",
        "\n",
        "y_pred=rf.predict(test_df)\n",
        "print(\"Accuracy:\",metrics.accuracy_score(test_labels, y_pred))"
      ],
      "metadata": {
        "id": "R1Up2MpmuN52",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4f402e8-533a-4770-8161-104b3c1be973"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9811320754716981\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_imp = pd.Series(rf.feature_importances_,index=df_dum.columns).sort_values(ascending=False)\n",
        "feature_imp"
      ],
      "metadata": {
        "id": "c_xW0W3PuYrs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3aa7435c-bde0-4bd4-cd50-d11eb6430b99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age                 0.187704\n",
              "contracept_years    0.176647\n",
              "first_sex           0.158491\n",
              "num_sex_partners    0.114913\n",
              "num_preg            0.103606\n",
              "iud_years           0.071381\n",
              "pack_years          0.042708\n",
              "smoke_years         0.042602\n",
              "contracept          0.026038\n",
              "iud                 0.023577\n",
              "stds_num            0.019641\n",
              "stds                0.014434\n",
              "std_num_dx          0.009629\n",
              "smoker              0.008629\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "# Creating a bar plot\n",
        "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
        "# Add labels to your graph\n",
        "plt.xlabel('Feature Importance Score')\n",
        "plt.ylabel('Features')\n",
        "plt.title(\"Visualizing Important Features\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "Ff6SX-cLuYu9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "7d69eae2-e081-43f9-850c-53991bebe8ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df['std_num_dx'].value_counts())\n",
        "print(df['stds'].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m9vOX89lZJYG",
        "outputId": "b0902e4e-1d70-4829-8561-ac1d5feae763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0    582\n",
            "1.0     52\n",
            "Name: std_num_dx, dtype: int64\n",
            "0.0    575\n",
            "1.0     59\n",
            "Name: stds, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Dropping additional features per convo with Julia\n",
        "df.drop('contracept', axis = 1, inplace = True)\n",
        "df.drop('iud', axis = 1, inplace = True)\n",
        "df.drop('std_num_dx', axis = 1, inplace = True)\n",
        "df.drop('stds', axis = 1, inplace = True)\n",
        "df.drop('smoker', axis = 1, inplace = True)\n",
        "df.drop('smoke_years', axis = 1, inplace = True)\n",
        "\n",
        "#These are the final variables to keep\n",
        "#Age\n",
        "#Contracept_years - # years on contraception\n",
        "#First_sex - age first intercourse\n",
        "#Num_sex_partners\n",
        "#Num_preg\n",
        "#Iud_years - # years with IUD\n",
        "#Pack_years\n",
        "#Stds - number of STD diagnoses\n"
      ],
      "metadata": {
        "id": "8bZYHTknYu2W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Now let's try to expand the dataset with smote \n",
        "#Synthetic minority oversampling technique\n",
        "#Used when there is imbalanced classification, i.e. too few examples of the minority class \n",
        "#We can oversample the examples in the minority class \n",
        "#https://machinelearningmastery.com/smote-oversampling-for-imbalanced-classification/\n",
        "\n",
        "#An improvement on duplicating examples from the minority class is to synthesize \n",
        "#new examples from the minority class. This is a type of data augmentation for tabular \n",
        "#data and can be very effective.\n",
        "#SMOTE works by selecting examples that are close in the feature space, drawing a line\n",
        "#between the examples in the feature space and drawing a new sample at a point along that line.\n",
        "\n",
        "# Transform the dataset\n",
        "oversample = SMOTE() #Completely balance\n",
        "df, columns_labels = oversample.fit_resample(df, columns_labels)\n",
        "\n",
        "counter = Counter(columns_labels)\n",
        "print(counter)\n",
        "\n",
        "#Awesome, we have more observations now !"
      ],
      "metadata": {
        "id": "aSeELM1Vua9g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85771bd1-0a82-4738-98d5-a60780ab9a3c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Counter({0.0: 617, 1.0: 617})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.concat([df, columns_labels], axis=1)"
      ],
      "metadata": {
        "id": "O_1HIaXabOHr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_hRkDDO_bTgx",
        "outputId": "ba41f271-bb53-4a30-ef4a-114aee090712"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    age  num_sex_partners  first_sex  num_preg  pack_years  contracept_years  \\\n",
              "0  34.0               1.0       17.0       1.0    0.000000               0.0   \n",
              "1  52.0               5.0       16.0       4.0   37.000000               3.0   \n",
              "2  46.0               3.0       21.0       4.0    0.000000              15.0   \n",
              "3  42.0               3.0       23.0       2.0    0.000000               0.0   \n",
              "4  51.0               3.0       17.0       6.0    3.400391               0.0   \n",
              "\n",
              "   iud_years  stds_num  cancer  \n",
              "0        0.0       0.0     0.0  \n",
              "1        0.0       0.0     1.0  \n",
              "2        0.0       0.0     0.0  \n",
              "3        0.0       0.0     0.0  \n",
              "4        7.0       0.0     0.0  "
            ],
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              "\n",
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              "      <th>age</th>\n",
              "      <th>num_sex_partners</th>\n",
              "      <th>first_sex</th>\n",
              "      <th>num_preg</th>\n",
              "      <th>pack_years</th>\n",
              "      <th>contracept_years</th>\n",
              "      <th>iud_years</th>\n",
              "      <th>stds_num</th>\n",
              "      <th>cancer</th>\n",
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              "      <td>4.0</td>\n",
              "      <td>37.000000</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>46.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>42.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>23.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>51.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>3.400391</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd1584c9-4624-4457-9465-7bed004ff21f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "    .colab-df-container {\n",
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              "    .colab-df-convert {\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "\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-fd1584c9-4624-4457-9465-7bed004ff21f 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-fd1584c9-4624-4457-9465-7bed004ff21f');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Let's go back to our EDA, and combine the dataframe \n",
        "\n",
        "sns.lmplot(x = 'age', y = 'pack_years', data = df, fit_reg=False, hue = 'cancer')"
      ],
      "metadata": {
        "id": "Ze3CJB6rubDg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 387
        },
        "outputId": "a3621cc3-e658-4332-a7da-73901b680125"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f2b5f53bc70>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 422.685x360 with 1 Axes>"
            ],
            "image/png": 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RzlPFDau+rFjWTP+xsamRDcD4ZJrly5orGFX1K3SXzrm2kG584ifAm0sdrojMo5AOAjcBrwDuxI1ynsAlnSeKH1Z92bppDUEQkpoICEP3OQhCtm5aU+nQqlqhLWvm6qcWGxsoWYwikp9Cks3ZOR/Piz6yf5Z5nL92BZdtXU9XexPDYwFd7U1cpuKABRXasmau5JRp6ixHuCIyj7yn0ay1vyhlIPXu/LUrqiK51EoJdnahPzPhEgy+T8MpK+etRuvYuIUjO24iQwrPT7rEkw5IrVYLP5FKK2inTmPMMuACYDngZY9ba79U5LikBGqlBDu3Ci3e1j2VNBYqe25ZvQE2X3pCNdrhgbCM0YvIbPJONsaYi4Cv4cqg24AhoBXYDyjZ1IBaKcGea6F/cNf2Be+xmbWfWm9vqUIVkTwVsmbzj8BHrLVdwFD0+SPAP5UkMim6WinBDgb6yAQBk0d/yUTfL5g8+ksyQaCN00RqWCHJ5lxccoHjU2jXAu8pakRSMiuWNTM+mZ52rBpLsL2GJjJDzxKm0+DFCNNpMkPP4jU0VTo0EVmkQpLNOMen3fpzuj2fUtyQpFRqpgTby/mjN/txEakthSSbe4BXR3++E7gFuA014qwZtVKCHY6PEW8/BS8eJwwzePE48fZTCMfHFn6yiFSlQqrRLgWyE/7vwa3htANXFjsoKZ1qKcGej9+5nGC4n8SyM6eOZSZT+K1dFYxKRE5GIffZPJPz537gT/J9rjEmCXwW+E1gGbAP+Ftr7Tei8+twHQrWR+cut9bele/rS32Z634ZNdUUqV2FTKNhjHmpMeZzxpjbo683GGPyuWPOx5VIXwh0AFcBXzHGnGuMSQC348qqu4APA9uNMfo1dolqWb2B7s2X4rd2EaaG8Vu76J6n07OIVL9C7rN5C/AvwFdxSQNcsvog8Mr5nmutHQGuyTn0bWPMo7gbRJ8LNAEfjbYvuMUY8xfAVuDz+cYn9WXW+2VEpGZ5YZjf3dXGmAeAy6y1PzbG9Ftru6Lpsf3W2tk7I879WqcCTwEbgYuA37bWXpxz/kZg1Fq74HpQb2/vKtQMVESqTE9Pj+oncxRSILDSWvvj6M/ZDDVR4GtgjPGBm4FbrbX3GWNeCwzOeNgA0F3I665bt45kMjnn+d7eXnp6egp5yYqplVgVZ/HVSqy1EifUVqz1rJA1myeNMefNOLYBt6Cfl2h76S9HX2YLDIZxVW25OnDtcEREpA4Ukmz+CdhmjHk74Btjfg83Qvl4Pk82xni4NZgzgDdYayeiUw8CL4oSUdZ50XEREakDhZQ+fylKCFdGz7sO+KS19qt5vsQNwFrgYmttbjOunUAKeLcx5npcYcC5uOo0OUm1sqWAiNS3gtZbrLVfAL5Q6EWMMc8FLsO1vDlojMme+pC19kPGmNfh7rP5IG5a7vXW2qOFXkemq5UtBUSk/hVS+vxvwA3W2nsKvUi08dqclRnW2geAFxf6ujK/WtlSQETqXyEjmwTwA2OMxd1vc4u1drg0YUkxHDo6SlvT9L/iatxSQETqXyFrNv/LGPPnwNuAPwM+aoz5D+BfrLW7SxWgLN6KZc30HxubGtlAdW4pAMe3gc7dYbNl9YY5j4tIeUVr9p61Nr3gg2dR6JrNAPBJ4JPGmI3Ap4B7jDH3Af+MG+1oD94qsXXTGm7cdj8pApKJOOOT6arcUiB3G2ivsZVguJ8jO24itf4iRu7//gnHUesaqSPGmGbgb4E3ASuAZ3AtvSxwPa5npI/rsP+X1tr7oudtAr6PK6r6R+A0YBdwibX24Hyvba39r+j8bwPvxxVlPQt8ylp7fXRuFe6G+UtxhWFrgPNZZKVwQb3RogDajDGX4xprrgY+g7t35hrgi4sJQkqjVrYUyN0G2vM8tx103Gfop7fPenxw1/ZKhyxSTJ/neCeVNlz7r8eic9cBZwFnAo8AX4v6SeZ6A67113Nw9yxem89rG2Muwm0V839xN9G/AXivMeYPZ7z+24DNQCvw6GK/yUIKBF6CuxHzzcAe4NPAV7NlzMaYm4EngT9abDBSfLWwpUAw0IfX2DrtmOcnyUyMEfdXnHBc20NLvYhad/0e8KvW2kcBrLX7cY2LIWcUYYz5v7iq3jW4n8FZV1lrB6PH3BI9Jp/XvhL4tLX2zuy1jDGfBt6OS0JZH7DWPn2y32sh02jfA/4DuGi2ijRr7bPGmM+cbECy9GT3r/ESjVPHwmCcWEMTYTB+wnG/s6BWfCLVbFX02c48EU1jfRR4Ca6rSiY6tZycZGOtPZDztGGgbaHXjpwD/Ga0Fp8Vx/WtzFWU3pOFJJszozWb+fSfTDCyNM21f03bi1/LyP3f1742Us+ejD6fC/zPjHOfw62jbLDWHo62XTlK/hukz/fa4NZvvmyt/dACr5NZ4Hxe8l6zySPRAFx9ErHIEjXX/jXdL3+T9rWRumatPYzbtuWzxphzAIwxZxlj1uNGMyPAgDGmA/hIEV8bXLHXu4wxrzTG+NHHOmPMK4rz3U1XUDVaHtRSWxZlrv1rtK+NLAHvxFWEfccYsxw4CPw18C7cPY0DwAHgfbjKsGK89v3W2q8bY1LA3+NaiYErACgoqeWr2MlGZc8iIgWINpf8P9HHTDM77d+a87ydzPgFf2ZLsQVeG2vtDmDHHOeenPn6J6Pg0mcREZFCKdmIiEjJFTvZaM1GREROkHeyMcY8b47jL8/58rKTjkhEROpOISObb0bld1OMMRuAbdmvrbVfKVZgIiJSPwpJNl/D9eXxAYzbAe2bwF+VIjAREakfhdzUeTXu7tUbjTFnAd8FPmyt/XKpghMRkfpQaIHAW4F1uNYH/55tRS0iIjKfeW/qNMb83SyHfw6sBDLZ89baD5YgNhERqRMLdRC4aI7jNudcCCjZiIjUEGNMJ67Z528Bx4DrrLWfneOxf4bb96Yd+BbwTmvtsUKuN2+ysdbOlWxERKS2fRqXA87AbYR5hzFmj7X2+7kPMsZcjOuvdjGwD9cO51O4TdXyVsjmaV3ARNRrJ3usBUjk2RFaREQW8Np3b98MvBc4G7eXzEdv//iWWfuXLVb0s/tNuI3VhoD7jDH/BvwxbqvpXJfg1uiz21H/DfBzY8zl2c0z81FIgcDXccUBuV4EaI9eKaqRvbs5cPP7eerTl3Pg5vczsnd3pUMSKYso0XwGOB1X/Xs68JnoeDGdC3jW2odzjt3HiT/j4XhRGADW2uzGbecUcsFCks2LgJk7dN4zR3AiizKydzdHdtzkdu5sbCUY7ufIjpuUcGSpeC8wDmRHDKPR1+8t8nVaces0uQY4vsvnzMcOzjg2OMdj51RIspkAmmYca6RIu7iJAAzu2g5xn1iiEc/ziCUaIe674yL172yOJ5qs0eh4MQ3jFvtzdQBDeT62fY7HzqmQZHMP8J4Zx/6KE0c7IgU7ctdtPPmxPyL1iwcJjh4gGDoydc7zkwQDfRWMTqRsngCaZxxrjo4X06NAaIxZm3PsPODBWR77IPAr2S+MMc/HNV1+rJALFrJ52lXAD40xW3Clz+cCzwXy2kI0Kp17O2467ivW2ktyzoW47J3dfO0/c89L7RrZu5vBXdsJBvrwO5fTsXHLCTtvHrnrNgZ/dNvxA2FIZmSAAPDbugmDcfzO5eUNXKQyPopbswH3M7EZSEbHi8ZaO2KM+U/g740xb8eNnP4YeMssD/8CcIsx5hZc0rsWuLWQ4gAorF3NQ8ALgK8AR3B7W79gxgLTfA7gth/9/Bzne6y1rdHHJfnGJfO7d88hrr7hbt5x3R1cfcPd3LvnUNmune/6y9BPb4cwhHD6jGxmdJDMZArSAR0bt5QtbpFKiarOrsBt37ws+nxFsavRIlfgfsE/iNut8xpr7feNMc8xxgwbY54DYK29A/eze0f02Azw54VerKBtoa21h4CPFXqR6LnbAIwx5wNnLeY1pDD37jnEjdvux/c92pp8+o+NceO2+2Hres5fu6Lk189dfwHwEo1kSDG4a/u00U1mYtQlG7zoIxrghiF+a9esoyGRehUlllIkl2miW1beNMvxp3BFAbnHPoW7t2bRCko2xphlwAXAcnI2SrPWfulkgojcaYyJAbuAd1tr9xXhNZe0bTsfx/c9GhvcX3Njg0+KgG07Hy9LsgkG+vAap/2bnWP9JSfB5H7txTjjrR8ocZQiUg6F3NR5EW6bgRBX8jaEy377gZNNNhcCP8HNT16L2ztnvbV2Mt8XePDB2da1puvt7V10gOVWjFj3H+ynscFjJJiYOhaGIfsPpor2Xsz3Oq3xJrzhQYgnjh9MTxImW6c9ryOWwEuPMz3hQBhLlCXOalMrsdZKnFCZWHt6esp+zWpWyMjmH4GPWGs/ZIzpt9Z2GWOuw83hnRRr7Q+jP04YY96Fq/9eh2v6mZd169aRTCbnPN/b21szf/nFinXlz+6m/9jY1MgGIDURsLK7qSivv1CcI50eR3bcBPE4np8kDMYhFtL9qrdOmxY7sOccxg89STg+6tZtvBhespnkilWsKUOc1aRWYq2VOKG2Yq1nhZQ+nwt8JPpzdgrtWk4shy6GcOGHyEK2blpDEISkJgLC0H0OgpCtm9aU5fotqzfQvflS/NYuwtQwfmsX3ZsvPWH9pWPjFuKNLfjLTiex4nn4y04n3tgyb1GAugyI1JZCRjbj0eMDoN8YcxruLtJT8nlytMOnD8SBuDGmEUjjklgCeAB30+i1uMq1hwqITWZx/toVsHU923Y+Tt/RUZYva2brpjVlWa/Jalm9YcHF/ZbVG2DzpQuWSGdlq9yI+9Oq3JglkYlIdSgk2dwDvBrXC+37wC3AGK6fTj7eh+scmvVW4Iu49Z4bcBVqI8CPgddYaydOeAUp2PlrV5Q1uSxWPkkpK98qNxGpHoUkm0txoxKAdwP/gGtvcGU+T7bWXgNcM8dpU0AcssTlX+UmItWikJs6nwHOMMbcAHwZt67yz9bagloWiJwsv3O5KzbIoS4DItUt72RjjPkD4C6gC7e+0gHsNMb8YYliE5lVx8YtkA7ITKYIw1BdBkRqQCHTaO8Htlprv5U9YIz5LeCTuPUbkbIotKBARKabr1flLI99E+7WlxXA3cDbrbW/LPSahSSb0zmxhcJ3gNMKvajIySqkoEBETpDtVflqTtw6ZkrUFfrfgDfgEs1HcP0xLyz0goUkm+8Br2J6wrk4Oi4iIkWw77o3nrAt9PP+5r+K2iutgF6VbwW+ba397+jx7wP6jDGrrbV7C7lmIcnmKeA/jTG3496AVcBrgc8bY/4u55v4YCEBiIiIEyWaz+Dua5zaFnrfdW+8otgJJ0/rgJ9lv7DWDhpjnoyOlyzZrMfda3Max6fO7iVnUx1chZqSjYjI4sy2LXT2eCWSzWxbQs+1ffS88k421tqLCn1xEREpyNm4EU2uUmwLna9Cto+eVyG90UREpLTKtS10vmZuCd2OS3wLt9mfoaD9bEREpKTKsi30XL0qZ9nW5Wbgp8aYV+L2Gvt74CeFFgeARjZSBdTBWcSJigBO2Ba6BMUB78P1trwKV3E2BvwrQLQl9MsBrLV7gHcANwFHgLXAHyzmghrZSEWpg7PIdFFiKWkxwHy9Kq21M7eEvg247WSvqZGNVFRuB2fP81wn57jvjotI3dDIpkzu3XOIbTsf59DRUVbkua/MYp5Ta9TBWWRp0MimDO7dc4gbt91P/7Ex2pp8+o+NceO2+7l3z6GiPqcWqYOzyNKgZFMG23Y+ju97NDb4eJ777Pse23Y+XtTn1CJ1cBZZGpRsyuDQ0VGSifi0Y8lEnL6jo3M8Y3HPqUUtqzfQvflS/NYuwtQwfmsX3SoOEKk7WrMpgxXLmuk/NkZjw/G3e3wyzfJlM+/dOrnn1Cp1cBapfxrZlMHWTWsIgpDUREAYus9BELJ105qiPkdEpFop2ZTB+WtXcNnW9XS1NzE8FtDV3sRlW9fPW1m2mOeIiFQrTaOVyflrVxScKBbzHBGRaqSRjYiIlJySjYiIlJym0aRmjOzdzeCu7QQDffidy+nYuEVVbCI1QiMbqQnZhp3BcP+0hp3qEC1SG5RspCaoYadIbVOykZoQDPTh+clpx9SwU6R2lG3NxhjzZ8DbgRcBX7HWXpJzbh1uc+L2Cz0AAByxSURBVJ71wD7gcmvtXeWKTaqf37ncTaElGqeOqWGnSO0o58jmAG5L0c/nHjTGJIDbga8BXcCHge3GmK4yxiZVTg07RWpb2ZKNtXabtfbruK1Fc20CmoCPWmvHrbW3AI8BW8sVm1Q/NewUqW1eGIZlvaAx5lrgrOw0mjHmSuC3rbUX5zzmRmDUWnvlQq/X29u7CniiNNGKiCxOT0+PV+kYqkk13GfTCgzOODYAdBfyIuvWrSOZTM55vre3l56ensKjq4BaiVVxFl+txForcUJtxVrPqqEabRhon3GsAxiqQCwiIlIC1ZBsHgReZIzJjeW86LiIiNSBcpY++9H14kDcGNMIpIGdQAp4tzHmelxhwLm46jQREakD5VyzeR/w/pyv3wp80Vp7iTHmdbj7bD6Iu8/m9dbao2WMrezu3XOIbTsf59DRUVYsa2brpjUl2U6gXNcREZlP2ZKNtfYa4Jo5zj0AvLhcsVTavXsOceO2+/F9j7Ymn/5jY9y47X4o8uZo5bqOiMhCqmHNZsnZtvNxfN+jscHH89xn3/fYtvPxmryOiMhClGwq4NDRUZKJ+LRjyUScvqOjNXkdEZGFKNlUwIplzYxPpqcdG59Ms3xZc01eR0RkIUo2FbB10xqCICQ1ERCG7nMQhGzdtKYmryMishAlmwo4f+0KLtu6nq72JobHArram7isBIv25bqOiMhCqqFdzZJ0/toVJ/zQz5Yp7z/Yz8qf3V2UMuXZrlMvVNYtUjs0sqkS2TLl/mNjNDZ4U2XK9+45VOnQqlLu+5Vb1q33S6Q6KdlUCZUpF0bvl0ht0TRalTh0dJS2pul/HaUoU66XqadyvV8iUhxKNrOoxA/kFcuaoym0438lxS5TrqeOAuV4v0SkeDSNNkOl1gLKUaZcT1NPKusWqS1KNjNU6gdybplyaiIsSZlyPXUUUFm3SG3RNNoMlVwLyJYpl2pnwUpOPZViarKey7pF6o1GNjPUc4uXSk09qUxZRJRsZqjntYBKTT3V01qRiCyOptFmOH/tCti6nm07H6fv6CjLa7g8eDaVmHpSmbKIKNnMQmsBxaUyZRHRNJqUXD1PTYpIfpRspORUpiwimkaTstDUpMjSpmQzi1LcE1KtPcmqNS4RqS+aRpuhFPeEVOt9JtUal4jUH41sZsi9JwSgscEnRcC2nY8v+jf+Yr1msUchpfheZxrZu5vBXdsJBvrwO5fTsXELLas3FOW1RaR2KNnMUIp7Qhb7mrnJpTnpMzCUorU5UbSOzaW+/2Vk726O7LgJ4j5eYyvBcL/7evOlSjgiS4ym0WYoRbuaxbzmzCmug8+OMDQ6STodFu0u/FK35hnctR3iPrFEI57nEUs0Qtx3x0VkSVGymaEU94Qs5jVntnhJZ0JiHgwMj0895mRHIaW+/yUY6MPzk9OOeX6SYKCvKK8vIrVDyWaGUtwTspjXnLkdQMKPEQKTQWbq2MmOQkp9/4vfuZwwGJ92LAzG8TuXF+X1RaR2VM2ajTFmJ/ASIIgOHbLWrq5ELIu5J2ShxftCX3Nmi5fOtiR9R0fx4x5hGDI+mS7KKGQx32t20b+9bz8H9qycc9G/Y+MWjuy4iQwpPD/pEk86oGPjlpOKWURqT9Ukm8hfWmv/pdJBFKoU2y1v3bSGG7fdT4qAZCJOPAbtLQ20tzYwPBYsukHoyVa05S76h37jvIv+Las3wOZLVY0mIlWXbGpSKUqIZ+s+/Y7XnVypczGSYu6iP5MjxBKNZEgxuGv7rEmkZfUGJRcRwQvDsNIxAFPTaOsAD7DA+6y1dy70vN7e3lXAEyUNbgGf2H6QxgYPz/OmjrkF95C/3HJ6BSOb7gvfO8zQWECDf3ypbiLI0Nbkc8lvnJrXa7T/4DOEfiPkfK+EIV6Q4tiFVxQ7ZJGa1dPT4y38qKWjmkY2fw08DEwAvwfcbow5z1r7WD5PXrduHclkcs7zpdpqGWDlz+4+oYV+aiJgZXfToq6ZjXUxU17zPeezO+6gsy05LSk2hyHDY0HecR7Ys5JguJ9YopHR0RGam1vITKbwl63knBK9vyerlH/3xVYrsdZKnFBbsdazqqlGs9b+1Fo7ZK0dt9Z+EbgL+J1Kx5WPUpQQL6aVzELPKcZ9NR0bt0A6IDOZgjB0n+dY9B/Zu5sDN7+fpz59OQdufj8je3fnfR0RqS9Vk2xmUR3ze3koRQnxYrZSXug5xUiKLas30L35UvzWLrwghd/aRfcsxQHZQoJguH9a9wAlHJGlqSqm0YwxncCLgR/gSp/fArwCuLIS8Sxm+qrYLfQX00pmoecUa8vr7KJ/b2/vnFNn0woJAG+BQgIRqW9VkWyABHAt8HwgDTwCvN5a+0i5AylFGfNiLGYr5XyeU659ZYKBPrzG1mnH1D1AZOmqimRjrT0MXFDpOKA8nZDzMfM+m3xu4lzMc0rF71zuptCikQ2oe4DIUlbNazYVMbNNDBS3E3K+FrMOVE3bL+cWEoQLFBKISP2ripFNNVnM9FWpLDTlNdfaUjXstKnuASKSS8lmhkpORWWTx/6D/az82d3zLt5Xy9rSfKqxe4C2wRapDE2jzVCpqajce2QaG7wF76tZTGn0UqdtsEUqRyObWVRiKio3eYwEEwsWJpR6l816VC3FHyJLkZJNkZzs9EyhySPftSVNGx2nBC1SOUo2RbCY9ZOZSaAp6daH8i1MyGdtqZrXdeZKgtm9ckpRVFBNxR8iS42STRHkMz2T+8O1KRnn2PAELU3+VBIYGXMtZIC8Wsnk0w1g287HmUynGRwJmAwyJPwYzY1+xaeN5kqCiZfG6Hr4vyDuT2txM9teOYtRTfchiSw1SjZFsND0zMwfrk/3DROkQxqTxxf3Afx4nLaWBvYfTLGyu2nBKa+F1paeeuYYw6OTpDMuiQXpNBOTGYL0sZP9lqcpdKpuruQ89LPb6eooXYubYrXrEZHCKdkUwULTMzN/uKYzIZ4HA0PjtDS6Y8lEnOGxgE+956KitUQfn0i7a4HbJSh01x4cnmDLe79BU9Jnyyuex++/6vmLvsZipurmSs7NYwN4/vR9dYrd4qZa7kMSWWpU+pyHe/cc4uob7uYd193B1TfcfUKp7ELdlGd2JUj4MTxgMshMHSvF2sFk+vjrE05vo+3H3J47t97xKF/97uJb0C2mBHuurQ5G/U7CYHzacbW4EakPSjYLyOfejPPXruCVF6ykf2iCJw4O0T80wSsvWDn1G/SKZc0MDI/zy8PDPHnwGEE6QzoTEo95Rdv/ZjYxzyMeA7wT92vwvBh+LAYebP/hvkVfYzHtfeZKzm2/9lq1uBGpU5pGW0A+i+z37jnEnffsp6utgdOWNTE+mebOe/Zzzsouzl+7ghet7ubhfUfAg7gH6RAyIWTCkCcODk1NZxVjeid3/STmwWTGzaDNJe7B2Hiw6OstpsJrrrWTX1m7gpHndKnFjUgdUrJZwFPPHGNkbBIPj5jnEQRuzSN3kX2harQH9h6hsy3JaMolrJgHoRcS8zyec3rrCclpsWaun4ym4oxPZk4Y1cRysk86hKbk4v8ZLLbCa661k2pscSMiJ0/TaAsI0iEhEIt5eJ5HLOYRRsezFppKOnR0lM7WBs48tYVVp7fhxz3iMS8qFCheq5mZ6yfpdIZYzE2n+fEYftxlmUwIYZghyGQghC2veN6ir1lNnaZFpHppZLOARDxGKpryinnuBzWhO5610FTSzPPZwoCEf/w1kok4Tz1zjKtvuDuvRpxwYsnx/meG6O5ITp2fDDL4MY9M6LHq9DYADh4ZYWQsYCIIicU8XnHeGSdVjQaq8BKRhWlks4CVp7XR2ZbEj8dIZ8CPx+hsS7LytLapx8y24D0yFnBsZJx3XHcHx0bGGRkLps67wgDobDueGAaGxxlNBXk34rx3zyE++R+7efQXRzk6OMajvzjK0Og4A8MTU49J+DHS4fGkNpIKSI2nSSZirD6znTNOaeaRJ/vViFJESk7JZgFbN60hEY/R3ZHkuae10t2RJBGPTVuTOH/tCp6/qosDz46y95fHOHB4hJGxCdLpDG1NPul0hjAM8ePuXprTTmmhsSHO0cExnjx4jP2HhhgcnqC9pSHvEuIvfPMhhkYnyWTcNFkmA2EI/UOpqaTW3OhDCM2NPmEYcnQwBcCy9kZ1ihaRstI0GidOR71odTcP7D0y9fXzV3Vxz54+xsaDWSvHvvrdR7jrvgN4HjT4HhNByEQQMjI6SWOnPzV91tbSwKfecxH37jnE9bf+3K0HhSHgkclkpk2rwfwlxAcOjxDz3FoSgBfduZkButqb6Ds6yhmntvHql7jvpe/oKJkw5NTORlqaEnldQ0SkWJZcspktsdx5z/6pCq4Dh4d4eJ+rHutsbeDA4WEe2vus+8HueQRBmm/d/cS0yrHtP9xHSEiYcdVdWYMjE3R3NgHTf6hv2/k4LU0+3R2NU4/df2iI/mPjtOYkgoHhCVITad5x3R2ztoGZWWUW4mL80OW/Pu3470efr77hbvqPjU07p0aUIlIOSyrZzNZa5bbvPUZ7S4LWZveDfzQVgOc+d7UlGRqdiKq3IOG76aqh0Um+8M2Hpn7wj6YmCUN3P4vnucdCVEwQyf2hPlu7lq62JIcHjk+B9Q+NMzA0Tmdbw1Ssn/yP3XS2NTI6HhCPeUxMZvC844ULYQhnLW+d8/vPLVM+x3uaXw166QiHaImfzsjeJpUci0jJLKlk427QzEy7QTOdzjAyFtAVrfdPBm7xIzUeRHf7R52YAc/z3HRVJuTA4ZGp1/U81wkge/dk1IbMPS8MGZ9MMzw6iR+P847r7mBkbJJ0OkNXToGA78c4a0Ur7S1J9h9MMZlJ09ToM5pKMzg8jOdBOp1hbDzNWctbSKfTTEymCcOQICpcaG1J8LbXvGDO7z97M+VPv3sHLxneCbEEre2dNGZGitpdWepXIVuXi+RaUslm/zNDDI1OEIu5Ni5BOkMmhImcPl2eB0H0ZczLTRvHpcOQMBNOTW/5MY+JTDg1osk1PBbQmIzjeR5BOh0VDKQZGHI9wDpbG6ZuhHzH617I+WtX0Nvbyye+cXharBNBhjB0MXueR1ebG4mlJjK0NiXy7mB8/toVnNH7GEGyfaq7MlDU7spSn3JnBnIrJqthfySpfksq2UymM4SE0cK8Syxe1DcsNeHugM9NGBM5jTKBac0j/fjxqTiAWAwyOQ9P+PD8Vafwoct/natvuJvh0QmODKamRlS+79E/NE7/0PisRQeT6QyZMCSTnp7Ecv+c8OMcG5kkzFnnyUcw0IfX6KbbRlOT9A+NE6TTtPQ/xYE9h/SDQ2ZV6Nbl9UA73RbPkko2YSYzLSFkf3A3+LGpCq50epbhySwymZBfPDNEwo8RMv11ASYD6G5302QzW96MT6Sn1nPiMY8gyPDtHz85reggzGRmHSllN1gbSQUc7h+blvRm+y1ztv9ZzuhcTjDcz1g6zuGBMTyggYD+sJWv6DdVmcNS21a7mne6rUVL6j4bLxabGs3A8ZFNIhHnQ5f/Ojf9zcWzTJrNLhMy1Sttco4+lvfscfuwzGx5k5tE/LhHJgw5NjLBF7/58LRYY7GcWKdOkPc9M7N1rP7kf+zmK788m8NHjnHk6CBhmCHhBfhehvsSPbrvRuY019YQ9VrNuJjtM2RuS2pkk4jHopX+nIMzWs/MJ9toMmsiyEwlg9mMjE1OXXdV/Gle07ib5fFBYrieN9mrhsBQJgmjIfuu+zSdwHWNQOPsr8sY0MrxSoRRGCbJfp6LOfoY+677JwC6gKsAAggDOMwytk/8Kj8Zfw6ndaxlQ6aXJGmIRmWvG7+dCc/nkaOrOHDzd4vSeXlk7251ca4TudWMpdwao1ostZFcqVVNsjHGdAKfA34LOAZcZ639bDGv0dHWwOnj+/idpl5Oiw0S944PMfZe+68AfKIrv9cKgSCMMRYmaPHGic+RdPZe+yWuSQDzLKt4QHvMFQzMNnU268VzPwOtjLOWR2cennaN5RzlnS3fcwfSzLr3QEMYsJ7HGXvSfT050MfYkw/OGkYnsPfbecTL7K81M+/neixYwQ0jm8lkjvdwu3DDynlvvu1uT067+faCtcs5cmx83sqpSs3J1+JawPlrV/DYBSvZ/sN9jKYCmhu9om2NcbJK8X4uZvsMmVs1TaN9Gpf8zgBeA3zAGHNRMS/Q/Owj/H7zjzg9Pj3RLIYH+F6G9tjciUbmN9/bdo5/iMuadwBufWzn7l/yD1/82dSU4IHDQ9x6x6McODxMW5PPEwcG2Ln7l4yNT+LHYGx8kp27f8kTBwbm7DWXz8Z4pVCp656s3H2bVnT6dLU1cOc9+ysed6nez4V24JXCVEWyMca0AG8C3metHbLW3gf8G/DHxbzOKxsfotEL8PJemZmfckxpnZs4NG2NbXwyMzV/nnvzred5jI65hTNXZRibGiGOjgVzzrdXak6+VtcCqjXuUsWl7TOKq1qm0c4FPGvtwznH7gNele8LPPjg7FM9ubpjQ8S9UEmiRnhwwpzgyKibL8/eGzUxGTAyOjpV3ReGkMmp5MseHxkdJQxD9h9M0dvbC8D+g/00NniMBMc7Zc98TCnMd104vaTXPhkz457tPa2GuODEv8fFxucBb/y1nAXU0afp7X06r+f29PQs6pr1qlqSTStunSbXANA2y2NntW7dOpLJ5Jzne3t7OZJpo9VLEfM0KqkFIRz/i4qSRkuzmy9vGBlmIsjQ4MdpaW4m1j9IJrp3KhaL4WXShOHxXUlbmptJTQSs7G6a+iGw8md3nzAnP/MxpTDfdaF6f0jlxj0yOjrre1rpuLJy4+rt7a3a93QpqYppNGAYaJ9xrAMYKuZF7ky9kFToU6yxTXEm42Quj06uIAyPF00kE7E5t09ojqqGXG+641WCzU3+nPPtlZqTr9W1gGqNu1rjkumqJdk8CoTGmLU5x84DFp4bK8BH/uF/89XRl3Ew3UE6PLmEk61GO5ZJkud9oEtWtLnprMfn8liwghtHNwPu/qRNG87kqrf92tT8+RmntvGWi8/ljFNbGR4LOPuMTjZtOJOmZIIgA03JBJs2nMnZZ3SSmghnnW+v1Jx8ra4F5MY913ta6bhq6f1carwwr1rb0jPG3AIkgbcDZwP/DbzFWvv9+Z7X29u7Cngin2m0WhlK10qsirP4aiXWWokTKhqrZutzVMvIBuAK3C+7B4EdwDULJRoREakN1VIggLV2AFf+LCIidaaaRjYiIlKnlGxERKTklGxERKTklGxERKTklGxERKTkqqYa7STEASYmJhZ6HOPj4yUPplhqJVbFWXy1EmutxAmVifXBBx9cBTzd09Mzx/aKS0vV3NS5WL29vS8D7qp0HCIiszi7p6fnyUoHUQ3qYWRzD/By3M2g6QUeKyJSTvm1iF4Can5kIyIi1U8FAiIiUnJKNiIiUnJKNiIiUnJKNiIiUnJKNiIiUnJKNiIiUnJKNiIiUnJKNiIiUnJKNiIiUnL10K5mijEmCXwW+E1gGbAP+Ftr7Tei8+uAm4D10bnLrbUV66tmjPk48GagA+gHPmetva4aY41iOgV4BHjcWvuS6FhVxWmM2Qm8BMg2PzxkrV0dnbsQ+AzwPOB+4B3W2ocqEWcUzxuBDwBnA88CV1prt1XTe2qMGZ5xqAn4rLX2z6PzVfOeGmOeg/v//6W4v/9vAX9mrR2upvd0qaq3kY0P7AcuxP0Avwr4ijHmXGNMArgd+BrQBXwY2G6M6apUsMC/As+31rbj/gf5A2PMm6s0VoCPAg9nv6jiOP/SWtsafWQTTTewHRdjFy7m7caYivzCZYx5JfAJ4E+BNuB84L5qe09z3sdW4DRgDLgNqu89Bf4F90vbmcDzcUn8b6vtPV2q6irZWGtHrLXXWGuftNZmrLXfBh4FLgA24X4r+6i1dtxaewvwGLC1gvE+Yq0dyTmUAdZQhbFGv8GeA/x7zuFNVFmc89gKPGqtvcVaO45LnM24X0wq4YPAB621P4r+rR621u6jut/TNwJ9HO+yXm3v6dnAV621Y9bao8A2YB3V/Z4uGXWVbGYyxpwKrAUewv2je8Bam8l5yH3R8YoxxlwVTVU8DbQCN1NlsRpjGoBPA1cAuZ1bqyrOHNcaY44YY34cjSDAxfQ/2QdEMT9ABWI1xsSBXwOWGWMeNcYcMMb8uzGmg+p9TwHeBnzJWpv9N1A172nkE7jZgZbo//3fBb5Ndb+nS0bdJptoKH8zcKu19j7cD/LBGQ8bwE1hVIy19h+iGDYAX8JNA1RbrFcB/22t/Z8Zx6stToC/xv2GewZwI3C7MeYcqivWFUAC+D3glcALgFNwPyyrKc4pxpjn4kYsX8w5XG2x/gg3fTaIG4ENADdQfXEuSXWZbIwxMeDL0Zd/En0eBtpnPLQDGCpXXHOx1obW2p/j5sM/QBXFaoxZA1wCvH+W01UTZ5a19qfW2qFouuSLuCmf36G6Yh2NPn/aWvu0tXYAuJbqizPX/wJ+ZK19IudY1cQajRZ34NZmWoBuYBL4JFUU51JWd8nGGOMBn8f9ZvsGa212v+gHgRdFiSjrvOh4tfCB1VRXrC/DLQw/aox5Bvc/74bozw9TPXHOJTvl8yDwK9mD0b+T9VQg1ii57Gf6lGRWNf3d5/ojpo9qoIreU9zC/1m4BD4erdn8G7CZ6n1Pl5S6Kn2O3IBbp7nYWjuac3wnkALebYy5Hrc4eC6uQqXsogqZS3CVPcdwRQxX4CplqinWW3G/MWa9BfeD5zXAEaonTowxncCLgR/gSl/fArwCuBI4DHzUGPP7uIXjv8CNMH5QiVhxZbh/Zoz5FjACXA18g+r6uwfAGPNSXIXXbTNObaNK3lNr7bPGmH3AnxpjPoIrVLgEV469kyp7T5eiuhrZRPPKl+F+azlojBmOPq621k4Cr8MtGg4A7wNeH/0GVAlhFMs+XLL5MnA98KlqijWq7Hkm+4Gb+56Mvq6aOCMJ3HTUYdx9K38exfOItfYI8PooxgFczFustcFcL1ZiH8KtMTwM7AWO4u6zqbb3FFxhwDZr7bRppyp8T98A/AZuvWYv4OHus6nG93TJ0bbQIiJScnU1shERkeqkZCMiIiWnZCMiIiWnZCMiIiWnZCMiIiWnZCMiIiWnZCMiIiWnZCMiIiWnZCMiIiVXj73RRKYxxlwB/G/gObjWQF8H3mutHTXGtOLaBG3B9c/6OK7H1zXW2i9Ez38+8DFc/7oAtzvle2dsfCci89DIRpaCg7hk0o7rnfUq4G+ic/8MvBB4EW4nUoNrOgmAMeYU3DYF38Ulq1+JHveJMsUuUhfUG02WHGPMlbiNyzbiuhRvsdZ+JzrXitvA7p3W2i8YY/4Kt1XFy3Oe/2Lgh0CztTZd9m9ApAZpGk3qnjHmd4H3AGtwnaETwCHgVCAJPJl9rLV22BhzJOfp5wAvNsYM5BzzcF27TwN+WdLgReqEko3UNWPMWbg9ed4C3G6tHY9GNu/CbUUwDqwCbPT47C6PWc8AO621rypn3CL1RslG6l0bbm3y2SjRrMdtUoe1NmOMuRn4gDHmftxePR+b8fx/B95ljPlT4Eu4rbvPAnqstV8v1zchUutUICB1zVq7B7dZ1q3GmGO4ZPKlnIdcCTwCPAQ8Fn304SrTsNY+BbwUeDVuQ64B4Du4ggIRyZMKBERyGGM6cLtmvsxau6vS8YjUC02jyZJmjFkFrAR+DHTg7rl5HLingmGJ1B1No8lSlwQ+i5seewxXHPBaa21Q0ahE6oym0UREpOQ0shERkZJTshERkZJTshERkZJTshERkZJTshERkZL7/1gPIrPrjVVwAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Now let's try the random forest with the new dataset \n",
        "#Create labels\n",
        "labels = np.array(df['cancer'])\n",
        "df.drop('cancer', axis = 1, inplace = True)\n",
        "\n",
        "#Create dummies/one hot encoding\n",
        "df_dum = pd.get_dummies(df)\n",
        "df_list_dum = list(df_dum.columns)\n",
        "\n",
        "# Split the data into training and testing sets\n",
        "train_df, test_df, train_labels, test_labels = train_test_split(df_dum, labels, test_size = 0.25, random_state = 42)\n",
        "print('Training Features Shape:', train_df.shape)\n",
        "print('Training Labels Shape:', train_labels.shape)\n",
        "print('Testing Features Shape:', test_df.shape)\n",
        "print('Testing Labels Shape:', test_labels.shape)"
      ],
      "metadata": {
        "id": "z_UTZYQQubGY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e5cf01ea-1e29-4276-c203-8e8fc158b713"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Features Shape: (925, 8)\n",
            "Training Labels Shape: (925,)\n",
            "Testing Features Shape: (309, 8)\n",
            "Testing Labels Shape: (309,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model on training data\n",
        "rf = RandomForestClassifier(n_estimators = 1000, random_state = 42)\n",
        "\n",
        "rf_model = rf.fit(train_df, train_labels)\n",
        "\n",
        "pred_train = rf_model.predict(train_df)\n",
        "pred_test = rf_model.predict(test_df)\n",
        "\n",
        "print(\"Accuracy train: \", metrics.accuracy_score(train_labels, pred_train))\n",
        "print(\"Accuracy test: \", metrics.accuracy_score(test_labels, pred_test))"
      ],
      "metadata": {
        "id": "Qy6iITg8ubLU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b403604a-43bd-4348-fc61-c57559bed1ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy train:  1.0\n",
            "Accuracy test:  0.9902912621359223\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_imp = pd.Series(rf.feature_importances_,index=df_dum.columns).sort_values(ascending=False)\n",
        "feature_imp"
      ],
      "metadata": {
        "id": "VYYvS4tDubOw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d1e1ebc-edc0-4b5c-a069-13baefe309f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "first_sex           0.261813\n",
              "age                 0.154283\n",
              "num_preg            0.136172\n",
              "num_sex_partners    0.132834\n",
              "iud_years           0.124037\n",
              "contracept_years    0.115064\n",
              "stds_num            0.052130\n",
              "pack_years          0.023666\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%matplotlib inline\n",
        "# Creating a bar plot\n",
        "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
        "# Add labels to your graph\n",
        "plt.xlabel('Feature Importance Score')\n",
        "plt.ylabel('Features')\n",
        "plt.title(\"Visualizing Important Features\")\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "6sojYOWYuoKk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "outputId": "fe7e173f-33ce-4d7f-fe5b-da512255e943"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:matplotlib.legend:No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Confusion matrix\n",
        "\n",
        "print(confusion_matrix(test_labels, pred_test))\n",
        "color='black'\n",
        "matrix = plot_confusion_matrix(rf, test_df, test_labels, cmap=plt.cm.Blues)\n",
        "matrix.ax_.set_title('Confusion Matrix: Random Forest', color=color)\n",
        "plt.grid(b=None)\n",
        "plt.xlabel('Predicted Label', color=color)\n",
        "plt.ylabel('True Label', color=color)\n",
        "plt.gcf().axes[0].tick_params(colors=color)\n",
        "plt.gcf().axes[1].tick_params(colors=color)\n",
        "plt.show()\n",
        "\n",
        "#Classification report \n",
        "print(classification_report(test_labels, pred_test))"
      ],
      "metadata": {
        "id": "6M2lLeSzuoNb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        },
        "outputId": "5da104a7-8482-441c-d2b6-83ea837ad8f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[163   0]\n",
            " [  3 143]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      1.00      0.99       163\n",
            "         1.0       1.00      0.98      0.99       146\n",
            "\n",
            "    accuracy                           0.99       309\n",
            "   macro avg       0.99      0.99      0.99       309\n",
            "weighted avg       0.99      0.99      0.99       309\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Getting metrics \n",
        "y_pred_rf = rf.predict(test_df)\n",
        "train_pred_rf = rf.predict(train_df)\n",
        "print(\"Train Accuracy:\",metrics.accuracy_score(train_labels, train_pred_rf))\n",
        "print(\"Test Accuracy:\",metrics.accuracy_score(test_labels, y_pred_rf))\n",
        "print(\"Precision:\",metrics.precision_score(test_labels, y_pred_rf))\n",
        "print(\"Recall:\",metrics.recall_score(test_labels, y_pred_rf))\n",
        "\n",
        "#Justification for not using this model? "
      ],
      "metadata": {
        "id": "BLlxIDPSuoQB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44e92437-d96f-4eab-cd19-8a3f7ce8d0e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 1.0\n",
            "Test Accuracy: 0.9902912621359223\n",
            "Precision: 1.0\n",
            "Recall: 0.9794520547945206\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SVM\n",
        "svm_clf = svm.SVC(kernel = 'rbf', gamma = 0.1)\n",
        "svm_clf.fit(train_df, train_labels)\n",
        "y_pred_svm = svm_clf.predict(test_df)\n",
        "train_pred_svm = svm_clf.predict(train_df)\n",
        "print(\"Train Accuracy:\",metrics.accuracy_score(train_labels, train_pred_svm))\n",
        "print(\"Test Accuracy:\",metrics.accuracy_score(test_labels, y_pred_svm))\n",
        "print(\"Precision:\",metrics.precision_score(test_labels, y_pred_svm))\n",
        "print(\"Recall:\",metrics.recall_score(test_labels, y_pred_svm))\n",
        "\n",
        "#Classification report SVM \n",
        "print(classification_report(test_labels, y_pred_svm))\n",
        "\n",
        "#Confusion matrix\n",
        "print(confusion_matrix(test_labels, y_pred_svm))\n",
        "color='black'\n",
        "matrix = plot_confusion_matrix(svm_clf, test_df, test_labels, cmap=plt.cm.Blues)\n",
        "matrix.ax_.set_title('Confusion Matrix: SVM', color=color)\n",
        "plt.grid(b=None)\n",
        "plt.xlabel('Predicted Label', color=color)\n",
        "plt.ylabel('True Label', color=color)\n",
        "plt.gcf().axes[0].tick_params(colors=color)\n",
        "plt.gcf().axes[1].tick_params(colors=color)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "zWLWejRKuoSq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "outputId": "5324aeb6-ec30-4f5d-f6e0-8f1f9e30c89b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.9837837837837838\n",
            "Test Accuracy: 0.9546925566343042\n",
            "Precision: 0.9459459459459459\n",
            "Recall: 0.958904109589041\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.96      0.95      0.96       163\n",
            "         1.0       0.95      0.96      0.95       146\n",
            "\n",
            "    accuracy                           0.95       309\n",
            "   macro avg       0.95      0.95      0.95       309\n",
            "weighted avg       0.95      0.95      0.95       309\n",
            "\n",
            "[[155   8]\n",
            " [  6 140]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#KNN\n",
        "knn = KNeighborsClassifier(n_neighbors=3)\n",
        "knn.fit(train_df, train_labels)\n",
        "y_pred_knn = knn.predict(test_df)\n",
        "train_pred_knn = knn.predict(train_df)\n",
        "print(\"Train Accuracy:\",metrics.accuracy_score(train_labels, train_pred_knn))\n",
        "print(\"Test Accuracy:\",metrics.accuracy_score(test_labels, y_pred_knn))\n",
        "print(\"Precision:\",metrics.precision_score(test_labels, y_pred_knn))\n",
        "print(\"Recall:\",metrics.recall_score(test_labels, y_pred_knn))\n",
        "\n",
        "#Classification report KNN \n",
        "print(classification_report(test_labels, y_pred_knn))\n",
        "\n",
        "#Confusion matrix\n",
        "print(confusion_matrix(test_labels, y_pred_knn))\n",
        "color='black'\n",
        "matrix = plot_confusion_matrix(knn, test_df, test_labels, cmap=plt.cm.Blues)\n",
        "matrix.ax_.set_title('Confusion Matrix: KNN', color=color)\n",
        "plt.grid(b=None)\n",
        "plt.xlabel('Predicted Label', color=color)\n",
        "plt.ylabel('True Label', color=color)\n",
        "plt.gcf().axes[0].tick_params(colors=color)\n",
        "plt.gcf().axes[1].tick_params(colors=color)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "O0urmmytb0Nl",
        "outputId": "c3394523-91da-4645-b619-8ae16c843dd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.9589189189189189\n",
            "Test Accuracy: 0.9158576051779935\n",
            "Precision: 0.8614457831325302\n",
            "Recall: 0.9794520547945206\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      0.86      0.92       163\n",
            "         1.0       0.86      0.98      0.92       146\n",
            "\n",
            "    accuracy                           0.92       309\n",
            "   macro avg       0.92      0.92      0.92       309\n",
            "weighted avg       0.92      0.92      0.92       309\n",
            "\n",
            "[[140  23]\n",
            " [  3 143]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Logistic Regression\n",
        "lr = LogisticRegression(penalty = 'l2')\n",
        "lr.fit(train_df, train_labels)\n",
        "y_pred_lr = lr.predict(test_df)\n",
        "train_pred_lr = lr.predict(train_df)\n",
        "print(\"Train Accuracy:\",metrics.accuracy_score(train_labels, train_pred_lr))\n",
        "print(\"Test Accuracy:\",metrics.accuracy_score(test_labels, y_pred_lr))\n",
        "print(\"Precision:\",metrics.precision_score(test_labels, y_pred_lr))\n",
        "print(\"Recall:\",metrics.recall_score(test_labels, y_pred_lr))\n",
        "\n",
        "#Classification report SVM \n",
        "print(classification_report(test_labels, y_pred_lr))\n",
        "\n",
        "#Confusion matrix\n",
        "print(confusion_matrix(test_labels, y_pred_lr))\n",
        "color='black'\n",
        "matrix = plot_confusion_matrix(lr, test_df, test_labels, cmap=plt.cm.Blues)\n",
        "matrix.ax_.set_title('Confusion Matrix: LR', color=color)\n",
        "plt.grid(b=None)\n",
        "plt.xlabel('Predicted Label', color=color)\n",
        "plt.ylabel('True Label', color=color)\n",
        "plt.gcf().axes[0].tick_params(colors=color)\n",
        "plt.gcf().axes[1].tick_params(colors=color)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "2KLf5mbsb3xk",
        "outputId": "83a7d118-5090-46b8-f41c-e7ed1a8dd19d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.7156756756756757\n",
            "Test Accuracy: 0.7637540453074434\n",
            "Precision: 0.7482993197278912\n",
            "Recall: 0.7534246575342466\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.78      0.77      0.78       163\n",
            "         1.0       0.75      0.75      0.75       146\n",
            "\n",
            "    accuracy                           0.76       309\n",
            "   macro avg       0.76      0.76      0.76       309\n",
            "weighted avg       0.76      0.76      0.76       309\n",
            "\n",
            "[[126  37]\n",
            " [ 36 110]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Neural network - simple MLP \n",
        "nn_model = Sequential([\n",
        "    Dense(input_shape=[8], units=32, activation = tf.nn.relu,kernel_regularizer=tf.keras.regularizers.L2(0.0001)),\n",
        "    Dense(units=16, activation = tf.nn.relu,kernel_regularizer=tf.keras.regularizers.L2(0.0001)),\n",
        "    Dense(name = \"output_layer\", units = 1, activation = tf.nn.sigmoid)\n",
        "])"
      ],
      "metadata": {
        "id": "iTncjmYkb7E1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nn_model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2FUc555cTg4",
        "outputId": "87d23f56-0369-4f52-8163-aa6560ca2acd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense (Dense)               (None, 32)                288       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 833\n",
            "Trainable params: 833\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nn_model.compile(optimizer='adam',           \n",
        "               loss = 'binary_crossentropy',\n",
        "               metrics=['accuracy'])\n",
        "#There are many optimizers to use -- also part of hyperparameter tuning"
      ],
      "metadata": {
        "id": "mIgJLCy2cWv6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.config.run_functions_eagerly(True)\n",
        "\n",
        "nn_history = nn_model.fit(\n",
        "    train_df\n",
        "    ,train_labels\n",
        "    ,epochs = 100\n",
        "    ,batch_size = 20\n",
        "    ,validation_split=0.20 \n",
        "    ,callbacks=[tf.keras.callbacks.ModelCheckpoint(\"Risk_model_1.h5\",save_best_only=True,save_weights_only=False)\n",
        "                ,tf.keras.callbacks.EarlyStopping(monitor='val_accuracy', patience=2)]\n",
        "    )\n",
        "#The callbacks is a checkpoint to save the model object as a pretrained model so you can use it\n",
        "#Early stopping will stop training when the monitor value (val_accuracy) fails to improve over\n",
        "#the last couple of epochs \n",
        "#Training will terminate early with early stopping--prevents overfitting"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X2F1HdfRcZsi",
        "outputId": "b9859113-bb57-4795-d230-e09b94df7d95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n",
            "Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "36/37 [============================>.] - ETA: 0s - loss: 0.7790 - accuracy: 0.5417"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r37/37 [==============================] - 3s 87ms/step - loss: 0.7755 - accuracy: 0.5419 - val_loss: 0.6871 - val_accuracy: 0.5135\n",
            "Epoch 2/100\n",
            "37/37 [==============================] - 1s 39ms/step - loss: 0.6697 - accuracy: 0.5743 - val_loss: 0.6301 - val_accuracy: 0.6162\n",
            "Epoch 3/100\n",
            "37/37 [==============================] - 1s 39ms/step - loss: 0.6482 - accuracy: 0.6243 - val_loss: 0.5973 - val_accuracy: 0.7568\n",
            "Epoch 4/100\n",
            "37/37 [==============================] - 2s 44ms/step - loss: 0.5946 - accuracy: 0.7027 - val_loss: 0.5399 - val_accuracy: 0.7892\n",
            "Epoch 5/100\n",
            "37/37 [==============================] - 2s 51ms/step - loss: 0.5732 - accuracy: 0.7243 - val_loss: 0.5482 - val_accuracy: 0.6811\n",
            "Epoch 6/100\n",
            "37/37 [==============================] - 2s 53ms/step - loss: 0.5606 - accuracy: 0.7149 - val_loss: 0.5265 - val_accuracy: 0.7676\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Test accuracy\n",
        "print(f\"Test acc: {nn_model.evaluate(test_df, test_labels)[1]:.3f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_EHTQVKWchkT",
        "outputId": "19f0ba67-c754-4a49-fa24-a99a18994a16"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10/10 [==============================] - 0s 13ms/step - loss: 0.5052 - accuracy: 0.8058\n",
            "Test acc: 0.806\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Even though the `tf.config.experimental_run_functions_eagerly` option is set, this option does not apply to tf.data functions. To force eager execution of tf.data functions, please use `tf.data.experimental.enable_debug_mode()`.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Ensemble model - max voting classifier\n",
        "\n",
        "#We will use RF, KNN, SVM, LR\n",
        "\n",
        "lr_ensemble = LogisticRegression(penalty = 'l2')\n",
        "svm_ensemble = svm.SVC(kernel = 'rbf', gamma = 0.1,probability=True)\n",
        "knn_ensemble = KNeighborsClassifier(n_neighbors=3)\n",
        "rf_ensemble = RandomForestClassifier(n_estimators = 1000, random_state = 42)\n",
        "\n",
        "ensemble_model = VotingClassifier(\n",
        "    estimators=[('lr', lr_ensemble), ('svm', svm_ensemble), ('rf', rf_ensemble), ('knn', knn_ensemble)], voting='soft')\n",
        "\n",
        "ensemble_model.fit(train_df, train_labels)\n",
        "pred_ensemble = ensemble_model.predict(test_df)\n",
        "train_pred_ensemble = ensemble_model.predict(train_df)\n",
        "print(\"Train Accuracy:\",metrics.accuracy_score(train_labels, train_pred_ensemble))\n",
        "print(\"Test Accuracy:\",metrics.accuracy_score(test_labels, pred_ensemble))\n",
        "print(\"Precision:\",metrics.precision_score(test_labels, pred_ensemble))\n",
        "print(\"Recall:\",metrics.recall_score(test_labels, pred_ensemble))\n",
        "\n",
        "#Classification report \n",
        "print(classification_report(test_labels, pred_ensemble))\n",
        "\n",
        "#Confusion matrix\n",
        "print(confusion_matrix(test_labels, pred_ensemble))\n",
        "color='black'\n",
        "matrix = plot_confusion_matrix(ensemble_model, test_df, test_labels, cmap=plt.cm.Blues)\n",
        "matrix.ax_.set_title('Confusion Matrix: Ensemble Model', color=color)\n",
        "plt.grid(b=None)\n",
        "plt.xlabel('Predicted Label', color=color)\n",
        "plt.ylabel('True Label', color=color)\n",
        "plt.gcf().axes[0].tick_params(colors=color)\n",
        "plt.gcf().axes[1].tick_params(colors=color)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "YLjz9wY-d54W",
        "outputId": "36431393-d2f6-4542-8531-2f95565bef75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train Accuracy: 0.9967567567567568\n",
            "Test Accuracy: 0.9644012944983819\n",
            "Precision: 0.9470198675496688\n",
            "Recall: 0.9794520547945206\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.98      0.95      0.97       163\n",
            "         1.0       0.95      0.98      0.96       146\n",
            "\n",
            "    accuracy                           0.96       309\n",
            "   macro avg       0.96      0.97      0.96       309\n",
            "weighted avg       0.96      0.96      0.96       309\n",
            "\n",
            "[[155   8]\n",
            " [  3 143]]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Saving this model \n",
        "\n",
        "filename = 'final_risk_model.pkl'\n",
        "\n",
        "with open(filename, 'wb') as file:  \n",
        "    pickle.dump(ensemble_model, file)\n",
        "\n",
        "# some time later...\n",
        " \n",
        "# load the model from disk\n",
        "#with open(filename, 'rb') as file:  \n",
        "#    ensemble_model = pickle.load(file)\n",
        "\n",
        "#ensemble_model\n",
        "#result = loaded_model.score(test_df, test_labels)\n",
        "#print(result)\n",
        "\n",
        "# Load the Model back from file\n"
      ],
      "metadata": {
        "id": "ZED6txe2naql"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Shap values for the ensemble model (can do this for our final model of choice)\n",
        "# Fits the explainer\n",
        "explainer = shap.Explainer(ensemble_model.predict, test_df)\n",
        "# Calculates the SHAP values - It takes some time\n",
        "shap_values = explainer(test_df)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jxh_Zct0eU0e",
        "outputId": "0b0f8ad1-fdd0-4476-cdd6-86d035fbd9a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "Compilation is falling back to object mode WITH looplifting enabled because Function \"_delta_masking\" failed type inference due to: Invalid use of type(CPUDispatcher(<function _single_delta_mask at 0x7f2aa959b8b0>)) with parameters (int64, array(float16, 2d, C), array(bool, 1d, C), array(float16, 2d, C), array(float16, 1d, A), int64)\n",
            "\n",
            "During: resolving callee type: type(CPUDispatcher(<function _single_delta_mask at 0x7f2aa959b8b0>))\n",
            "During: typing of call at /usr/local/lib/python3.8/dist-packages/shap/maskers/_tabular.py (217)\n",
            "\n",
            "\n",
            "File \"../usr/local/lib/python3.8/dist-packages/shap/maskers/_tabular.py\", line 217:\n",
            "def _delta_masking(masks, x, curr_delta_inds, varying_rows_out,\n",
            "    <source elided>\n",
            "            curr_delta_inds[dpos] = -curr_delta_inds[dpos] - 1 # -value + 1 is the original index that needs flipped\n",
            "            _single_delta_mask(curr_delta_inds[dpos], masked_inputs_tmp, last_mask, data, x, noop_code)\n",
            "            ^\n",
            "\n",
            "Function \"_delta_masking\" was compiled in object mode without forceobj=True.\n",
            "\n",
            "File \"../usr/local/lib/python3.8/dist-packages/shap/maskers/_tabular.py\", line 197:\n",
            "@jit\n",
            "def _delta_masking(masks, x, curr_delta_inds, varying_rows_out,\n",
            "^\n",
            "\n",
            "\n",
            "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
            "\n",
            "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
            "\n",
            "File \"../usr/local/lib/python3.8/dist-packages/shap/maskers/_tabular.py\", line 197:\n",
            "@jit\n",
            "def _delta_masking(masks, x, curr_delta_inds, varying_rows_out,\n",
            "^\n",
            "\n",
            "\n",
            "Compilation is falling back to object mode WITH looplifting enabled because Function _build_fixed_single_output failed at nopython mode lowering due to: float16\n",
            "\n",
            "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"_build_fixed_single_output\" failed type inference due to: Cannot determine Numba type of <class 'numba.core.dispatcher.LiftedLoop'>\n",
            "\n",
            "File \"../usr/local/lib/python3.8/dist-packages/shap/utils/_masked_model.py\", line 371:\n",
            "def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):\n",
            "    <source elided>\n",
            "    #     averaged_outs[0] = link(np.mean(last_outs))\n",
            "    for i in range(0, len(averaged_outs)):\n",
            "    ^\n",
            "\n",
            "Function \"_build_fixed_single_output\" was compiled in object mode without forceobj=True, but has lifted loops.\n",
            "\n",
            "File \"../usr/local/lib/python3.8/dist-packages/shap/utils/_masked_model.py\", line 366:\n",
            "def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):\n",
            "    <source elided>\n",
            "    # to carry over evaluation outputs\n",
            "    sample_count = last_outs.shape[0]\n",
            "    ^\n",
            "\n",
            "\n",
            "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
            "\n",
            "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
            "\n",
            "File \"../usr/local/lib/python3.8/dist-packages/shap/utils/_masked_model.py\", line 366:\n",
            "def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):\n",
            "    <source elided>\n",
            "    # to carry over evaluation outputs\n",
            "    sample_count = last_outs.shape[0]\n",
            "    ^\n",
            "\n",
            "\n",
            "Compilation is falling back to object mode WITHOUT looplifting enabled because Function \"_build_fixed_single_output\" failed type inference due to: No implementation of function Function(<built-in function mul>) found for signature:\n",
            " \n",
            " >>> mul(none, array(float64, 1d, C))\n",
            " \n",
            "There are 14 candidate implementations:\n",
            "  - Of which 12 did not match due to:\n",
            "  Overload of function 'mul': File: <numerous>: Line N/A.\n",
            "    With argument(s): '(none, array(float64, 1d, C))':\n",
            "   No match.\n",
            "  - Of which 2 did not match due to:\n",
            "  Operator Overload in function 'mul': File: unknown: Line unknown.\n",
            "    With argument(s): '(none, array(float64, 1d, C))':\n",
            "   No match for registered cases:\n",
            "    * (int64, int64) -> int64\n",
            "    * (int64, uint64) -> int64\n",
            "    * (uint64, int64) -> int64\n",
            "    * (uint64, uint64) -> uint64\n",
            "    * (float32, float32) -> float32\n",
            "    * (float64, float64) -> float64\n",
            "    * (complex64, complex64) -> complex64\n",
            "    * (complex128, complex128) -> complex128\n",
            "\n",
            "During: typing of intrinsic-call at /usr/local/lib/python3.8/dist-packages/shap/utils/_masked_model.py (378)\n",
            "\n",
            "File \"../usr/local/lib/python3.8/dist-packages/shap/utils/_masked_model.py\", line 378:\n",
            "def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):\n",
            "    <source elided>\n",
            "            if linearizing_weights is not None:\n",
            "                averaged_outs[i] = np.mean(linearizing_weights * link(last_outs))\n",
            "                ^\n",
            "\n",
            "Function \"_build_fixed_single_output\" was compiled in object mode without forceobj=True.\n",
            "\n",
            "File \"../usr/local/lib/python3.8/dist-packages/shap/utils/_masked_model.py\", line 371:\n",
            "def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):\n",
            "    <source elided>\n",
            "    #     averaged_outs[0] = link(np.mean(last_outs))\n",
            "    for i in range(0, len(averaged_outs)):\n",
            "    ^\n",
            "\n",
            "\n",
            "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
            "\n",
            "For more information visit https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
            "\n",
            "File \"../usr/local/lib/python3.8/dist-packages/shap/utils/_masked_model.py\", line 371:\n",
            "def _build_fixed_single_output(averaged_outs, last_outs, outputs, batch_positions, varying_rows, num_varying_rows, link, linearizing_weights):\n",
            "    <source elided>\n",
            "    #     averaged_outs[0] = link(np.mean(last_outs))\n",
            "    for i in range(0, len(averaged_outs)):\n",
            "    ^\n",
            "\n",
            "Exact explainer: 310it [11:26,  2.24s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def print_feature_importances_shap_values(shap_values, features):\n",
        "    '''\n",
        "    Prints the feature importances based on SHAP values in an ordered way\n",
        "    shap_values -> The SHAP values calculated from a shap.Explainer object\n",
        "    features -> The name of the features, on the order presented to the explainer\n",
        "    '''\n",
        "    # Calculates the feature importance (mean absolute shap value) for each feature\n",
        "    importances = []\n",
        "    for i in range(shap_values.values.shape[1]):\n",
        "        importances.append(np.mean(np.abs(shap_values.values[:, i])))\n",
        "    # Calculates the normalized version\n",
        "    importances_norm = softmax(importances)\n",
        "    # Organize the importances and columns in a dictionary\n",
        "    feature_importances = {fea: imp for imp, fea in zip(importances, features)}\n",
        "    feature_importances_norm = {fea: imp for imp, fea in zip(importances_norm, features)}\n",
        "    # Sorts the dictionary\n",
        "    feature_importances = {k: v for k, v in sorted(feature_importances.items(), key=lambda item: item[1], reverse = True)}\n",
        "    feature_importances_norm= {k: v for k, v in sorted(feature_importances_norm.items(), key=lambda item: item[1], reverse = True)}\n",
        "    # Prints the feature importances\n",
        "    for k, v in feature_importances.items():\n",
        "        print(f\"{k} -> {v:.4f} (softmax = {feature_importances_norm[k]:.4f})\")"
      ],
      "metadata": {
        "id": "BGwWmcwBg-EP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print_feature_importances_shap_values(shap_values, df.columns)"
      ],
      "metadata": {
        "id": "5Ixqo6fehBXQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a5e69d5b-b315-43e5-e728-1593cea13fbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iud_years -> 0.1463 (softmax = 0.1340)\n",
            "age -> 0.1210 (softmax = 0.1307)\n",
            "first_sex -> 0.1188 (softmax = 0.1304)\n",
            "contracept_years -> 0.0767 (softmax = 0.1250)\n",
            "num_preg -> 0.0507 (softmax = 0.1218)\n",
            "num_sex_partners -> 0.0488 (softmax = 0.1216)\n",
            "pack_years -> 0.0316 (softmax = 0.1195)\n",
            "stds_num -> 0.0103 (softmax = 0.1170)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.plots.bar(shap_values)"
      ],
      "metadata": {
        "id": "QIt56p4nhIbS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "outputId": "6e507d04-ab1e-43c1-a5b4-e999e8bed656"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, plot_type='violin')"
      ],
      "metadata": {
        "id": "6mwO2byuhPuN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "outputId": "98e3ff1b-82bc-4da2-ec02-408d6e2abbfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x338.4 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "id": "GaW8WkmOjs-W",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a544ff5b-34ef-4162-c89f-ca7c9a185d35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['age', 'num_sex_partners', 'first_sex', 'num_preg', 'pack_years',\n",
              "       'contracept_years', 'iud_years', 'stds_num'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_prob = ensemble_model.predict_proba(pd.DataFrame([[25, 1, 20, 1, 10, 10, 10, 1]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hzVy86S_tu5G",
        "outputId": "64de4fd1-6c98-4577-f953-0df9d1df3088"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "X does not have valid feature names, but SVC was fitted with feature names\n",
            "X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n",
            "X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prediction_prob[:,1]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZcYRhariuR_6",
        "outputId": "66010cce-6c15-4f60-9cd3-2bbaf6c0692b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.29172091])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ensemble_model.classes_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JIB4MBuAufCY",
        "outputId": "c341b336-a2d9-47e7-d408-1ffe9b8273a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1.], dtype=float16)"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the prediction function\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "def predict(model, age,num_sex_partners,first_sex,num_preg,pack_years,contracept_years,iud_years,stds_num):\n",
        "    #Predicting the price of the carat\n",
        "    prediction_prob = model.predict_proba(pd.DataFrame([[age,num_sex_partners,first_sex,num_preg,pack_years,contracept_years,iud_years,stds_num]]))\n",
        "    risk_array = prediction_prob[:, 1]\n",
        "    risk = risk_array.astype(float)\n",
        "    if risk < 0.25:\n",
        "        print('Low Risk')\n",
        "    elif (0.25 <= risk < 0.5):\n",
        "        print('Low Moderate Risk')\n",
        "    elif (0.5 <= risk < 0.75):\n",
        "        print('High Moderate Risk')\n",
        "    else:\n",
        "        print('High Risk')\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "XKMKDS2jkD_V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict(ensemble_model, 25, 1, 20, 1, 10, 10, 10, 1)\n"
      ],
      "metadata": {
        "id": "SD2q2Em5kEB_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da233cb3-b7f2-4b14-9f9c-365edf6e160d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Low Moderate Risk\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the prediction function\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "def predict(model, age,num_sex_partners,first_sex,num_preg,pack_years,contracept_years,iud_years,stds_num):\n",
        "    #Predicting the price of the carat\n",
        "    prediction_prob = model.predict_proba(pd.DataFrame([[age,num_sex_partners,first_sex,num_preg,pack_years,contracept_years,iud_years,stds_num]]))\n",
        "    risk_array = prediction_prob[:, 1]\n",
        "    risk = risk_array.astype(float)\n",
        "    if risk < 0.25:\n",
        "        category = 'Low Risk'\n",
        "    elif (0.25 <= risk < 0.5):\n",
        "        category = 'Low Moderate Risk'\n",
        "    elif (0.5 <= risk < 0.75):\n",
        "        category = 'High Moderate Risk'\n",
        "    else:\n",
        "        category = 'High Risk'\n",
        "    return category "
      ],
      "metadata": {
        "id": "jmOYJvGQzAiX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "category = predict(ensemble_model, 25, 1, 20, 1, 10, 10, 10, 1)\n",
        "category\n",
        "print('Your risk category is {}'.format(category))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BK1QtunWzIgc",
        "outputId": "6dc67da8-3beb-40d2-8689-f44149ab53bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your risk category is Low Moderate Risk\n"
          ]
        }
      ]
    }
  ]
}