Data_Analysis.ipynb

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      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "-mGaOeR_Usv2"
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      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from scipy import stats\n",
        "from scipy.stats import randint\n",
        "\n",
        "# prep\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn import preprocessing\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.preprocessing import binarize, LabelEncoder, MinMaxScaler\n",
        "\n",
        "# models\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier\n",
        "\n",
        "# Validation libraries\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import accuracy_score, mean_squared_error, precision_recall_curve\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn import tree as t\n",
        "\n",
        "#Neural Network\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "#Bagging\n",
        "from sklearn.ensemble import BaggingClassifier, AdaBoostClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#Naive bayes\n",
        "from sklearn.naive_bayes import GaussianNB \n",
        "\n",
        "#Stacking\n",
        "# from mlxtend.classifier import StackingClassifier\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "from subprocess import check_output"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# read in the data\n",
        "import pandas as pd\n",
        "train_df  = pd.read_csv ('survey.csv')\n",
        "#Pandas: whats the data row count?\n",
        "print(train_df.shape)\n",
        "    \n",
        "#Pandas: whats the distribution of the data?\n",
        "print(train_df.describe())\n",
        "    \n",
        "#Pandas: What types of data do i have?\n",
        "print(train_df.info())"
      ],
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        "id": "si0avg-1UxtY",
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      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1259, 27)\n",
            "                Age\n",
            "count  1.259000e+03\n",
            "mean   7.942815e+07\n",
            "std    2.818299e+09\n",
            "min   -1.726000e+03\n",
            "25%    2.700000e+01\n",
            "50%    3.100000e+01\n",
            "75%    3.600000e+01\n",
            "max    1.000000e+11\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1259 entries, 0 to 1258\n",
            "Data columns (total 27 columns):\n",
            " #   Column                     Non-Null Count  Dtype \n",
            "---  ------                     --------------  ----- \n",
            " 0   Timestamp                  1259 non-null   object\n",
            " 1   Age                        1259 non-null   int64 \n",
            " 2   Gender                     1259 non-null   object\n",
            " 3   Country                    1259 non-null   object\n",
            " 4   state                      744 non-null    object\n",
            " 5   self_employed              1241 non-null   object\n",
            " 6   family_history             1259 non-null   object\n",
            " 7   treatment                  1259 non-null   object\n",
            " 8   work_interfere             995 non-null    object\n",
            " 9   no_employees               1259 non-null   object\n",
            " 10  remote_work                1259 non-null   object\n",
            " 11  tech_company               1259 non-null   object\n",
            " 12  benefits                   1259 non-null   object\n",
            " 13  care_options               1259 non-null   object\n",
            " 14  wellness_program           1259 non-null   object\n",
            " 15  seek_help                  1259 non-null   object\n",
            " 16  anonymity                  1259 non-null   object\n",
            " 17  leave                      1259 non-null   object\n",
            " 18  mental_health_consequence  1259 non-null   object\n",
            " 19  phys_health_consequence    1259 non-null   object\n",
            " 20  coworkers                  1259 non-null   object\n",
            " 21  supervisor                 1259 non-null   object\n",
            " 22  mental_health_interview    1259 non-null   object\n",
            " 23  phys_health_interview      1259 non-null   object\n",
            " 24  mental_vs_physical         1259 non-null   object\n",
            " 25  obs_consequence            1259 non-null   object\n",
            " 26  comments                   164 non-null    object\n",
            "dtypes: int64(1), object(26)\n",
            "memory usage: 265.7+ KB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Data Cleaning Phase***"
      ],
      "metadata": {
        "id": "bwknvkOvvHO_"
      }
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    {
      "cell_type": "code",
      "source": [
        "#dealing with missing data\n",
        "#Let’s get rid of the variables \"Timestamp\",“comments”, “state” just to make our lives easier.\n",
        "train_df = train_df.drop(['comments'], axis= 1)\n",
        "train_df = train_df.drop(['state'], axis= 1)\n",
        "train_df = train_df.drop(['Timestamp'], axis= 1)\n",
        "\n",
        "train_df.isnull().sum().max() #just checking that there's no missing data missing...\n",
        "train_df.head(5)"
      ],
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          "height": 357
        },
        "id": "UrtvrIkHvF8x",
        "outputId": "7c40235f-edfb-47de-f440-9a708695715c"
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      "execution_count": 73,
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        {
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              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>No</td>\n",
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              "      <td>No</td>\n",
              "      <td>Don't know</td>\n",
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              "      <td>Don't know</td>\n",
              "      <td>Don't know</td>\n",
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              "      <th>2</th>\n",
              "      <td>32</td>\n",
              "      <td>Male</td>\n",
              "      <td>Canada</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            "text/plain": [
              "   Age  Gender  ... mental_vs_physical obs_consequence\n",
              "0   37  Female  ...                Yes              No\n",
              "1   44       M  ...         Don't know              No\n",
              "2   32    Male  ...                 No              No\n",
              "3   31    Male  ...                 No             Yes\n",
              "4   31    Male  ...         Don't know              No\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assign default values for each data type\n",
        "defaultInt = 0\n",
        "defaultString = 'NaN'\n",
        "defaultFloat = 0.0\n",
        "\n",
        "# Create lists by data tpe\n",
        "intFeatures = ['Age']\n",
        "stringFeatures = ['Gender', 'Country', 'self_employed', 'family_history', 'treatment', 'work_interfere',\n",
        "                 'no_employees', 'remote_work', 'tech_company', 'anonymity', 'leave', 'mental_health_consequence',\n",
        "                 'phys_health_consequence', 'coworkers', 'supervisor', 'mental_health_interview', 'phys_health_interview',\n",
        "                 'mental_vs_physical', 'obs_consequence', 'benefits', 'care_options', 'wellness_program',\n",
        "                 'seek_help']\n",
        "floatFeatures = []\n",
        "\n",
        "# Clean the NaN's\n",
        "for feature in train_df:\n",
        "    if feature in intFeatures:\n",
        "        train_df[feature] = train_df[feature].fillna(defaultInt)\n",
        "    elif feature in stringFeatures:\n",
        "        train_df[feature] = train_df[feature].fillna(defaultString)\n",
        "    elif feature in floatFeatures:\n",
        "        train_df[feature] = train_df[feature].fillna(defaultFloat)\n",
        "    else:\n",
        "        print('Error: Feature %s not recognized.' % feature)\n",
        "train_df.head(5) "
      ],
      "metadata": {
        "id": "96V6m7JMvdcz",
        "outputId": "75f45a7b-7d8d-4462-aafb-9859edeff867",
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      },
      "execution_count": 74,
      "outputs": [
        {
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          "data": {
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              "      <th>mental_health_interview</th>\n",
              "      <th>phys_health_interview</th>\n",
              "      <th>mental_vs_physical</th>\n",
              "      <th>obs_consequence</th>\n",
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              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Often</td>\n",
              "      <td>6-25</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Not sure</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Somewhat easy</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Some of them</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>44</td>\n",
              "      <td>M</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Rarely</td>\n",
              "      <td>More than 1000</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>No</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>32</td>\n",
              "      <td>Male</td>\n",
              "      <td>Canada</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Rarely</td>\n",
              "      <td>6-25</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>Somewhat difficult</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "      <td>Male</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Often</td>\n",
              "      <td>26-100</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Somewhat difficult</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Some of them</td>\n",
              "      <td>No</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>Maybe</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31</td>\n",
              "      <td>Male</td>\n",
              "      <td>United States</td>\n",
              "      <td>NaN</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Never</td>\n",
              "      <td>100-500</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>No</td>\n",
              "      <td>No</td>\n",
              "      <td>Some of them</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Don't know</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-72cfaf7c-f743-44d6-b632-f6da1587de47')\"\n",
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              "      \n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-72cfaf7c-f743-44d6-b632-f6da1587de47');\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",
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              "  "
            ],
            "text/plain": [
              "   Age  Gender  ... mental_vs_physical obs_consequence\n",
              "0   37  Female  ...                Yes              No\n",
              "1   44       M  ...         Don't know              No\n",
              "2   32    Male  ...                 No              No\n",
              "3   31    Male  ...                 No             Yes\n",
              "4   31    Male  ...         Don't know              No\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# clean the column 'Gender'\n",
        "# convert all columns to lower case\n",
        "gender = train_df['Gender'].str.lower()\n",
        "# print(gender)\n",
        "\n",
        "#Select unique elements\n",
        "gender = train_df['Gender'].unique()\n",
        "\n",
        "#Made gender groups\n",
        "male_str = [\"male\", \"m\", \"male-ish\", \"maile\", \"mal\", \"male (cis)\", \"make\", \"male \", \"man\",\"msle\", \"mail\", \"malr\",\"cis man\", \"Cis Male\", \"cis male\"]\n",
        "trans_str = [\"trans-female\", \"something kinda male?\", \"queer/she/they\", \"non-binary\",\"nah\", \"all\", \"enby\", \"fluid\", \"genderqueer\", \"androgyne\", \"agender\", \"male leaning androgynous\", \"guy (-ish) ^_^\", \"trans woman\", \"neuter\", \"female (trans)\", \"queer\", \"ostensibly male, unsure what that really means\"]           \n",
        "female_str = [\"cis female\", \"f\", \"female\", \"woman\",  \"femake\", \"female \",\"cis-female/femme\", \"female (cis)\", \"femail\"]\n",
        "\n",
        "for (row, col) in train_df.iterrows():\n",
        "\n",
        "    if str.lower(col.Gender) in male_str:\n",
        "        train_df['Gender'].replace(to_replace=col.Gender, value='male', inplace=True)\n",
        "\n",
        "    if str.lower(col.Gender) in female_str:\n",
        "        train_df['Gender'].replace(to_replace=col.Gender, value='female', inplace=True)\n",
        "\n",
        "    if str.lower(col.Gender) in trans_str:\n",
        "        train_df['Gender'].replace(to_replace=col.Gender, value='trans', inplace=True)\n",
        "\n",
        "#Get rid of bullshit\n",
        "stk_list = ['A little about you', 'p']\n",
        "train_df = train_df[~train_df['Gender'].isin(stk_list)]\n",
        "\n",
        "print(train_df['Gender'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f1H6r82dvoT_",
        "outputId": "b420375e-4743-40fb-85cd-53f51ea84833"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['female' 'male' 'trans']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#complete missing age with mean\n",
        "train_df['Age'].fillna(train_df['Age'].median(), inplace = True)\n",
        "\n",
        "# Fill with media() values < 18 and > 120\n",
        "s = pd.Series(train_df['Age'])\n",
        "s[s<18] = train_df['Age'].median()\n",
        "train_df['Age'] = s\n",
        "s = pd.Series(train_df['Age'])\n",
        "s[s>120] = train_df['Age'].median()\n",
        "train_df['Age'] = s\n",
        "\n",
        "#Ranges of Age\n",
        "train_df['age_range'] = pd.cut(train_df['Age'], [0,20,30,65,100], labels=[\"0-20\", \"21-30\", \"31-65\", \"66-100\"], include_lowest=True)"
      ],
      "metadata": {
        "id": "HeL9i2b9ayR2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "493895c6-f1be-4095-80fc-f51958af2c51"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py:6392: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  return self._update_inplace(result)\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:7: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  import sys\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  # Remove the CWD from sys.path while we load stuff.\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  del sys.path[0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#There are only 0.014% of self employed so let's change NaN to NOT self_employed\n",
        "#Replace \"NaN\" string from defaultString\n",
        "train_df['self_employed'] = train_df['self_employed'].replace([defaultString], 'No')\n",
        "print(train_df['self_employed'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9Hv4sZ4tyREe",
        "outputId": "bf2b4af0-84d6-40f7-de0a-ba1b8035796c"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['No' 'Yes']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#There are only 0.20% of self work_interfere so let's change NaN to \"Don't know\n",
        "#Replace \"NaN\" string from defaultString\n",
        "\n",
        "train_df['work_interfere'] = train_df['work_interfere'].replace([defaultString], 'Don\\'t know' )\n",
        "print(train_df['work_interfere'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3QA5709yZhM",
        "outputId": "3be20057-9fef-4d1a-b670-8f42500c6054"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Often' 'Rarely' 'Never' 'Sometimes' \"Don't know\"]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Encoding Data***"
      ],
      "metadata": {
        "id": "ojRFZFTlyi_U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Encoding data\n",
        "labelDict = {}\n",
        "for feature in train_df:\n",
        "    le = preprocessing.LabelEncoder()\n",
        "    le.fit(train_df[feature])\n",
        "    le_name_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n",
        "    train_df[feature] = le.transform(train_df[feature])\n",
        "    # Get labels\n",
        "    labelKey = 'label_' + feature\n",
        "    labelValue = [*le_name_mapping]\n",
        "    labelDict[labelKey] =labelValue\n",
        "    \n",
        "for key, value in labelDict.items():     \n",
        "    print(key, value)\n",
        "\n",
        "#Get rid of 'Country'\n",
        "train_df = train_df.drop(['Country'], axis= 1)\n",
        "train_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        },
        "id": "qOtlObixycvP",
        "outputId": "d530d2a3-69ad-4a68-87bb-dd11faf45fef"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "label_Age [18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 53, 54, 55, 56, 57, 58, 60, 61, 62, 65, 72]\n",
            "label_Gender ['female', 'male', 'trans']\n",
            "label_Country ['Australia', 'Austria', 'Belgium', 'Bosnia and Herzegovina', 'Brazil', 'Bulgaria', 'Canada', 'China', 'Colombia', 'Costa Rica', 'Croatia', 'Czech Republic', 'Denmark', 'Finland', 'France', 'Georgia', 'Germany', 'Greece', 'Hungary', 'India', 'Ireland', 'Israel', 'Italy', 'Japan', 'Latvia', 'Mexico', 'Moldova', 'Netherlands', 'New Zealand', 'Nigeria', 'Norway', 'Philippines', 'Poland', 'Portugal', 'Romania', 'Russia', 'Singapore', 'Slovenia', 'South Africa', 'Spain', 'Sweden', 'Switzerland', 'Thailand', 'United Kingdom', 'United States', 'Uruguay', 'Zimbabwe']\n",
            "label_self_employed ['No', 'Yes']\n",
            "label_family_history ['No', 'Yes']\n",
            "label_treatment ['No', 'Yes']\n",
            "label_work_interfere [\"Don't know\", 'Never', 'Often', 'Rarely', 'Sometimes']\n",
            "label_no_employees ['1-5', '100-500', '26-100', '500-1000', '6-25', 'More than 1000']\n",
            "label_remote_work ['No', 'Yes']\n",
            "label_tech_company ['No', 'Yes']\n",
            "label_benefits [\"Don't know\", 'No', 'Yes']\n",
            "label_care_options ['No', 'Not sure', 'Yes']\n",
            "label_wellness_program [\"Don't know\", 'No', 'Yes']\n",
            "label_seek_help [\"Don't know\", 'No', 'Yes']\n",
            "label_anonymity [\"Don't know\", 'No', 'Yes']\n",
            "label_leave [\"Don't know\", 'Somewhat difficult', 'Somewhat easy', 'Very difficult', 'Very easy']\n",
            "label_mental_health_consequence ['Maybe', 'No', 'Yes']\n",
            "label_phys_health_consequence ['Maybe', 'No', 'Yes']\n",
            "label_coworkers ['No', 'Some of them', 'Yes']\n",
            "label_supervisor ['No', 'Some of them', 'Yes']\n",
            "label_mental_health_interview ['Maybe', 'No', 'Yes']\n",
            "label_phys_health_interview ['Maybe', 'No', 'Yes']\n",
            "label_mental_vs_physical [\"Don't know\", 'No', 'Yes']\n",
            "label_obs_consequence ['No', 'Yes']\n",
            "label_age_range ['0-20', '21-30', '31-65', '66-100']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-613cfb55-4423-442b-8a6b-bd01ae07300a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>self_employed</th>\n",
              "      <th>family_history</th>\n",
              "      <th>treatment</th>\n",
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              "      <th>seek_help</th>\n",
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              "      <th>leave</th>\n",
              "      <th>mental_health_consequence</th>\n",
              "      <th>phys_health_consequence</th>\n",
              "      <th>coworkers</th>\n",
              "      <th>supervisor</th>\n",
              "      <th>mental_health_interview</th>\n",
              "      <th>phys_health_interview</th>\n",
              "      <th>mental_vs_physical</th>\n",
              "      <th>obs_consequence</th>\n",
              "      <th>age_range</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-613cfb55-4423-442b-8a6b-bd01ae07300a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      </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",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-613cfb55-4423-442b-8a6b-bd01ae07300a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Age  Gender  self_employed  ...  mental_vs_physical  obs_consequence  age_range\n",
              "0   19       0              0  ...                   2                0          2\n",
              "1   26       1              0  ...                   0                0          2\n",
              "2   14       1              0  ...                   1                0          2\n",
              "3   13       1              0  ...                   1                1          2\n",
              "4   13       1              0  ...                   0                0          2\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#missing data\n",
        "total = train_df.isnull().sum().sort_values(ascending=False)\n",
        "percent = (train_df.isnull().sum()/train_df.isnull().count()).sort_values(ascending=False)\n",
        "missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])\n",
        "missing_data.head(20)\n",
        "print(missing_data)\n",
        "\n",
        "train_df.to_csv('cleaned_survey.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TqFdyqB8ymro",
        "outputId": "ef14067d-290c-4553-feb1-287ff48215d2"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                           Total  Percent\n",
            "Age                            0      0.0\n",
            "Gender                         0      0.0\n",
            "obs_consequence                0      0.0\n",
            "mental_vs_physical             0      0.0\n",
            "phys_health_interview          0      0.0\n",
            "mental_health_interview        0      0.0\n",
            "supervisor                     0      0.0\n",
            "coworkers                      0      0.0\n",
            "phys_health_consequence        0      0.0\n",
            "mental_health_consequence      0      0.0\n",
            "leave                          0      0.0\n",
            "anonymity                      0      0.0\n",
            "seek_help                      0      0.0\n",
            "wellness_program               0      0.0\n",
            "care_options                   0      0.0\n",
            "benefits                       0      0.0\n",
            "tech_company                   0      0.0\n",
            "remote_work                    0      0.0\n",
            "no_employees                   0      0.0\n",
            "work_interfere                 0      0.0\n",
            "treatment                      0      0.0\n",
            "family_history                 0      0.0\n",
            "self_employed                  0      0.0\n",
            "age_range                      0      0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#correlation matrix\n",
        "corrmat = train_df.corr()\n",
        "f, ax = plt.subplots(figsize=(12, 9))\n",
        "sns.heatmap(corrmat, vmax=.8, square=True);\n",
        "plt.show()\n",
        "\n",
        "#treatment correlation matrix\n",
        "k = 10 #number of variables for heatmap\n",
        "cols = corrmat.nlargest(k, 'treatment')['treatment'].index\n",
        "cm = np.corrcoef(train_df[cols].values.T)\n",
        "sns.set(font_scale=1.25)\n",
        "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "aq2cNlLFyu0q",
        "outputId": "fdb5bfa5-8ec0-4d7c-99ed-7cdcbc6ac681"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define X and y\n",
        "feature_cols = ['Age', 'Gender', 'benefits', 'care_options', 'anonymity', 'leave', 'work_interfere']\n",
        "X = train_df[feature_cols]\n",
        "y = train_df.treatment\n",
        "\n",
        "# split X and y into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n",
        "\n",
        "# Create dictionaries for final graph\n",
        "# Use: methodDict['Stacking'] = accuracy_score\n",
        "methodDict = {}\n",
        "rmseDict = ()"
      ],
      "metadata": {
        "id": "9jwxKD7aZUXu"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def treeClassifier():\n",
        "    # Calculating the best parameters\n",
        "    tree = DecisionTreeClassifier()\n",
        "    featuresSize = feature_cols.__len__()\n",
        "    param_dist = {\"max_depth\": [3, None],\n",
        "              \"max_features\": randint(1, featuresSize),\n",
        "              \"min_samples_split\": randint(2, 9),\n",
        "              \"min_samples_leaf\": randint(1, 9),\n",
        "              \"criterion\": [\"gini\", \"entropy\"]}\n",
        "    tuningRandomizedSearchCV(tree, param_dist)\n",
        "    \n",
        "    # train a decision tree model on the training set\n",
        "    # tree = DecisionTreeClassifier()\n",
        "    model = tree.fit(X_train, y_train)\n",
        "\n",
        "    # get importance\n",
        "    importance = model.feature_importances_\n",
        "    # summarize feature importance\n",
        "    print(feature_cols)\n",
        "\n",
        "    for name, importance in zip(feature_cols, importance):\n",
        "      # print('Feature: %0d, Score: %.5f' % (i,importance))\n",
        "      print(name, importance)\n",
        "\n",
        "    # make class predictions for the testing set\n",
        "    y_pred_class = tree.predict(X_test)\n",
        "    \n",
        "    print('########### Tree classifier ###############')\n",
        "    \n",
        "    accuracy_score = evalClassModel(tree, y_test, y_pred_class, True)\n",
        "\n",
        "    #Data for final graph\n",
        "    methodDict['Tree clas.'] = accuracy_score * 100\n",
        "\n",
        "\n",
        "\n",
        "    return tree, model, feature_cols"
      ],
      "metadata": {
        "id": "KFRUVIB-ZYLP"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def tuningRandomizedSearchCV(model, param_dist):\n",
        "    #Searching multiple parameters simultaneously\n",
        "    # n_iter controls the number of searches\n",
        "    rand = RandomizedSearchCV(model, param_dist, cv=10, scoring='accuracy', n_iter=10, random_state=5)\n",
        "    rand.fit(X, y)\n",
        "    # rand.grid_scores_\n",
        "    \n",
        "    # examine the best model\n",
        "    print('Rand. Best Score: ', rand.best_score_)\n",
        "    print('Rand. Best Params: ', rand.best_params_)\n",
        "    \n",
        "    # run RandomizedSearchCV 20 times (with n_iter=10) and record the best score\n",
        "    best_scores = []\n",
        "    for _ in range(20):\n",
        "        rand = RandomizedSearchCV(model, param_dist, cv=10, scoring='accuracy', n_iter=10)\n",
        "        rand.fit(X, y)\n",
        "        best_scores.append(round(rand.best_score_, 3))\n",
        "    print(best_scores)"
      ],
      "metadata": {
        "id": "CfvznD5GJ49L"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evalClassModel(model, y_test, y_pred_class, plot=False):\n",
        "    #Classification accuracy: percentage of correct predictions\n",
        "    # calculate accuracy\n",
        "    print('Accuracy:', metrics.accuracy_score(y_test, y_pred_class))\n",
        "    \n",
        "    #Null accuracy: accuracy that could be achieved by always predicting the most frequent class\n",
        "    # examine the class distribution of the testing set (using a Pandas Series method)\n",
        "    print('Null accuracy:\\n', y_test.value_counts())\n",
        "    \n",
        "    # calculate the percentage of ones\n",
        "    print('Percentage of ones:', y_test.mean())\n",
        "    \n",
        "    # calculate the percentage of zeros\n",
        "    print('Percentage of zeros:',1 - y_test.mean())\n",
        "    \n",
        "    #Comparing the true and predicted response values\n",
        "    print('True:', y_test.values[0:25])\n",
        "    print('Pred:', y_pred_class[0:25])\n",
        "    \n",
        "    #Conclusion:\n",
        "    #Classification accuracy is the easiest classification metric to understand\n",
        "    #But, it does not tell you the underlying distribution of response values\n",
        "    #And, it does not tell you what \"types\" of errors your classifier is making\n",
        "    \n",
        "    #Confusion matrix\n",
        "    # save confusion matrix and slice into four pieces\n",
        "    confusion = metrics.confusion_matrix(y_test, y_pred_class)\n",
        "    #[row, column]\n",
        "    TP = confusion[1, 1]\n",
        "    TN = confusion[0, 0]\n",
        "    FP = confusion[0, 1]\n",
        "    FN = confusion[1, 0]\n",
        "    \n",
        "    # visualize Confusion Matrix\n",
        "    sns.heatmap(confusion,annot=True,fmt=\"d\") \n",
        "    plt.title('Confusion Matrix')\n",
        "    plt.xlabel('Predicted')\n",
        "    plt.ylabel('Actual')\n",
        "    plt.show()\n",
        "    \n",
        "    #Metrics computed from a confusion matrix\n",
        "    #Classification Accuracy: Overall, how often is the classifier correct?\n",
        "    accuracy = metrics.accuracy_score(y_test, y_pred_class)\n",
        "    print('Classification Accuracy:', accuracy)\n",
        "    \n",
        "    #Classification Error: Overall, how often is the classifier incorrect?\n",
        "    print('Classification Error:', 1 - metrics.accuracy_score(y_test, y_pred_class))\n",
        "    \n",
        "    #False Positive Rate: When the actual value is negative, how often is the prediction incorrect?\n",
        "    false_positive_rate = FP / float(TN + FP)\n",
        "    print('False Positive Rate:', false_positive_rate)\n",
        "    \n",
        "    #Precision: When a positive value is predicted, how often is the prediction correct?\n",
        "    print('Precision:', metrics.precision_score(y_test, y_pred_class))\n",
        "    \n",
        "    \n",
        "    # IMPORTANT: first argument is true values, second argument is predicted probabilities\n",
        "    print('AUC Score:', metrics.roc_auc_score(y_test, y_pred_class))\n",
        "    \n",
        "    # calculate cross-validated AUC\n",
        "    print('Cross-validated AUC:', cross_val_score(model, X, y, cv=10, scoring='roc_auc').mean())\n",
        "    \n",
        "    ##########################################\n",
        "    #Adjusting the classification threshold\n",
        "    ##########################################\n",
        "    # print the first 10 predicted responses\n",
        "    # 1D array (vector) of binary values (0, 1)\n",
        "    print('First 10 predicted responses:\\n', model.predict(X_test)[0:10])\n",
        "\n",
        "    # print the first 10 predicted probabilities of class membership\n",
        "    print('First 10 predicted probabilities of class members:\\n', model.predict_proba(X_test)[0:10])\n",
        "\n",
        "    # print the first 10 predicted probabilities for class 1\n",
        "    model.predict_proba(X_test)[0:10, 1]\n",
        "    \n",
        "    # store the predicted probabilities for class 1\n",
        "    y_pred_prob = model.predict_proba(X_test)[:, 1]\n",
        "    \n",
        "    if plot == True:\n",
        "        # histogram of predicted probabilities\n",
        "        # adjust the font size \n",
        "        plt.rcParams['font.size'] = 12\n",
        "        # 8 bins\n",
        "        plt.hist(y_pred_prob, bins=8)\n",
        "        \n",
        "        # x-axis limit from 0 to 1\n",
        "        plt.xlim(0,1)\n",
        "        plt.title('Histogram of predicted probabilities')\n",
        "        plt.xlabel('Predicted probability of treatment')\n",
        "        plt.ylabel('Frequency')\n",
        "    \n",
        "    \n",
        "    # predict treatment if the predicted probability is greater than 0.3\n",
        "    # it will return 1 for all values above 0.3 and 0 otherwise\n",
        "    # results are 2D so we slice out the first column\n",
        "    y_pred_prob = y_pred_prob.reshape(-1,1) \n",
        "    # y_pred_class = y_pred_prob.binarize(0.3)[0]\n",
        "    y_pred_class = np.where(y_pred_class > 0.3, 1, 0)\n",
        "    # print the first 10 predicted probabilities\n",
        "    print('First 10 predicted probabilities:\\n', y_pred_prob[0:10])\n",
        "    \n",
        "    ##########################################\n",
        "    #ROC Curves and Area Under the Curve (AUC)\n",
        "    ##########################################\n",
        "    \n",
        "    #Question: Wouldn't it be nice if we could see how sensitivity and specificity are affected by various thresholds, without actually changing the threshold?\n",
        "    #Answer: Plot the ROC curve!\n",
        "    \n",
        "    \n",
        "    #AUC is the percentage of the ROC plot that is underneath the curve\n",
        "    #Higher value = better classifier\n",
        "    roc_auc = metrics.roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "    # IMPORTANT: first argument is true values, second argument is predicted probabilities\n",
        "    # we pass y_test and y_pred_prob\n",
        "    # we do not use y_pred_class, because it will give incorrect results without generating an error\n",
        "    # roc_curve returns 3 objects fpr, tpr, thresholds\n",
        "    # fpr: false positive rate\n",
        "    # tpr: true positive rate\n",
        "    fpr, tpr, thresholds = metrics.roc_curve(y_test, y_pred_prob)\n",
        "    if plot == True:\n",
        "        plt.figure()\n",
        "        \n",
        "        plt.plot(fpr, tpr, color='darkorange', label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "        plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
        "        plt.xlim([0.0, 1.0])\n",
        "        plt.ylim([0.0, 1.0])\n",
        "        plt.rcParams['font.size'] = 12\n",
        "        plt.title('ROC curve for treatment classifier')\n",
        "        plt.xlabel('False Positive Rate (1 - Specificity)')\n",
        "        plt.ylabel('True Positive Rate (Sensitivity)')\n",
        "        plt.legend(loc=\"lower right\")\n",
        "        plt.show()\n",
        "      \n",
        "    \n",
        "    return accuracy\n",
        "    "
      ],
      "metadata": {
        "id": "sqr0fI0_KzFW"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "decision_tree, model, feature_cols = treeClassifier()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GJ0TpL5pJRVd",
        "outputId": "b5a568c7-0301-447e-fba0-aaca8ab75270"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rand. Best Score:  0.8305206349206349\n",
            "Rand. Best Params:  {'criterion': 'entropy', 'max_depth': 3, 'max_features': 5, 'min_samples_leaf': 7, 'min_samples_split': 7}\n",
            "[0.831, 0.83, 0.831, 0.831, 0.831, 0.831, 0.83, 0.829, 0.83, 0.831, 0.789, 0.83, 0.817, 0.831, 0.831, 0.831, 0.823, 0.823, 0.829, 0.831]\n",
            "['Age', 'Gender', 'benefits', 'care_options', 'anonymity', 'leave', 'work_interfere']\n",
            "Age 0.20095705233980482\n",
            "Gender 0.026708522377941347\n",
            "benefits 0.04471877315713845\n",
            "care_options 0.05708755058311387\n",
            "anonymity 0.05270595488336403\n",
            "leave 0.07368193033326098\n",
            "work_interfere 0.5441402163253765\n",
            "########### Tree classifier ###############\n",
            "Accuracy: 0.7513227513227513\n",
            "Null accuracy:\n",
            " 0    191\n",
            "1    187\n",
            "Name: treatment, dtype: int64\n",
            "Percentage of ones: 0.4947089947089947\n",
            "Percentage of zeros: 0.5052910052910053\n",
            "True: [0 0 0 0 0 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 0 1 1 0 0]\n",
            "Pred: [0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Classification Accuracy: 0.7513227513227513\n",
            "Classification Error: 0.24867724867724872\n",
            "False Positive Rate: 0.28272251308900526\n",
            "Precision: 0.7313432835820896\n",
            "AUC Score: 0.7516868717977434\n",
            "Cross-validated AUC: 0.7427234644604557\n",
            "First 10 predicted responses:\n",
            " [0 0 0 0 0 1 0 1 0 1]\n",
            "First 10 predicted probabilities of class members:\n",
            " [[0.5 0.5]\n",
            " [1.  0. ]\n",
            " [1.  0. ]\n",
            " [1.  0. ]\n",
            " [1.  0. ]\n",
            " [0.  1. ]\n",
            " [1.  0. ]\n",
            " [0.  1. ]\n",
            " [1.  0. ]\n",
            " [0.  1. ]]\n",
            "First 10 predicted probabilities:\n",
            " [[0.5]\n",
            " [0. ]\n",
            " [0. ]\n",
            " [0. ]\n",
            " [0. ]\n",
            " [1. ]\n",
            " [0. ]\n",
            " [1. ]\n",
            " [0. ]\n",
            " [1. ]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(25,20))\n",
        "_ = t.plot_tree(decision_tree, max_depth=3, feature_names=feature_cols, filled=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "o5pZrGt8QdHU",
        "outputId": "50a220c9-1d67-4177-b5a3-5a145e747bfb"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1800x1440 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict_proba([[1, 0, 1, 1, 1, 1, 1]])\n",
        "# ['Age', 'Gender', 'benefits', 'care_options', 'anonymity', 'leave', 'work_interfere']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdiBvIFLeQYk",
        "outputId": "075b2404-e666-4524-e332-c1f53733ba52"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
            "  \"X does not have valid feature names, but\"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "wVbF41WrSdFe"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}