Machine_Learning_Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dependencies\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import StandardScaler,OneHotEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report\n",
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data from SQLite database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>United Kingdom</td>\n",
       "      <td>United Kingdom</td>\n",
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       "      <td>I don't know</td>\n",
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       "      <td>United States of America</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>No</td>\n",
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       "      <td>Female</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
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       "      <th>...</th>\n",
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       "      <td>Rarely</td>\n",
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       "      <td>Canada</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
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       "      <th>857</th>\n",
       "      <td>I don't know</td>\n",
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       "      <td>Yes</td>\n",
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       "      <td>1</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>Often</td>\n",
       "      <td>Female</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>Yes</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>1</td>\n",
       "      <td>Rarely</td>\n",
       "      <td>Often</td>\n",
       "      <td>Female</td>\n",
       "      <td>United States of America</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
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       "      <td>Somewhat difficult</td>\n",
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       "      <td>Rarely</td>\n",
       "      <td>Sometimes</td>\n",
       "      <td>Male</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
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       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>I don't know</td>\n",
       "      <td>I am not sure</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Somewhat difficult</td>\n",
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       "      <td>Female</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>United States of America</td>\n",
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       "<p>861 rows × 32 columns</p>\n",
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      ],
      "text/plain": [
       "                0              1             2                           3  \\\n",
       "0              No            Yes           Yes               Somewhat easy   \n",
       "1             Yes            Yes            No  Neither easy nor difficult   \n",
       "2             Yes  I am not sure           Yes               Somewhat easy   \n",
       "3    I don't know             No            No               Somewhat easy   \n",
       "4             Yes            Yes           Yes                   Very easy   \n",
       "..            ...            ...           ...                         ...   \n",
       "856           Yes  I am not sure           Yes               Somewhat easy   \n",
       "857  I don't know  I am not sure  I don't know               Somewhat easy   \n",
       "858           Yes             No            No               Somewhat easy   \n",
       "859           Yes            Yes           Yes          Somewhat difficult   \n",
       "860  I don't know  I am not sure           Yes          Somewhat difficult   \n",
       "\n",
       "         4      5             6    7  8                  9  ...            22  \\\n",
       "0       No     No           Yes   No  1  Yes, they all did  ...           Yes   \n",
       "1      Yes  Maybe            No   No  1       I don't know  ...           Yes   \n",
       "2      Yes    Yes            No  Yes  1       No, none did  ...            No   \n",
       "3       No     No           Yes   No  1           Some did  ...            No   \n",
       "4       No     No  I don't know   No  1           Some did  ...           Yes   \n",
       "..     ...    ...           ...  ... ..                ...  ...           ...   \n",
       "856     No     No           Yes   No  1           Some did  ...  I don't know   \n",
       "857  Maybe     No  I don't know   No  1       I don't know  ...  I don't know   \n",
       "858     No     No           Yes   No  1           Some did  ...           Yes   \n",
       "859  Maybe  Maybe  I don't know  Yes  1           Some did  ...           Yes   \n",
       "860  Maybe     No            No   No  1       No, none did  ...           Yes   \n",
       "\n",
       "        23     24   25 26                    27                    28      29  \\\n",
       "0      Yes    Yes  Yes  1                Rarely             Sometimes    Male   \n",
       "1      Yes    Yes  Yes  1             Sometimes             Sometimes  Female   \n",
       "2       No    Yes   No  1  Not applicable to me                 Often    Male   \n",
       "3       No     No   No  0  Not applicable to me  Not applicable to me    Male   \n",
       "4      Yes    Yes  Yes  1             Sometimes                 Often  Female   \n",
       "..     ...    ...  ... ..                   ...                   ...     ...   \n",
       "856  Maybe  Maybe   No  0                Rarely                Rarely    Male   \n",
       "857    Yes    Yes  Yes  1                Rarely                 Often  Female   \n",
       "858    Yes    Yes  Yes  1                Rarely                 Often  Female   \n",
       "859    Yes  Maybe  Yes  1                Rarely             Sometimes    Male   \n",
       "860  Maybe    Yes  Yes  0             Sometimes                 Often  Female   \n",
       "\n",
       "                           30                        31  \n",
       "0    United States of America  United States of America  \n",
       "1    United States of America  United States of America  \n",
       "2              United Kingdom            United Kingdom  \n",
       "3    United States of America  United States of America  \n",
       "4    United States of America  United States of America  \n",
       "..                        ...                       ...  \n",
       "856                    Canada                    Canada  \n",
       "857                    Canada                    Canada  \n",
       "858  United States of America  United States of America  \n",
       "859  United States of America  United States of America  \n",
       "860  United States of America  United States of America  \n",
       "\n",
       "[861 rows x 32 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data back into a dataframe from the database\n",
    "mental_df = pd.read_sql_table('pre_encoded_survey', 'sqlite:///mental_health.db').drop(columns=['index'])\n",
    "mental_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     object\n",
       "1     object\n",
       "2     object\n",
       "3     object\n",
       "4     object\n",
       "5     object\n",
       "6     object\n",
       "7     object\n",
       "8      int64\n",
       "9     object\n",
       "10    object\n",
       "11    object\n",
       "12    object\n",
       "13    object\n",
       "14    object\n",
       "15    object\n",
       "16    object\n",
       "17    object\n",
       "18    object\n",
       "19    object\n",
       "20    object\n",
       "21    object\n",
       "22    object\n",
       "23    object\n",
       "24    object\n",
       "25    object\n",
       "26     int64\n",
       "27    object\n",
       "28    object\n",
       "29    object\n",
       "30    object\n",
       "31    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "mental_df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     3\n",
       "2     3\n",
       "3     6\n",
       "4     3\n",
       "5     3\n",
       "6     3\n",
       "7     2\n",
       "9     4\n",
       "10    4\n",
       "11    4\n",
       "12    4\n",
       "13    4\n",
       "14    4\n",
       "15    4\n",
       "16    3\n",
       "17    3\n",
       "18    5\n",
       "19    5\n",
       "20    6\n",
       "21    4\n",
       "22    3\n",
       "23    3\n",
       "24    3\n",
       "25    2\n",
       "27    5\n",
       "28    5\n",
       "29    3\n",
       "30    7\n",
       "31    8\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate our categorical variable list\n",
    "mental_cat = mental_df.dtypes[mental_df.dtypes == \"object\"].index.tolist()\n",
    "\n",
    "# Check the number of unique values in each column\n",
    "mental_df[mental_cat].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>31_Canada</th>\n",
       "      <th>31_Germany</th>\n",
       "      <th>31_Netherlands</th>\n",
       "      <th>31_Other</th>\n",
       "      <th>31_Sweden</th>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 120 columns</p>\n",
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      "text/plain": [
       "   0_I don't know  0_No  0_Not eligible for coverage / N/A  0_Yes  \\\n",
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       "   1_I am not sure  1_No  1_Yes  2_I don't know  2_No  2_Yes  ...  \\\n",
       "0              0.0   0.0    1.0             0.0   0.0    1.0  ...   \n",
       "1              0.0   0.0    1.0             0.0   1.0    0.0  ...   \n",
       "2              1.0   0.0    0.0             0.0   0.0    1.0  ...   \n",
       "3              0.0   1.0    0.0             0.0   1.0    0.0  ...   \n",
       "4              0.0   0.0    1.0             0.0   0.0    1.0  ...   \n",
       "\n",
       "   30_United Kingdom  30_United States of America  31_Australia  31_Canada  \\\n",
       "0                0.0                          1.0           0.0        0.0   \n",
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       "3                0.0                          1.0           0.0        0.0   \n",
       "4                0.0                          1.0           0.0        0.0   \n",
       "\n",
       "   31_Germany  31_Netherlands  31_Other  31_Sweden  31_United Kingdom  \\\n",
       "0         0.0             0.0       0.0        0.0                0.0   \n",
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       "\n",
       "[5 rows x 120 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a OneHotEncoder instance\n",
    "enc = OneHotEncoder(sparse=False)\n",
    "\n",
    "# Fit and transform the OneHotEncoder using the categorical variable list\n",
    "encode_df = pd.DataFrame(enc.fit_transform(mental_df[mental_cat]))\n",
    "\n",
    "# Add the encoded variable names to the DataFrame\n",
    "encode_df.columns = enc.get_feature_names(mental_cat)\n",
    "encode_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge one-hot encoded features and drop the originals\n",
    "mental_df = mental_df.merge(encode_df,left_index=True, right_index=True)\n",
    "mental_df = mental_df.drop(mental_cat,1)\n",
    "mental_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Send encoded dataframe to database to be used in model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the engine\n",
    "engine = create_engine(\"sqlite:///mental_health.db\", echo=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Use pandas to_sql to write records stored in a dataframe to a SQL database\n",
    "\n",
    "# Write dataframe of all survey questions to a sqlite table\n",
    "mental_df.to_sql('encoded_survey', con=engine, if_exists='replace')\n",
    "#engine.execute(\"SELECT * FROM encoded_survey\").fetchall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>859</th>\n",
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       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>861 rows × 122 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     8  26  0_I don't know  0_No  0_Not eligible for coverage / N/A  0_Yes  \\\n",
       "0    1   1             0.0   1.0                                0.0    0.0   \n",
       "1    1   1             0.0   0.0                                0.0    1.0   \n",
       "2    1   1             0.0   0.0                                0.0    1.0   \n",
       "3    1   0             1.0   0.0                                0.0    0.0   \n",
       "4    1   1             0.0   0.0                                0.0    1.0   \n",
       "..  ..  ..             ...   ...                                ...    ...   \n",
       "856  1   0             0.0   0.0                                0.0    1.0   \n",
       "857  1   1             1.0   0.0                                0.0    0.0   \n",
       "858  1   1             0.0   0.0                                0.0    1.0   \n",
       "859  1   1             0.0   0.0                                0.0    1.0   \n",
       "860  1   0             1.0   0.0                                0.0    0.0   \n",
       "\n",
       "     1_I am not sure  1_No  1_Yes  2_I don't know  ...  30_United Kingdom  \\\n",
       "0                0.0   0.0    1.0             0.0  ...                0.0   \n",
       "1                0.0   0.0    1.0             0.0  ...                0.0   \n",
       "2                1.0   0.0    0.0             0.0  ...                1.0   \n",
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       "4                0.0   0.0    1.0             0.0  ...                0.0   \n",
       "..               ...   ...    ...             ...  ...                ...   \n",
       "856              1.0   0.0    0.0             0.0  ...                0.0   \n",
       "857              1.0   0.0    0.0             1.0  ...                0.0   \n",
       "858              0.0   1.0    0.0             0.0  ...                0.0   \n",
       "859              0.0   0.0    1.0             0.0  ...                0.0   \n",
       "860              1.0   0.0    0.0             0.0  ...                0.0   \n",
       "\n",
       "     30_United States of America  31_Australia  31_Canada  31_Germany  \\\n",
       "0                            1.0           0.0        0.0         0.0   \n",
       "1                            1.0           0.0        0.0         0.0   \n",
       "2                            0.0           0.0        0.0         0.0   \n",
       "3                            1.0           0.0        0.0         0.0   \n",
       "4                            1.0           0.0        0.0         0.0   \n",
       "..                           ...           ...        ...         ...   \n",
       "856                          0.0           0.0        1.0         0.0   \n",
       "857                          0.0           0.0        1.0         0.0   \n",
       "858                          1.0           0.0        0.0         0.0   \n",
       "859                          1.0           0.0        0.0         0.0   \n",
       "860                          1.0           0.0        0.0         0.0   \n",
       "\n",
       "     31_Netherlands  31_Other  31_Sweden  31_United Kingdom  \\\n",
       "0               0.0       0.0        0.0                0.0   \n",
       "1               0.0       0.0        0.0                0.0   \n",
       "2               0.0       0.0        0.0                1.0   \n",
       "3               0.0       0.0        0.0                0.0   \n",
       "4               0.0       0.0        0.0                0.0   \n",
       "..              ...       ...        ...                ...   \n",
       "856             0.0       0.0        0.0                0.0   \n",
       "857             0.0       0.0        0.0                0.0   \n",
       "858             0.0       0.0        0.0                0.0   \n",
       "859             0.0       0.0        0.0                0.0   \n",
       "860             0.0       0.0        0.0                0.0   \n",
       "\n",
       "     31_United States of America  \n",
       "0                            1.0  \n",
       "1                            1.0  \n",
       "2                            0.0  \n",
       "3                            1.0  \n",
       "4                            1.0  \n",
       "..                           ...  \n",
       "856                          0.0  \n",
       "857                          0.0  \n",
       "858                          1.0  \n",
       "859                          1.0  \n",
       "860                          1.0  \n",
       "\n",
       "[861 rows x 122 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data back into a dataframe from the database\n",
    "mental_df = pd.read_sql_table('encoded_survey', 'sqlite:///mental_health.db').drop(columns=['index'])\n",
    "mental_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Question</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Does your employer provide mental health benef...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Do you know the options for mental health care...</td>\n",
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       "      <th>2</th>\n",
       "      <td>Does your employer offer resources to learn mo...</td>\n",
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       "      <td>If a mental health issue prompted you to reque...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Do you think that discussing a mental health d...</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Do you think that discussing a physical health...</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Do you feel that your employer takes mental he...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Have you heard of or observed negative consequ...</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Do you have previous employers?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Have your previous employers provided mental h...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Were you aware of the options for mental healt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Did your previous employers ever formally disc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Was your anonymity protected if you chose to t...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Do you think that discussing a mental health d...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Would you have been willing to discuss a menta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Did you feel that your previous employers took...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Did you hear of or observe negative consequenc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Would you bring up a mental health issue with ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Do you feel that being identified as a person ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Do you think that team members/co-workers woul...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>How willing would you be to share with friends...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Have you observed or experienced an unsupporti...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Do you have a family history of mental illness?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Have you had a mental health disorder in the p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Do you currently have a mental health disorder?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Have you been diagnosed with a mental health c...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Have you ever sought treatment for a mental he...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>If you have a mental health issue, do you feel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>If you have a mental health issue, do you feel...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>What is your gender?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>What country do you live in?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>What country do you work in?</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Question\n",
       "0   Does your employer provide mental health benef...\n",
       "1   Do you know the options for mental health care...\n",
       "2   Does your employer offer resources to learn mo...\n",
       "3   If a mental health issue prompted you to reque...\n",
       "4   Do you think that discussing a mental health d...\n",
       "5   Do you think that discussing a physical health...\n",
       "6   Do you feel that your employer takes mental he...\n",
       "7   Have you heard of or observed negative consequ...\n",
       "8                     Do you have previous employers?\n",
       "9   Have your previous employers provided mental h...\n",
       "10  Were you aware of the options for mental healt...\n",
       "11  Did your previous employers ever formally disc...\n",
       "12  Was your anonymity protected if you chose to t...\n",
       "13  Do you think that discussing a mental health d...\n",
       "14  Would you have been willing to discuss a menta...\n",
       "15  Did you feel that your previous employers took...\n",
       "16  Did you hear of or observe negative consequenc...\n",
       "17  Would you bring up a mental health issue with ...\n",
       "18  Do you feel that being identified as a person ...\n",
       "19  Do you think that team members/co-workers woul...\n",
       "20  How willing would you be to share with friends...\n",
       "21  Have you observed or experienced an unsupporti...\n",
       "22    Do you have a family history of mental illness?\n",
       "23  Have you had a mental health disorder in the p...\n",
       "24    Do you currently have a mental health disorder?\n",
       "25  Have you been diagnosed with a mental health c...\n",
       "26  Have you ever sought treatment for a mental he...\n",
       "27  If you have a mental health issue, do you feel...\n",
       "28  If you have a mental health issue, do you feel...\n",
       "29                               What is your gender?\n",
       "30                       What country do you live in?\n",
       "31                       What country do you work in?"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data back into a dataframe from the database\n",
    "questions_df = pd.read_sql_table('pre_encoded_questions', 'sqlite:///mental_health.db').drop(columns=['index'])\n",
    "questions_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the target set.\n",
    "y = mental_df[\"25_Yes\"]\n",
    "\n",
    "# Define the features set.\n",
    "X = mental_df.drop(columns=[\"25_Yes\",\"25_No\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into Train and Test sets.\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a random forest classifier.\n",
    "rf_model = RandomForestClassifier(n_estimators=128, random_state=78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting the model\n",
    "rf_model = rf_model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       1., 1., 0., 1., 0., 0., 1., 0., 1., 1., 1., 0., 1., 1., 0., 1., 0.,\n",
       "       0., 0., 1., 1., 0., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1., 0., 1.,\n",
       "       0., 1., 1., 0., 0., 1., 1., 0., 1., 1., 1., 0., 0., 1., 0., 0., 1.,\n",
       "       0., 1., 1., 0., 1., 1., 1., 0., 1., 0., 0., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 0., 0., 1., 1., 0., 1., 0.])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Making predictions using the testing data.\n",
    "predictions = rf_model.predict(X_test)\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>75</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>7</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           75           23\n",
       "Actual 1            7          111"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating the confusion matrix.\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "\n",
    "# Create a DataFrame from the confusion matrix.\n",
    "cm_df = pd.DataFrame(\n",
    "cm, index=[\"Actual 0\", \"Actual 1\"], columns=[\"Predicted 0\", \"Predicted 1\"])\n",
    "\n",
    "cm_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculating the accuracy score.\n",
    "acc_score = accuracy_score(y_test, predictions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create a Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Predicted 0</th>\n",
       "      <th>Predicted 1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Actual 0</th>\n",
       "      <td>75</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Actual 1</th>\n",
       "      <td>7</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Predicted 0  Predicted 1\n",
       "Actual 0           75           23\n",
       "Actual 1            7          111"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score : 0.8611111111111112\n",
      "Classification Report\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.91      0.77      0.83        98\n",
      "         1.0       0.83      0.94      0.88       118\n",
      "\n",
      "    accuracy                           0.86       216\n",
      "   macro avg       0.87      0.85      0.86       216\n",
      "weighted avg       0.87      0.86      0.86       216\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Displaying results\n",
    "print(\"Confusion Matrix\")\n",
    "display(cm_df)\n",
    "print(f\"Accuracy Score : {acc_score}\")\n",
    "print(\"Classification Report\")\n",
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 1.10419050e-01, 5.67529724e-03, 1.65687097e-03,\n",
       "       3.34889219e-03, 4.77398577e-03, 2.86263014e-03, 6.59232613e-03,\n",
       "       6.87360840e-03, 4.75727324e-03, 4.52849706e-03, 4.32812773e-03,\n",
       "       3.84626568e-03, 2.44265684e-03, 4.74462438e-03, 3.93220193e-03,\n",
       "       1.96594474e-03, 2.45223494e-03, 4.32743555e-03, 4.14851247e-03,\n",
       "       2.76312046e-03, 3.94917584e-03, 3.04315937e-03, 2.26892999e-03,\n",
       "       4.42000131e-03, 3.18407639e-03, 3.68580684e-03, 2.50538604e-03,\n",
       "       1.92557573e-03, 4.96987345e-03, 4.02072982e-03, 7.53026483e-03,\n",
       "       2.36400258e-03, 5.49362358e-03, 6.82871236e-03, 2.98918112e-03,\n",
       "       1.99578370e-03, 1.32254134e-03, 3.64470239e-03, 4.60907129e-03,\n",
       "       8.87053686e-04, 4.09469487e-03, 1.66981912e-03, 3.11731938e-03,\n",
       "       2.70249597e-03, 3.47603523e-03, 2.49857420e-03, 4.73271396e-03,\n",
       "       5.25853416e-03, 1.09213371e-03, 3.55670457e-03, 3.82392394e-03,\n",
       "       1.48931802e-03, 5.92142403e-03, 4.83229807e-03, 5.52944146e-03,\n",
       "       8.67261202e-04, 4.24373462e-03, 3.63790159e-03, 1.33819673e-03,\n",
       "       3.52337157e-03, 4.70457136e-03, 2.27761535e-03, 5.09720601e-03,\n",
       "       1.48013486e-03, 9.74735092e-04, 3.56826688e-03, 1.87431654e-03,\n",
       "       4.61220633e-03, 3.39251757e-03, 2.15908157e-03, 3.97732359e-03,\n",
       "       1.09691524e-03, 2.35067212e-03, 2.62899355e-03, 1.40045065e-03,\n",
       "       3.66390993e-03, 4.88522724e-03, 3.25369720e-03, 4.20801536e-03,\n",
       "       3.92040223e-03, 6.09875180e-03, 3.34012519e-03, 4.31860930e-03,\n",
       "       2.08773044e-02, 1.87398806e-02, 1.58858884e-02, 6.01976677e-02,\n",
       "       1.20248058e-01, 1.38676462e-02, 3.68153955e-02, 8.94888194e-02,\n",
       "       3.26145254e-03, 7.24086890e-02, 1.30968184e-03, 1.52994812e-02,\n",
       "       9.31987753e-03, 2.03765295e-04, 4.37201858e-02, 2.91200630e-02,\n",
       "       4.50932783e-03, 9.23245175e-03, 4.69447728e-03, 4.55127772e-03,\n",
       "       8.88642181e-04, 1.26947644e-04, 1.75915960e-03, 1.15920860e-03,\n",
       "       9.82574596e-04, 2.25969427e-03, 1.62972450e-03, 3.59269743e-03,\n",
       "       9.19225625e-05, 1.22621938e-03, 5.51998884e-04, 1.04972430e-03,\n",
       "       2.70030468e-03, 1.36094931e-03, 1.88350257e-03, 4.21848984e-03])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate feature importance in the Random Forest model.\n",
    "importances = rf_model.feature_importances_\n",
    "importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.12024805797660318, '23_Yes'),\n",
       " (0.11041904981287384, '26'),\n",
       " (0.08948881936577566, '24_Yes'),\n",
       " (0.07240868904721894, '27_Not applicable to me'),\n",
       " (0.060197667718725555, '23_No'),\n",
       " (0.043720185842798506, '28_Not applicable to me'),\n",
       " (0.036815395479663025, '24_No'),\n",
       " (0.02912006295329515, '28_Often'),\n",
       " (0.020877304353417295, '22_No'),\n",
       " (0.01873988058843284, '22_Yes'),\n",
       " (0.01588588843548007, '23_Maybe'),\n",
       " (0.015299481191566024, '27_Rarely'),\n",
       " (0.013867646180869025, '24_Maybe'),\n",
       " (0.009319877532035678, '27_Sometimes'),\n",
       " (0.009232451751315603, '28_Sometimes'),\n",
       " (0.007530264833722061, '9_Some did'),\n",
       " (0.006873608396547313, '1_Yes'),\n",
       " (0.006828712358086473, '10_N/A (not currently aware)'),\n",
       " (0.006592326132076189, '1_No'),\n",
       " (0.006098751799722544, '21_Yes, I experienced'),\n",
       " (0.0059214240286992025, \"15_I don't know\"),\n",
       " (0.005675297236103962, \"0_I don't know\"),\n",
       " (0.005529441456887535, '15_Some did'),\n",
       " (0.005493623584711995, '10_I was aware of some'),\n",
       " (0.005258534156281813, '13_Yes, all of them'),\n",
       " (0.00509720601394967, '18_Maybe'),\n",
       " (0.004969873449539534, \"9_I don't know\"),\n",
       " (0.004885227235971476, '20_Somewhat open'),\n",
       " (0.004832298069130747, '15_None did'),\n",
       " (0.004773985774505891, '0_Yes'),\n",
       " (0.004757273239359274, \"2_I don't know\"),\n",
       " (0.00474462438256302, '3_Somewhat difficult'),\n",
       " (0.004732713963944044, '13_Some of them'),\n",
       " (0.004704571362841303, '17_No'),\n",
       " (0.004694477283394608, '29_Female'),\n",
       " (0.004612206328236454, '19_Maybe'),\n",
       " (0.004609071289895478, '11_Some did'),\n",
       " (0.004551277722747784, '29_Male'),\n",
       " (0.004528497064968191, '2_No'),\n",
       " (0.0045093278315437215, '28_Rarely'),\n",
       " (0.004420001312399554, \"6_I don't know\"),\n",
       " (0.004328127729206886, '2_Yes'),\n",
       " (0.0043274355527788, '4_Maybe'),\n",
       " (0.004318609295670676, \"22_I don't know\"),\n",
       " (0.0042437346194589935, '16_None of them'),\n",
       " (0.004218489837384891, '31_United States of America'),\n",
       " (0.0042080153579229194, '21_Maybe/Not sure'),\n",
       " (0.004148512474691333, '4_No'),\n",
       " (0.00409469487040336, \"12_I don't know\"),\n",
       " (0.004020729815157015, '9_No, none did'),\n",
       " (0.0039773235860317375, '19_Yes, I think they would'),\n",
       " (0.00394917583696326, '5_Maybe'),\n",
       " (0.003932201934965467, '3_Somewhat easy'),\n",
       " (0.003920402225205477, '21_No'),\n",
       " (0.0038462656779453546, \"3_I don't know\"),\n",
       " (0.003823923943166018, '14_Some of my previous employers'),\n",
       " (0.0036858068386194246, '6_Yes'),\n",
       " (0.003663909929799883, '20_Somewhat not open'),\n",
       " (0.0036447023906744334, '11_None did'),\n",
       " (0.0036379015871988584, '16_Some of them'),\n",
       " (0.0035926974332522034, '30_United States of America'),\n",
       " (0.003568266882616158, '18_Yes, I think it would'),\n",
       " (0.003556704572037552, '14_No, at none of my previous employers'),\n",
       " (0.003523371567202977, '17_Maybe'),\n",
       " (0.0034760352302814048, \"13_I don't know\"),\n",
       " (0.003392517573674982, \"19_No, I don't think they would\"),\n",
       " (0.0033488921897075617, '0_Not eligible for coverage / N/A'),\n",
       " (0.0033401251894899205, '21_Yes, I observed'),\n",
       " (0.0032614525361438166, '27_Never'),\n",
       " (0.003253697203449811, '20_Very open'),\n",
       " (0.0031840763922629405, '6_No'),\n",
       " (0.003117319384988977, '12_Sometimes'),\n",
       " (0.0030431593725536847, '5_No'),\n",
       " (0.0029891811172830535, '10_No, I only became aware later'),\n",
       " (0.0028626301448683522, '1_I am not sure'),\n",
       " (0.002763120459132322, '4_Yes'),\n",
       " (0.002702495970587231, '12_Yes, always'),\n",
       " (0.002700304681168638, '31_Other'),\n",
       " (0.002628993547790125,\n",
       "  '20_Not applicable to me (I do not have a mental illness)'),\n",
       " (0.002505386040665268, '7_No'),\n",
       " (0.0024985742000502107, '13_None of them'),\n",
       " (0.002452234941148902, '3_Very easy'),\n",
       " (0.0024426568448041933, '3_Neither easy nor difficult'),\n",
       " (0.0023640025809882367, '9_Yes, they all did'),\n",
       " (0.0023506721234228807, '20_Neutral'),\n",
       " (0.0022776153452677907, '17_Yes'),\n",
       " (0.0022689299937348546, '5_Yes'),\n",
       " (0.002259694271194966, '30_Other'),\n",
       " (0.002159081569693065, '19_No, they do not'),\n",
       " (0.0019957837019981273, '10_Yes, I was aware of all of them'),\n",
       " (0.0019659447362507976, '3_Very difficult'),\n",
       " (0.0019255757266982484, '7_Yes'),\n",
       " (0.00188350257440474, '31_United Kingdom'),\n",
       " (0.0018743165419644787, '18_Yes, it has'),\n",
       " (0.0017591596042759532, '30_Canada'),\n",
       " (0.0016698191172917118, '12_No'),\n",
       " (0.001656870971835973, '0_No'),\n",
       " (0.0016297244960730773, '30_United Kingdom'),\n",
       " (0.001489318016517049, '14_Yes, at all of my previous employers'),\n",
       " (0.001480134864856029, \"18_No, I don't think it would\"),\n",
       " (0.0014004506514670345, '20_Not open at all'),\n",
       " (0.0013609493093240032, '31_Sweden'),\n",
       " (0.001338196729098717, '16_Yes, all of them'),\n",
       " (0.001322541340796772, \"11_I don't know\"),\n",
       " (0.0013096818449635716, '27_Often'),\n",
       " (0.0012262193801065905, '31_Canada'),\n",
       " (0.0011592086038048545, '30_Germany'),\n",
       " (0.0010969152409325876, '19_Yes, they do'),\n",
       " (0.0010921337064618492, \"14_I don't know\"),\n",
       " (0.001049724298985151, '31_Netherlands'),\n",
       " (0.000982574595740978, '30_Netherlands'),\n",
       " (0.0009747350916809387, '18_No, it has not'),\n",
       " (0.0008886421807399982, '29_Other'),\n",
       " (0.0008870536861229455, '11_Yes, they all did'),\n",
       " (0.0008672612024514066, '15_Yes, they all did'),\n",
       " (0.0005519988836541869, '31_Germany'),\n",
       " (0.0002037652947386564, '28_Never'),\n",
       " (0.00012694764367634752, '30_Australia'),\n",
       " (9.192256248014986e-05, '31_Australia'),\n",
       " (0.0, '8')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can sort the features by their importance.\n",
    "sorted(zip(rf_model.feature_importances_, X.columns), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 86.11 %\n"
     ]
    }
   ],
   "source": [
    "# save our model to use later\n",
    "import pickle\n",
    "\n",
    "# Save to file in the current working directory\n",
    "pkl_filename = \"pickle_model.pkl\"\n",
    "with open(pkl_filename, 'wb') as file:\n",
    "    pickle.dump(rf_model, file)\n",
    "\n",
    "# Load from file\n",
    "with open(pkl_filename, 'rb') as file:\n",
    "    pickle_model = pickle.load(file)\n",
    "    \n",
    "# Calculate the accuracy score and predict target values\n",
    "score = pickle_model.score(X_test, y_test)\n",
    "print(\"Test score: {0:.2f} %\".format(100 * score))\n",
    "Ypredict = pickle_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our model’s accuracy score is 86.11, meaning that it accurately predicts if an individual has a mental health disorder 86.11% of the time, based off how they answer survey questions (assuming they answer honestly). Since this model is not making a prediction that has high consequences, it is merely for the interest of an individual working in tech and wanting to know their likelihood of having (or developing) a mental health disorder, or for the interest of a tech company and wanting to know if offering certain mental health services would be of benefit to their employees, an accuracy of 86.11% is sufficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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