Checkpoint 2/checkpoint_2.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "Qx6Na4mfOGJQ"
      },
      "outputs": [],
      "source": [
        "# Import necessary libs\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix\n",
        "from sklearn.model_selection import cross_val_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 782
        },
        "id": "vJMiwmVgOLjZ",
        "outputId": "ee2e2c6a-1737-4610-d930-81d0c6e14f1c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataframe shape - (3883, 15)\n",
            "\n",
            "Unique users within set - 14\n",
            "\n",
            "Types of data within our set:\n",
            "platform.name                           object\n",
            "user.id                                 object\n",
            "task.id                                 object\n",
            "task.monetary_reward_in_dollars        float64\n",
            "task.assignable_hits_count             float64\n",
            "task.requester_name                     object\n",
            "task.estimate_duration_in_seconds        int64\n",
            "total.labor.event_count                  int64\n",
            "total.labor.duration_in_seconds          int64\n",
            "working.labor.event_count                int64\n",
            "working.labor.duration_in_seconds        int64\n",
            "invisible.labor.event_count              int64\n",
            "invisible.labor.duration_in_seconds      int64\n",
            "adjusted.monetary_reward_in_dollars    float64\n",
            "completed_task                           int64\n",
            "dtype: object\n",
            "\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>platform.name</th>\n",
              "      <th>user.id</th>\n",
              "      <th>task.id</th>\n",
              "      <th>task.monetary_reward_in_dollars</th>\n",
              "      <th>task.assignable_hits_count</th>\n",
              "      <th>task.requester_name</th>\n",
              "      <th>task.estimate_duration_in_seconds</th>\n",
              "      <th>total.labor.event_count</th>\n",
              "      <th>total.labor.duration_in_seconds</th>\n",
              "      <th>working.labor.event_count</th>\n",
              "      <th>working.labor.duration_in_seconds</th>\n",
              "      <th>invisible.labor.event_count</th>\n",
              "      <th>invisible.labor.duration_in_seconds</th>\n",
              "      <th>adjusted.monetary_reward_in_dollars</th>\n",
              "      <th>completed_task</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>3W9XHF7WGLV68SQR2YVUGGPI6QVTK3</td>\n",
              "      <td>2.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>HCI Lab</td>\n",
              "      <td>7200</td>\n",
              "      <td>52</td>\n",
              "      <td>326</td>\n",
              "      <td>5</td>\n",
              "      <td>35</td>\n",
              "      <td>47</td>\n",
              "      <td>291</td>\n",
              "      <td>2.090556</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>3KA7IJSNW54MTVXHF3TMHNX8OOTPBI</td>\n",
              "      <td>0.15</td>\n",
              "      <td>1.0</td>\n",
              "      <td>PickFu</td>\n",
              "      <td>720</td>\n",
              "      <td>2</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.156250</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>3UEBBGULPFPBKLKAVLPVYAEF6LSUFW</td>\n",
              "      <td>0.30</td>\n",
              "      <td>23.0</td>\n",
              "      <td>CB Lab</td>\n",
              "      <td>3600</td>\n",
              "      <td>36</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>2</td>\n",
              "      <td>24</td>\n",
              "      <td>29</td>\n",
              "      <td>0.302583</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>3MQY1YVHS35Y79MCYT1FYVKHDBQB2G</td>\n",
              "      <td>2.00</td>\n",
              "      <td>80.0</td>\n",
              "      <td>Tamara Berg</td>\n",
              "      <td>3600</td>\n",
              "      <td>23</td>\n",
              "      <td>45</td>\n",
              "      <td>17</td>\n",
              "      <td>39</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>2.025000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>3E9VAUV7C0KFQYQSLVDQPISUFSBYA9</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>PP Group</td>\n",
              "      <td>14400</td>\n",
              "      <td>53</td>\n",
              "      <td>252</td>\n",
              "      <td>34</td>\n",
              "      <td>87</td>\n",
              "      <td>19</td>\n",
              "      <td>165</td>\n",
              "      <td>0.712250</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>307L9TDWJYSU5X3QAPDSGIUMBKC3ND</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Kirk Wakefield</td>\n",
              "      <td>172800</td>\n",
              "      <td>50</td>\n",
              "      <td>115</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>21</td>\n",
              "      <td>107</td>\n",
              "      <td>0.200133</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>389A2A304OIIICG8DF1T1DF22WGC00</td>\n",
              "      <td>0.50</td>\n",
              "      <td>20.0</td>\n",
              "      <td>MSH University of Grenoble</td>\n",
              "      <td>1800</td>\n",
              "      <td>113</td>\n",
              "      <td>166</td>\n",
              "      <td>21</td>\n",
              "      <td>10</td>\n",
              "      <td>92</td>\n",
              "      <td>156</td>\n",
              "      <td>0.546111</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>3SNR5F7R91STS53HGDQSJBBU3NSIE8</td>\n",
              "      <td>0.20</td>\n",
              "      <td>40.0</td>\n",
              "      <td>SLS-6</td>\n",
              "      <td>1200</td>\n",
              "      <td>34</td>\n",
              "      <td>33</td>\n",
              "      <td>20</td>\n",
              "      <td>14</td>\n",
              "      <td>14</td>\n",
              "      <td>19</td>\n",
              "      <td>0.205500</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>375VMB7D4JK7T05MZHOWSEUJYFJDI0</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.0</td>\n",
              "      <td>PsychLabAtBC</td>\n",
              "      <td>1200</td>\n",
              "      <td>44</td>\n",
              "      <td>201</td>\n",
              "      <td>15</td>\n",
              "      <td>63</td>\n",
              "      <td>29</td>\n",
              "      <td>138</td>\n",
              "      <td>0.583750</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>MTURK</td>\n",
              "      <td>ae862298385abab2a0a1619f8cedef9d</td>\n",
              "      <td>3EQVJH0T40M4UDKGQD8XZ9P98S2THC</td>\n",
              "      <td>0.13</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Tom Kramer</td>\n",
              "      <td>3600</td>\n",
              "      <td>51</td>\n",
              "      <td>52</td>\n",
              "      <td>19</td>\n",
              "      <td>4</td>\n",
              "      <td>32</td>\n",
              "      <td>48</td>\n",
              "      <td>0.131878</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  platform.name                           user.id  \\\n",
              "0         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "1         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "2         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "3         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "4         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "5         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "6         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "7         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "8         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "9         MTURK  ae862298385abab2a0a1619f8cedef9d   \n",
              "\n",
              "                          task.id  task.monetary_reward_in_dollars  \\\n",
              "0  3W9XHF7WGLV68SQR2YVUGGPI6QVTK3                             2.00   \n",
              "1  3KA7IJSNW54MTVXHF3TMHNX8OOTPBI                             0.15   \n",
              "2  3UEBBGULPFPBKLKAVLPVYAEF6LSUFW                             0.30   \n",
              "3  3MQY1YVHS35Y79MCYT1FYVKHDBQB2G                             2.00   \n",
              "4  3E9VAUV7C0KFQYQSLVDQPISUFSBYA9                             0.70   \n",
              "5  307L9TDWJYSU5X3QAPDSGIUMBKC3ND                             0.20   \n",
              "6  389A2A304OIIICG8DF1T1DF22WGC00                             0.50   \n",
              "7  3SNR5F7R91STS53HGDQSJBBU3NSIE8                             0.20   \n",
              "8  375VMB7D4JK7T05MZHOWSEUJYFJDI0                             0.50   \n",
              "9  3EQVJH0T40M4UDKGQD8XZ9P98S2THC                             0.13   \n",
              "\n",
              "   task.assignable_hits_count         task.requester_name  \\\n",
              "0                         1.0                     HCI Lab   \n",
              "1                         1.0                      PickFu   \n",
              "2                        23.0                      CB Lab   \n",
              "3                        80.0                 Tamara Berg   \n",
              "4                         0.0                    PP Group   \n",
              "5                         1.0              Kirk Wakefield   \n",
              "6                        20.0  MSH University of Grenoble   \n",
              "7                        40.0                       SLS-6   \n",
              "8                         0.0                PsychLabAtBC   \n",
              "9                         1.0                  Tom Kramer   \n",
              "\n",
              "   task.estimate_duration_in_seconds  total.labor.event_count  \\\n",
              "0                               7200                       52   \n",
              "1                                720                        2   \n",
              "2                               3600                       36   \n",
              "3                               3600                       23   \n",
              "4                              14400                       53   \n",
              "5                             172800                       50   \n",
              "6                               1800                      113   \n",
              "7                               1200                       34   \n",
              "8                               1200                       44   \n",
              "9                               3600                       51   \n",
              "\n",
              "   total.labor.duration_in_seconds  working.labor.event_count  \\\n",
              "0                              326                          5   \n",
              "1                               30                          2   \n",
              "2                               31                         12   \n",
              "3                               45                         17   \n",
              "4                              252                         34   \n",
              "5                              115                         29   \n",
              "6                              166                         21   \n",
              "7                               33                         20   \n",
              "8                              201                         15   \n",
              "9                               52                         19   \n",
              "\n",
              "   working.labor.duration_in_seconds  invisible.labor.event_count  \\\n",
              "0                                 35                           47   \n",
              "1                                 30                            0   \n",
              "2                                  2                           24   \n",
              "3                                 39                            6   \n",
              "4                                 87                           19   \n",
              "5                                  8                           21   \n",
              "6                                 10                           92   \n",
              "7                                 14                           14   \n",
              "8                                 63                           29   \n",
              "9                                  4                           32   \n",
              "\n",
              "   invisible.labor.duration_in_seconds  adjusted.monetary_reward_in_dollars  \\\n",
              "0                                  291                             2.090556   \n",
              "1                                    0                             0.156250   \n",
              "2                                   29                             0.302583   \n",
              "3                                    6                             2.025000   \n",
              "4                                  165                             0.712250   \n",
              "5                                  107                             0.200133   \n",
              "6                                  156                             0.546111   \n",
              "7                                   19                             0.205500   \n",
              "8                                  138                             0.583750   \n",
              "9                                   48                             0.131878   \n",
              "\n",
              "   completed_task  \n",
              "0               1  \n",
              "1               1  \n",
              "2               0  \n",
              "3               0  \n",
              "4               1  \n",
              "5               1  \n",
              "6               0  \n",
              "7               0  \n",
              "8               1  \n",
              "9               1  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Read dataframe in\n",
        "df = pd.read_csv(\"../data/cloudworker_tasks_adjusted.csv\")\n",
        "\n",
        "# Let's examine the first couple of entries in the dataset\n",
        "print(f'Dataframe shape - {df.shape}\\n')\n",
        "\n",
        "uniqueUsers = df['user.id'].unique()\n",
        "print(f'Unique users within set - { uniqueUsers.size }\\n')\n",
        "print(f'Types of data within our set:\\n{df.dtypes}\\n')\n",
        "df.head(n=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "DkrBtk9bOcdl",
        "outputId": "8debafc6-2949-4d9e-c4bf-115d22cc9d38"
      },
      "outputs": [
        {
          "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>task.monetary_reward_in_dollars</th>\n",
              "      <th>task.assignable_hits_count</th>\n",
              "      <th>task.estimate_duration_in_seconds</th>\n",
              "      <th>total.labor.event_count</th>\n",
              "      <th>total.labor.duration_in_seconds</th>\n",
              "      <th>working.labor.event_count</th>\n",
              "      <th>working.labor.duration_in_seconds</th>\n",
              "      <th>invisible.labor.event_count</th>\n",
              "      <th>invisible.labor.duration_in_seconds</th>\n",
              "      <th>adjusted.monetary_reward_in_dollars</th>\n",
              "      <th>completed_task</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>3883.000000</td>\n",
              "      <td>3883.000000</td>\n",
              "      <td>3.883000e+03</td>\n",
              "      <td>3883.000000</td>\n",
              "      <td>3883.000000</td>\n",
              "      <td>3883.000000</td>\n",
              "      <td>3883.000000</td>\n",
              "      <td>3883.000000</td>\n",
              "      <td>3883.000000</td>\n",
              "      <td>3883.000000</td>\n",
              "      <td>3883.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.399683</td>\n",
              "      <td>1016.451713</td>\n",
              "      <td>9.632288e+03</td>\n",
              "      <td>197.545455</td>\n",
              "      <td>1469.697399</td>\n",
              "      <td>91.851404</td>\n",
              "      <td>360.423384</td>\n",
              "      <td>105.694051</td>\n",
              "      <td>1109.274015</td>\n",
              "      <td>0.610492</td>\n",
              "      <td>0.645892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.896798</td>\n",
              "      <td>2202.351516</td>\n",
              "      <td>4.522715e+04</td>\n",
              "      <td>384.557432</td>\n",
              "      <td>5799.593581</td>\n",
              "      <td>215.920480</td>\n",
              "      <td>1611.744319</td>\n",
              "      <td>234.490680</td>\n",
              "      <td>5446.413915</td>\n",
              "      <td>2.425395</td>\n",
              "      <td>0.478304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000e+01</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-17362.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.080000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.800000e+03</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>77.000000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>33.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>26.500000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.150000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>3.600000e+03</td>\n",
              "      <td>62.000000</td>\n",
              "      <td>236.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>0.153167</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.250000</td>\n",
              "      <td>1405.000000</td>\n",
              "      <td>7.200000e+03</td>\n",
              "      <td>230.000000</td>\n",
              "      <td>1132.500000</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>224.000000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>696.500000</td>\n",
              "      <td>0.265833</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>15.000000</td>\n",
              "      <td>36201.000000</td>\n",
              "      <td>1.814400e+06</td>\n",
              "      <td>11399.000000</td>\n",
              "      <td>81298.000000</td>\n",
              "      <td>10312.000000</td>\n",
              "      <td>67827.000000</td>\n",
              "      <td>5901.000000</td>\n",
              "      <td>78984.000000</td>\n",
              "      <td>98.154167</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       task.monetary_reward_in_dollars  task.assignable_hits_count  \\\n",
              "count                      3883.000000                 3883.000000   \n",
              "mean                          0.399683                 1016.451713   \n",
              "std                           0.896798                 2202.351516   \n",
              "min                           0.000000                    0.000000   \n",
              "25%                           0.080000                    0.000000   \n",
              "50%                           0.150000                   46.000000   \n",
              "75%                           0.250000                 1405.000000   \n",
              "max                          15.000000                36201.000000   \n",
              "\n",
              "       task.estimate_duration_in_seconds  total.labor.event_count  \\\n",
              "count                       3.883000e+03              3883.000000   \n",
              "mean                        9.632288e+03               197.545455   \n",
              "std                         4.522715e+04               384.557432   \n",
              "min                         6.000000e+01                 2.000000   \n",
              "25%                         1.800000e+03                38.000000   \n",
              "50%                         3.600000e+03                62.000000   \n",
              "75%                         7.200000e+03               230.000000   \n",
              "max                         1.814400e+06             11399.000000   \n",
              "\n",
              "       total.labor.duration_in_seconds  working.labor.event_count  \\\n",
              "count                      3883.000000                3883.000000   \n",
              "mean                       1469.697399                  91.851404   \n",
              "std                        5799.593581                 215.920480   \n",
              "min                           8.000000                   2.000000   \n",
              "25%                          77.000000                  24.000000   \n",
              "50%                         236.000000                  42.000000   \n",
              "75%                        1132.500000                  88.000000   \n",
              "max                       81298.000000               10312.000000   \n",
              "\n",
              "       working.labor.duration_in_seconds  invisible.labor.event_count  \\\n",
              "count                        3883.000000                  3883.000000   \n",
              "mean                          360.423384                   105.694051   \n",
              "std                          1611.744319                   234.490680   \n",
              "min                             0.000000                     0.000000   \n",
              "25%                            33.000000                     0.000000   \n",
              "50%                            71.000000                    22.000000   \n",
              "75%                           224.000000                   116.000000   \n",
              "max                         67827.000000                  5901.000000   \n",
              "\n",
              "       invisible.labor.duration_in_seconds  \\\n",
              "count                          3883.000000   \n",
              "mean                           1109.274015   \n",
              "std                            5446.413915   \n",
              "min                          -17362.000000   \n",
              "25%                              26.500000   \n",
              "50%                             111.000000   \n",
              "75%                             696.500000   \n",
              "max                           78984.000000   \n",
              "\n",
              "       adjusted.monetary_reward_in_dollars  completed_task  \n",
              "count                          3883.000000     3883.000000  \n",
              "mean                              0.610492        0.645892  \n",
              "std                               2.425395        0.478304  \n",
              "min                               0.000000        0.000000  \n",
              "25%                               0.100000        0.000000  \n",
              "50%                               0.153167        1.000000  \n",
              "75%                               0.265833        1.000000  \n",
              "max                              98.154167        1.000000  "
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# As we can see we have a dataframe with approximately 3883 entries and just over 15 feautre columns within it\n",
        "# We can see that one of those feature columns is our predictor which is completed_task which is either 0 - did not complete or 1 - did complete\n",
        "# Let's graph some of the feautres and see what they look like\n",
        "\n",
        "# First let's get some more information about our data\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0DbyQtAStc9",
        "outputId": "d8cbc77c-9c71-490e-a6c0-b8c8dc96a820"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataframe shape - (2900, 15)\n",
            "\n"
          ]
        }
      ],
      "source": [
        "def trim_outliers(df, column, k):\n",
        "\n",
        "    Q1 = df[column].quantile(0.25)\n",
        "    Q3 = df[column].quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "    low = Q1 - k * IQR\n",
        "    upper = Q3 + k * IQR\n",
        "\n",
        "    trimmed_df = df[(df[column] >= low) & (df[column] <= upper)]\n",
        "\n",
        "    return trimmed_df\n",
        "\n",
        "# Let's trim accordingly\n",
        "t_df = trim_outliers(df, 'task.monetary_reward_in_dollars', k=2.5)\n",
        "t_df = trim_outliers(t_df, 'task.assignable_hits_count', k=2.5)\n",
        "t_df = trim_outliers(t_df, 'total.labor.event_count', k=2.5)\n",
        "\n",
        "# Let's see the new shape of our dataset\n",
        "print(f'Dataframe shape - {t_df.shape}\\n')\n",
        "\n",
        "# Look's like we were able to trim out 983 entries that we determined to be outliers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "hu1x9l1WW2jf"
      },
      "outputs": [],
      "source": [
        "# Now that our data is trimmed let's put it into a k-nearest neighbors model\n",
        "\n",
        "# Let's split our training and test data with our features\n",
        "x_features = ['task.monetary_reward_in_dollars', 'task.assignable_hits_count', 'total.labor.event_count']\n",
        "y_predictor = 'completed_task'\n",
        "X_train, X_test, y_train, y_test = train_test_split(t_df[x_features], t_df[y_predictor], test_size=0.3, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdqNFYzXXWBU",
        "outputId": "441207fb-2449-4b50-e581-3d2c5c4a3fb7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy: 0.9\n",
            "Precision: 0.9377224199288257\n",
            "Recall: 0.9101899827288429\n",
            "F1-Score:  0.9237510955302366\n"
          ]
        }
      ],
      "source": [
        "# Feature scale\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)\n",
        "\n",
        "# Train the k-NN model\n",
        "k = 3\n",
        "knn_classifier = KNeighborsClassifier(n_neighbors=k)\n",
        "knn_classifier.fit(X_train_scaled, y_train)\n",
        "\n",
        "# Predict\n",
        "y_pred = knn_classifier.predict(X_test_scaled)\n",
        "\n",
        "# Model evaluation\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = metrics.precision_score(y_test, y_pred)\n",
        "recall = metrics.recall_score(y_test, y_pred)\n",
        "f1_score =  metrics.f1_score(y_test, y_pred) # , average='weighted')\n",
        "\n",
        "print(f'Accuracy: {accuracy}')\n",
        "print(\"Precision:\", precision)\n",
        "print(\"Recall:\", recall)\n",
        "print(\"F1-Score: \", f1_score)\n",
        "\n",
        "# new data \n",
        "# Accuracy: 0.9\n",
        "# Precision: 0.9377224199288257\n",
        "# Recall: 0.9101899827288429\n",
        "# F1-Score of SVM:  0.9237510955302366"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "NOOuVifTXg9r",
        "outputId": "b071733f-ebf1-48bf-d25f-1b69d7757427"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Let's get more information about our model\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='BuPu',\n",
        "            xticklabels=['Positive', 'Negative'],\n",
        "            yticklabels=['Positive', 'Negative'])\n",
        "\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted Label')\n",
        "plt.ylabel('True Label')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1qTu55JaWnKW",
        "outputId": "40a44391-0200-4d6c-edb0-f2171148e475"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Did actually completed: [1, 1, 0]\n",
            "Model prediction on completion: [1 1 0]\n",
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/Users/ztraboo/anaconda3/envs/cpsc6300/lib/python3.11/site-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but StandardScaler was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# 3 Cases of Interest\n",
        "\n",
        "# This includes the 3 cases of interests input for features\n",
        "custom_cases_x_test = [[0.50, 0.0, 53],\n",
        "                       [0.15, 3704.0, 99],\n",
        "                       [0.04, 2.0, 581]]\n",
        "\n",
        "# This includes the 3 cases of interest actual value for completetion from the dataset\n",
        "custom_cases_y_test = [[1, 1, 0]]\n",
        "\n",
        "x_new = scaler.transform(custom_cases_x_test)\n",
        "\n",
        "n_pred = knn_classifier.predict(x_new)\n",
        "\n",
        "print(f\"Did actually completed: {custom_cases_y_test[0]}\")\n",
        "print(f\"Model prediction on completion: {n_pred}\\n\")"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}