5_Automatidata_Random_Forest_and_XGB.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtNBZFHO3M7n"
      },
      "source": [
        "# **Automatidata**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rgSbVJvomcVa"
      },
      "source": [
        "# **Random Forest and XGBoost models**\n",
        "\n",
        "**The purpose** of this model is to find ways to generate more revenue for taxi cab drivers.  \n",
        "  \n",
        "**The goal** of this model is to predict whether or not a customer is a generous tipper.  \n",
        "\n",
        "*This Notebook has three parts:*\n",
        "\n",
        "**Part 1:** Ethical considerations\n",
        "* Consider the ethical implications of the request\n",
        "\n",
        "* Should the objective of the model be adjusted?\n",
        "\n",
        "**Part 2:** Feature engineering\n",
        "\n",
        "* Perform feature selection, extraction, and transformation to prepare the data for modeling\n",
        "\n",
        "**Part 3:** Modeling\n",
        "\n",
        "* Build the models, evaluate them, and advise on next steps\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzDjfCSLf6Jq"
      },
      "source": [
        "\n",
        "# **PACE stages**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E5g1A74r0ow_"
      },
      "source": [
        "\n",
        "## **PACE: Plan**\n",
        "\n",
        "Consider the questions:\n",
        "\n",
        "**What are you being asked to do?**\n",
        "\n",
        "Predict if a customer will **not** leave a tip.\n",
        "\n",
        "**What are the ethical implications of the model? What are the consequences of your model making errors?**\n",
        "  *   **What is the likely effect of the model when it predicts a false negative (i.e., when the model says a customer will give a tip, but they actually won't)?**\n",
        "\n",
        "  Drivers who didn't receive tips will probably be upset that the app told them a customer would leave a tip. If it happened often, drivers might not trust the app.\n",
        "  \n",
        "  *   **What is the likely effect of the model when it predicts a false positive (i.e., when the model says a customer will not give a tip, but they actually will)?**\n",
        "\n",
        "  Drivers are unlikely to pick up people who are predicted to not leave tips. Customers will have difficulty finding a taxi that will pick them up, and might get angry at the taxi company. Even when the model is correct, people who can't afford to tip will find it more difficult to get taxis, which limits the accessibility of taxi service to those who pay extra.\n",
        "  \n",
        "  \n",
        "**Do the benefits of such a model outweigh the potential problems?**\n",
        "\n",
        "It's not good to disincentivize drivers from picking up customers. It could also cause a customer backlash. The problems seem to outweigh the benefits.\n",
        "  \n",
        "**Would you proceed with the request to build this model? Why or why not?**\n",
        "\n",
        "No. Effectively limiting equal access to taxis is ethically problematic, and carries a lot of risk.\n",
        "\n",
        "**Can the objective be modified to make it less problematic?**\n",
        "\n",
        "We can build a model that predicts the most generous customers. This could accomplish the goal of helping taxi drivers increase their earnings from tips while preventing the wrongful exclusion of certain people from using taxis.-"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GUUrVKTe4cc5"
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      "source": [
        "*Suppose you were to modify the modeling objective so, instead of predicting people who won't tip at all, you predicted people who are particularly generous&mdash;those who will tip 20% or more? Consider the following questions:*\n",
        "\n",
        "**What features do you need to make this prediction?**\n",
        "\n",
        "Ideally, we'd have behavioral history for each customer, so we could know how much they tipped on previous taxi rides. We'd also want times, dates, and locations of both pickups and dropoffs, estimated fares, and payment method.\n",
        "\n",
        "\n",
        "**What would be the target variable?**\n",
        "\n",
        "The target variable would be a binary variable (1 or 0) that indicates whether or not the customer is expected to tip ≥ 20%.\n",
        "\n",
        "**What metric should you use to evaluate your model? Do you have enough information to decide this now?**\n",
        "\n",
        "This is a supervised learning, classification task. We could use accuracy, precision, recall, F-score, area under the ROC curve, or a number of other metrics. However, we don't have enough information at this time to know which are most appropriate. We need to know the class balance of the target variable."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8Vm3QEfGELS"
      },
      "source": [
        "### **Task 1. Imports and data loading**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "fKhnX2Puf4Bt"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "pd.set_option('display.max_columns', None)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from xgboost import XGBClassifier, plot_importance\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV, train_test_split, PredefinedSplit\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score,\\\n",
        "f1_score, confusion_matrix, ConfusionMatrixDisplay, PrecisionRecallDisplay\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "import pickle"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IeXTZ2tdbALL"
      },
      "source": [
        "There are two dataframes: one containing the original data, the other containing the mean durations, mean distances, and predicted fares from the previous notebook called nyc_preds_means.csv.\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DUc9yUHrYRl0",
        "outputId": "8f8debfd-7082-4d60-a4e1-89c8dd9d8525"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_path_main = '/content/drive/MyDrive/Data Analytics/Main Projects/Automatidata project/2017_Yellow_Taxi_Trip_Data.csv'\n",
        "file_path_prev = '/content/drive/MyDrive/Data Analytics/Main Projects/Automatidata project/Phase 5: Multiple Linear Regression model/nyc_preds_means.csv'"
      ],
      "metadata": {
        "id": "yqzG3_eh16b1"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "5weTXGKqa_iG"
      },
      "outputs": [],
      "source": [
        "df0 = pd.read_csv(file_path_main)\n",
        "\n",
        "nyc_preds_means = pd.read_csv(file_path_prev)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yt7Ce1CJYHKq"
      },
      "source": [
        "Inspecting the first few rows of `df0`:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "DZu6V2rJYHKr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "a9a0c94a-b97f-4819-83d0-8f72224a7cbb"
      },
      "outputs": [
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              "   Unnamed: 0  VendorID    tpep_pickup_datetime   tpep_dropoff_datetime  \\\n",
              "0    24870114         2   03/25/2017 8:55:43 AM   03/25/2017 9:09:47 AM   \n",
              "1    35634249         1   04/11/2017 2:53:28 PM   04/11/2017 3:19:58 PM   \n",
              "2   106203690         1   12/15/2017 7:26:56 AM   12/15/2017 7:34:08 AM   \n",
              "3    38942136         2   05/07/2017 1:17:59 PM   05/07/2017 1:48:14 PM   \n",
              "4    30841670         2  04/15/2017 11:32:20 PM  04/15/2017 11:49:03 PM   \n",
              "\n",
              "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
              "0                6           3.34           1                  N   \n",
              "1                1           1.80           1                  N   \n",
              "2                1           1.00           1                  N   \n",
              "3                1           3.70           1                  N   \n",
              "4                1           4.37           1                  N   \n",
              "\n",
              "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
              "0           100           231             1         13.0    0.0      0.5   \n",
              "1           186            43             1         16.0    0.0      0.5   \n",
              "2           262           236             1          6.5    0.0      0.5   \n",
              "3           188            97             1         20.5    0.0      0.5   \n",
              "4             4           112             2         16.5    0.5      0.5   \n",
              "\n",
              "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \n",
              "0        2.76           0.0                    0.3         16.56  \n",
              "1        4.00           0.0                    0.3         20.80  \n",
              "2        1.45           0.0                    0.3          8.75  \n",
              "3        6.39           0.0                    0.3         27.69  \n",
              "4        0.00           0.0                    0.3         17.80  "
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              "\n",
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\"improvement_surcharge\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015672737641519597,\n        \"min\": -0.3,\n        \"max\": 0.3,\n        \"samples\": [\n          0.3,\n          -0.3\n        ],\n        \"num_unique_values\": 3,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total_amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.097295300636276,\n        \"min\": -120.3,\n        \"max\": 1200.29,\n        \"samples\": [\n          10.14,\n          22.77\n        ],\n        \"num_unique_values\": 1369,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df0.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kLr4ckCNYHKs"
      },
      "source": [
        "Inspecting the first few rows of `nyc_preds_means`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "AYxtF2f-YHKs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "2b3aeffb-7a73-4215-9ec8-0f09a5c3122d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_duration  mean_distance  predicted_fare\n",
              "0      22.847222       3.521667       16.434245\n",
              "1      24.470370       3.108889       16.052218\n",
              "2       7.250000       0.881429        7.053706\n",
              "3      30.250000       3.700000       18.731650\n",
              "4      14.616667       4.435000       15.845642"
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_duration</th>\n",
              "      <th>mean_distance</th>\n",
              "      <th>predicted_fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>22.847222</td>\n",
              "      <td>3.521667</td>\n",
              "      <td>16.434245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24.470370</td>\n",
              "      <td>3.108889</td>\n",
              "      <td>16.052218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.250000</td>\n",
              "      <td>0.881429</td>\n",
              "      <td>7.053706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30.250000</td>\n",
              "      <td>3.700000</td>\n",
              "      <td>18.731650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
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              "\n",
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              "type": "dataframe",
              "variable_name": "nyc_preds_means",
              "summary": "{\n  \"name\": \"nyc_preds_means\",\n  \"rows\": 22699,\n  \"fields\": [\n    {\n      \"column\": \"mean_duration\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.080912842210125,\n        \"min\": 0.0,\n        \"max\": 88.78333333333333,\n        \"samples\": [\n          42.05,\n          34.45,\n          26.333333333333336\n        ],\n        \"num_unique_values\": 3374,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean_distance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.558992738326464,\n        \"min\": 0.0,\n        \"max\": 33.92,\n        \"samples\": [\n          1.119,\n          13.25,\n          0.01\n        ],\n        \"num_unique_values\": 2734,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.7061261674869,\n        \"min\": 2.9344941189205027,\n        \"max\": 89.17105003744787,\n        \"samples\": [\n          11.353339182782548,\n          8.07530265484944,\n          8.063223043885044\n        ],\n        \"num_unique_values\": 12148,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "nyc_preds_means.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xPWjFqQYHKs"
      },
      "source": [
        "#### Join the two dataframes\n",
        "\n",
        "Joining the two dataframes:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "NQ4Nq2FXYHKs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "d0ae6560-e58d-4a48-ea1f-b0993145becb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  VendorID    tpep_pickup_datetime   tpep_dropoff_datetime  \\\n",
              "0    24870114         2   03/25/2017 8:55:43 AM   03/25/2017 9:09:47 AM   \n",
              "1    35634249         1   04/11/2017 2:53:28 PM   04/11/2017 3:19:58 PM   \n",
              "2   106203690         1   12/15/2017 7:26:56 AM   12/15/2017 7:34:08 AM   \n",
              "3    38942136         2   05/07/2017 1:17:59 PM   05/07/2017 1:48:14 PM   \n",
              "4    30841670         2  04/15/2017 11:32:20 PM  04/15/2017 11:49:03 PM   \n",
              "\n",
              "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
              "0                6           3.34           1                  N   \n",
              "1                1           1.80           1                  N   \n",
              "2                1           1.00           1                  N   \n",
              "3                1           3.70           1                  N   \n",
              "4                1           4.37           1                  N   \n",
              "\n",
              "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
              "0           100           231             1         13.0    0.0      0.5   \n",
              "1           186            43             1         16.0    0.0      0.5   \n",
              "2           262           236             1          6.5    0.0      0.5   \n",
              "3           188            97             1         20.5    0.0      0.5   \n",
              "4             4           112             2         16.5    0.5      0.5   \n",
              "\n",
              "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
              "0        2.76           0.0                    0.3         16.56   \n",
              "1        4.00           0.0                    0.3         20.80   \n",
              "2        1.45           0.0                    0.3          8.75   \n",
              "3        6.39           0.0                    0.3         27.69   \n",
              "4        0.00           0.0                    0.3         17.80   \n",
              "\n",
              "   mean_duration  mean_distance  predicted_fare  \n",
              "0      22.847222       3.521667       16.434245  \n",
              "1      24.470370       3.108889       16.052218  \n",
              "2       7.250000       0.881429        7.053706  \n",
              "3      30.250000       3.700000       18.731650  \n",
              "4      14.616667       4.435000       15.845642  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "      <th>mean_duration</th>\n",
              "      <th>mean_distance</th>\n",
              "      <th>predicted_fare</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24870114</td>\n",
              "      <td>2</td>\n",
              "      <td>03/25/2017 8:55:43 AM</td>\n",
              "      <td>03/25/2017 9:09:47 AM</td>\n",
              "      <td>6</td>\n",
              "      <td>3.34</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>100</td>\n",
              "      <td>231</td>\n",
              "      <td>1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>16.56</td>\n",
              "      <td>22.847222</td>\n",
              "      <td>3.521667</td>\n",
              "      <td>16.434245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35634249</td>\n",
              "      <td>1</td>\n",
              "      <td>04/11/2017 2:53:28 PM</td>\n",
              "      <td>04/11/2017 3:19:58 PM</td>\n",
              "      <td>1</td>\n",
              "      <td>1.80</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>20.80</td>\n",
              "      <td>24.470370</td>\n",
              "      <td>3.108889</td>\n",
              "      <td>16.052218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>106203690</td>\n",
              "      <td>1</td>\n",
              "      <td>12/15/2017 7:26:56 AM</td>\n",
              "      <td>12/15/2017 7:34:08 AM</td>\n",
              "      <td>1</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>262</td>\n",
              "      <td>236</td>\n",
              "      <td>1</td>\n",
              "      <td>6.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.45</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.250000</td>\n",
              "      <td>0.881429</td>\n",
              "      <td>7.053706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38942136</td>\n",
              "      <td>2</td>\n",
              "      <td>05/07/2017 1:17:59 PM</td>\n",
              "      <td>05/07/2017 1:48:14 PM</td>\n",
              "      <td>1</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
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              "      <td>20.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>6.39</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>27.69</td>\n",
              "      <td>30.250000</td>\n",
              "      <td>3.700000</td>\n",
              "      <td>18.731650</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30841670</td>\n",
              "      <td>2</td>\n",
              "      <td>04/15/2017 11:32:20 PM</td>\n",
              "      <td>04/15/2017 11:49:03 PM</td>\n",
              "      <td>1</td>\n",
              "      <td>4.37</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>4</td>\n",
              "      <td>112</td>\n",
              "      <td>2</td>\n",
              "      <td>16.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>17.80</td>\n",
              "      <td>14.616667</td>\n",
              "      <td>4.435000</td>\n",
              "      <td>15.845642</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-3f423f97-3710-4adc-860b-aee10a371912 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3f423f97-3710-4adc-860b-aee10a371912');\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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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "    </g>\n",
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              "  </button>\n",
              "\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
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              "      --hover-bg-color: #434B5C;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df0"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df0 = df0.merge(nyc_preds_means,\n",
        "                left_index=True,\n",
        "                right_index=True)\n",
        "\n",
        "df0.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VZowX9rhU1o"
      },
      "source": [
        "### **Task 2. Feature engineering**\n",
        "\n",
        "Much of this data has already been processed in previous notebooks.\n",
        "\n",
        "Calling `info()` on the new combined dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "mBOSW8IDbO_d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14a2d79b-ce48-4bb0-9340-885da43a0ec8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 22699 entries, 0 to 22698\n",
            "Data columns (total 21 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Unnamed: 0             22699 non-null  int64  \n",
            " 1   VendorID               22699 non-null  int64  \n",
            " 2   tpep_pickup_datetime   22699 non-null  object \n",
            " 3   tpep_dropoff_datetime  22699 non-null  object \n",
            " 4   passenger_count        22699 non-null  int64  \n",
            " 5   trip_distance          22699 non-null  float64\n",
            " 6   RatecodeID             22699 non-null  int64  \n",
            " 7   store_and_fwd_flag     22699 non-null  object \n",
            " 8   PULocationID           22699 non-null  int64  \n",
            " 9   DOLocationID           22699 non-null  int64  \n",
            " 10  payment_type           22699 non-null  int64  \n",
            " 11  fare_amount            22699 non-null  float64\n",
            " 12  extra                  22699 non-null  float64\n",
            " 13  mta_tax                22699 non-null  float64\n",
            " 14  tip_amount             22699 non-null  float64\n",
            " 15  tolls_amount           22699 non-null  float64\n",
            " 16  improvement_surcharge  22699 non-null  float64\n",
            " 17  total_amount           22699 non-null  float64\n",
            " 18  mean_duration          22699 non-null  float64\n",
            " 19  mean_distance          22699 non-null  float64\n",
            " 20  predicted_fare         22699 non-null  float64\n",
            "dtypes: float64(11), int64(7), object(3)\n",
            "memory usage: 3.6+ MB\n"
          ]
        }
      ],
      "source": [
        "df0.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0D2RvXk0kwsx"
      },
      "source": [
        "It's known from EDA that customers who pay cash generally have a tip amount of $0. To meet the modeling objective, it's needed to sample the data to select only the customers who pay with credit card.\n",
        "\n",
        "Copying `df0` and assign the result to a variable called `df1`. Then, using a Boolean mask to filter `df1` so it contains only customers who paid with credit card:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "_pmNd78plQYr"
      },
      "outputs": [],
      "source": [
        "df1 = df0[df0[\"payment_type\"]==1].copy()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcYudtSYyMcZ"
      },
      "source": [
        "##### **Target**\n",
        "\n",
        "Notice that there isn't a column that indicates tip percent, which is what the model's target variable should be.\n",
        "\n",
        "We are adding a `tip_percent` column to the dataframe by performing the following calculation:  \n",
        "<br/>  \n",
        "\n",
        "\n",
        "$$tip\\ percent = \\frac{tip\\ amount}{total\\ amount - tip\\ amount}$$  \n",
        "\n",
        "Round the result to three places beyond the decimal. **This is an important step.** It affects how many customers are labeled as generous tippers. In fact, without performing this step, approximately 1,800 people who do tip ≥ 20% would be labeled as not generous.\n",
        "\n",
        "To understand why, you must consider how floats work. Computers make their calculations using floating-point arithmetic (hence the word \"float\"). Floating-point arithmetic is a system that allows computers to express both very large numbers and very small numbers with a high degree of precision, encoded in binary. However, precision is limited by the number of bits used to represent a number, which is generally 32 or 64, depending on the capabilities of your operating system.\n",
        "\n",
        "This comes with limitations in that sometimes calculations that should result in clean, precise values end up being encoded as very long decimals. Take, for example, the following calculation:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "awAP4bhuYHKu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dce1d6c7-516e-42f5-cd6f-451d69f16481"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.3000000000000003"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Example\n",
        "1.1 + 2.2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w0oDXLXnYHKu"
      },
      "source": [
        "Notice the three that is 16 places to the right of the decimal. As a consequence, if you were to then have a step in your code that identifies values ≤ 3.3, this would not be included in the result. Therefore, whenever you perform a calculation to compute a number that is then used to make an important decision or filtration, round the number. How many degrees of precision you round to is your decision, which should be based on your use case.\n",
        "\n",
        "Refer to this [guide for more information related to floating-point arithmetic](https://floating-point-gui.de/formats/fp/).  \n",
        "Refer to this [guide for more information related to fixed-point arithmetic](https://inst.eecs.berkeley.edu/~cs61c/sp06/handout/fixedpt.html), which is an alternative to floating-point arithmetic used in certain cases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "guanzJd8zBla"
      },
      "outputs": [],
      "source": [
        "df1[\"tip_percent\"] = round(  df1[\"tip_amount\"] /(df1[\"total_amount\"] - df1[\"tip_amount\"])    ,3)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bqb-SWfs-8Xn"
      },
      "source": [
        "Creating another column called `generous`. This will be the target variable. The column should be a binary indicator of whether or not a customer tipped ≥ 20% (0=no, 1=yes):"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "nqDSe0DSGwhB"
      },
      "outputs": [],
      "source": [
        "df1[\"generous\"] = np.where(df1[\"tip_percent\"]>=0.2,1,0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S92In2yuYHKv"
      },
      "source": [
        "#### Create day column"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H27zUVIlkaxA"
      },
      "source": [
        "Converting the `tpep_pickup_datetime` and `tpep_dropoff_datetime` columns to datetime:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "OIycxWBMkafJ"
      },
      "outputs": [],
      "source": [
        "df1['tpep_pickup_datetime'] = pd.to_datetime(df1['tpep_pickup_datetime'], format='%m/%d/%Y %I:%M:%S %p')\n",
        "df1['tpep_dropoff_datetime'] = pd.to_datetime(df1['tpep_dropoff_datetime'], format='%m/%d/%Y %I:%M:%S %p')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zpcM4FvNyPFY"
      },
      "source": [
        "Creating a `day` column that contains only the day of the week when each passenger was picked up. Then, convert the values to lowercase."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "abUvtMaYyWpD"
      },
      "outputs": [],
      "source": [
        "df1[\"day\"] = df1['tpep_pickup_datetime'].dt.day_name().str.lower()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d1riXVw8YHKw"
      },
      "source": [
        "#### Create time of day columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HwslVt8Hpu7x"
      },
      "source": [
        "Engineering four new columns that represent time of day bins. Each column should contain binary values (0=no, 1=yes) that indicate whether a trip began (picked up) during the following times:\n",
        "\n",
        "`am_rush` = [06:00&ndash;10:00)  \n",
        "`daytime` = [10:00&ndash;16:00)  \n",
        "`pm_rush` = [16:00&ndash;20:00)  \n",
        "`nighttime` = [20:00&ndash;06:00)  \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "x8LFySUyprau"
      },
      "outputs": [],
      "source": [
        "# Create 'am_rush' col\n",
        "df1[\"am_rush\"] = df1['tpep_pickup_datetime'].dt.hour.between(6,9,inclusive=\"both\").astype(int)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create 'daytime' col\n",
        "df1[\"day_time\"] = df1['tpep_pickup_datetime'].dt.hour.between(10,15,inclusive=\"both\").astype(int)"
      ],
      "metadata": {
        "id": "riHDd_OZFYcJ"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create 'pm_rush' col\n",
        "df1[\"pm_rush\"] = df1['tpep_pickup_datetime'].dt.hour.between(16,19,inclusive=\"both\").astype(int)"
      ],
      "metadata": {
        "id": "ao05RVMCFZh1"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create 'nighttime' col\n",
        "df1[\"nighttime\"] = ((df1['tpep_pickup_datetime'].dt.hour >= 20) | (df1['tpep_pickup_datetime'].dt.hour < 6)).astype(int)"
      ],
      "metadata": {
        "id": "M-Y0NADGFbqA"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1[[\"day_time\",\"am_rush\",\"nighttime\",\"pm_rush\"]].sum(numeric_only=True,axis=0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rl_qyI04Ge9B",
        "outputId": "e59e2a73-1b0b-4909-f01d-dab35b5d71de"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "day_time     4288\n",
              "am_rush      2480\n",
              "nighttime    4959\n",
              "pm_rush      3538\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CtS7t9C7YHKz"
      },
      "source": [
        "#### Create `month` column"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VrUmDy8U28bs"
      },
      "source": [
        "Creating a `month` column that contains only the abbreviated name of the month when each passenger was picked up, then convert the result to lowercase:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "g0rbRuZaYHK0"
      },
      "outputs": [],
      "source": [
        "df1[\"month\"] = df1['tpep_pickup_datetime'].dt.strftime('%b').str.lower()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qWbNVbngihE6"
      },
      "source": [
        "Examining the first five rows of your dataframe:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "jWxemeyl4vwQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "a4f559b7-9a39-4ba0-ccc7-bad88c05c2cd"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  VendorID tpep_pickup_datetime tpep_dropoff_datetime  \\\n",
              "0    24870114         2  2017-03-25 08:55:43   2017-03-25 09:09:47   \n",
              "1    35634249         1  2017-04-11 14:53:28   2017-04-11 15:19:58   \n",
              "2   106203690         1  2017-12-15 07:26:56   2017-12-15 07:34:08   \n",
              "3    38942136         2  2017-05-07 13:17:59   2017-05-07 13:48:14   \n",
              "5    23345809         2  2017-03-25 20:34:11   2017-03-25 20:42:11   \n",
              "\n",
              "   passenger_count  trip_distance  RatecodeID store_and_fwd_flag  \\\n",
              "0                6           3.34           1                  N   \n",
              "1                1           1.80           1                  N   \n",
              "2                1           1.00           1                  N   \n",
              "3                1           3.70           1                  N   \n",
              "5                6           2.30           1                  N   \n",
              "\n",
              "   PULocationID  DOLocationID  payment_type  fare_amount  extra  mta_tax  \\\n",
              "0           100           231             1         13.0    0.0      0.5   \n",
              "1           186            43             1         16.0    0.0      0.5   \n",
              "2           262           236             1          6.5    0.0      0.5   \n",
              "3           188            97             1         20.5    0.0      0.5   \n",
              "5           161           236             1          9.0    0.5      0.5   \n",
              "\n",
              "   tip_amount  tolls_amount  improvement_surcharge  total_amount  \\\n",
              "0        2.76           0.0                    0.3         16.56   \n",
              "1        4.00           0.0                    0.3         20.80   \n",
              "2        1.45           0.0                    0.3          8.75   \n",
              "3        6.39           0.0                    0.3         27.69   \n",
              "5        2.06           0.0                    0.3         12.36   \n",
              "\n",
              "   mean_duration  mean_distance  predicted_fare  tip_percent  generous  \\\n",
              "0      22.847222       3.521667       16.434245        0.200         1   \n",
              "1      24.470370       3.108889       16.052218        0.238         1   \n",
              "2       7.250000       0.881429        7.053706        0.199         0   \n",
              "3      30.250000       3.700000       18.731650        0.300         1   \n",
              "5      11.855376       2.052258       10.441351        0.200         1   \n",
              "\n",
              "   am_rush  day_time  pm_rush  nighttime month  \n",
              "0        1         0        0          0   mar  \n",
              "1        0         1        0          0   apr  \n",
              "2        1         0        0          0   dec  \n",
              "3        0         1        0          0   may  \n",
              "5        0         0        0          1   mar  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f89137d6-40b1-4e0a-80c6-52997de033e9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>VendorID</th>\n",
              "      <th>tpep_pickup_datetime</th>\n",
              "      <th>tpep_dropoff_datetime</th>\n",
              "      <th>passenger_count</th>\n",
              "      <th>trip_distance</th>\n",
              "      <th>RatecodeID</th>\n",
              "      <th>store_and_fwd_flag</th>\n",
              "      <th>PULocationID</th>\n",
              "      <th>DOLocationID</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>fare_amount</th>\n",
              "      <th>extra</th>\n",
              "      <th>mta_tax</th>\n",
              "      <th>tip_amount</th>\n",
              "      <th>tolls_amount</th>\n",
              "      <th>improvement_surcharge</th>\n",
              "      <th>total_amount</th>\n",
              "      <th>mean_duration</th>\n",
              "      <th>mean_distance</th>\n",
              "      <th>predicted_fare</th>\n",
              "      <th>tip_percent</th>\n",
              "      <th>generous</th>\n",
              "      <th>am_rush</th>\n",
              "      <th>day_time</th>\n",
              "      <th>pm_rush</th>\n",
              "      <th>nighttime</th>\n",
              "      <th>month</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>24870114</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-03-25 08:55:43</td>\n",
              "      <td>2017-03-25 09:09:47</td>\n",
              "      <td>6</td>\n",
              "      <td>3.34</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>100</td>\n",
              "      <td>231</td>\n",
              "      <td>1</td>\n",
              "      <td>13.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.76</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>16.56</td>\n",
              "      <td>22.847222</td>\n",
              "      <td>3.521667</td>\n",
              "      <td>16.434245</td>\n",
              "      <td>0.200</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>mar</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35634249</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-04-11 14:53:28</td>\n",
              "      <td>2017-04-11 15:19:58</td>\n",
              "      <td>1</td>\n",
              "      <td>1.80</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>186</td>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>16.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>4.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>20.80</td>\n",
              "      <td>24.470370</td>\n",
              "      <td>3.108889</td>\n",
              "      <td>16.052218</td>\n",
              "      <td>0.238</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>apr</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>106203690</td>\n",
              "      <td>1</td>\n",
              "      <td>2017-12-15 07:26:56</td>\n",
              "      <td>2017-12-15 07:34:08</td>\n",
              "      <td>1</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>262</td>\n",
              "      <td>236</td>\n",
              "      <td>1</td>\n",
              "      <td>6.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.45</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>8.75</td>\n",
              "      <td>7.250000</td>\n",
              "      <td>0.881429</td>\n",
              "      <td>7.053706</td>\n",
              "      <td>0.199</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>dec</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>38942136</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-05-07 13:17:59</td>\n",
              "      <td>2017-05-07 13:48:14</td>\n",
              "      <td>1</td>\n",
              "      <td>3.70</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>188</td>\n",
              "      <td>97</td>\n",
              "      <td>1</td>\n",
              "      <td>20.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>6.39</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>27.69</td>\n",
              "      <td>30.250000</td>\n",
              "      <td>3.700000</td>\n",
              "      <td>18.731650</td>\n",
              "      <td>0.300</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>may</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>23345809</td>\n",
              "      <td>2</td>\n",
              "      <td>2017-03-25 20:34:11</td>\n",
              "      <td>2017-03-25 20:42:11</td>\n",
              "      <td>6</td>\n",
              "      <td>2.30</td>\n",
              "      <td>1</td>\n",
              "      <td>N</td>\n",
              "      <td>161</td>\n",
              "      <td>236</td>\n",
              "      <td>1</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2.06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.3</td>\n",
              "      <td>12.36</td>\n",
              "      <td>11.855376</td>\n",
              "      <td>2.052258</td>\n",
              "      <td>10.441351</td>\n",
              "      <td>0.200</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>mar</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f89137d6-40b1-4e0a-80c6-52997de033e9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f89137d6-40b1-4e0a-80c6-52997de033e9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f89137d6-40b1-4e0a-80c6-52997de033e9');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-afbcb00b-5550-4bed-a742-d678bd19e2d7\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-afbcb00b-5550-4bed-a742-d678bd19e2d7')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-afbcb00b-5550-4bed-a742-d678bd19e2d7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df1"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "df1.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P2A__mnxYHK0"
      },
      "source": [
        "#### Drop columns\n",
        "\n",
        "Dropping redundant and irrelevant columns as well as those that would not be available when the model is deployed. This includes information like payment type, trip distance, tip amount, tip percentage, total amount, toll amount, etc. The target variable (`generous`) must remain in the data because it will get isolated as the `y` data for modeling:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "rLrAL2V9YHK0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3bbbe562-9eb5-4b60-8bea-b996b40d57c7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 15265 entries, 0 to 22698\n",
            "Data columns (total 14 columns):\n",
            " #   Column           Non-Null Count  Dtype  \n",
            "---  ------           --------------  -----  \n",
            " 0   VendorID         15265 non-null  int64  \n",
            " 1   passenger_count  15265 non-null  int64  \n",
            " 2   RatecodeID       15265 non-null  int64  \n",
            " 3   PULocationID     15265 non-null  int64  \n",
            " 4   DOLocationID     15265 non-null  int64  \n",
            " 5   mean_duration    15265 non-null  float64\n",
            " 6   mean_distance    15265 non-null  float64\n",
            " 7   predicted_fare   15265 non-null  float64\n",
            " 8   generous         15265 non-null  int64  \n",
            " 9   am_rush          15265 non-null  int64  \n",
            " 10  day_time         15265 non-null  int64  \n",
            " 11  pm_rush          15265 non-null  int64  \n",
            " 12  nighttime        15265 non-null  int64  \n",
            " 13  month            15265 non-null  object \n",
            "dtypes: float64(3), int64(10), object(1)\n",
            "memory usage: 1.7+ MB\n"
          ]
        }
      ],
      "source": [
        "drop_cols = ['Unnamed: 0', 'tpep_pickup_datetime', 'tpep_dropoff_datetime',\n",
        "             'payment_type', 'trip_distance', 'store_and_fwd_flag',\n",
        "             'fare_amount', 'extra', 'mta_tax', 'tip_amount', 'tolls_amount',\n",
        "             'improvement_surcharge', 'total_amount', 'tip_percent']\n",
        "\n",
        "df1 = df1.drop(drop_cols, axis=1)\n",
        "df1.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BBeEjw8TYHK1"
      },
      "source": [
        "#### Variable encoding"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BVs01W-Iitu7"
      },
      "source": [
        "Many of the columns are categorical and will need to be dummied (converted to binary). Some of these columns are numeric, but they actually encode categorical information, such as `RatecodeID` and the pickup and dropoff locations. To make these columns recognizable to the `get_dummies()` function as categorical variables, you'll first need to convert them to `type(str)`.\n",
        "\n",
        "1. Define a variable called `cols_to_str`, which is a list of the numeric columns that contain categorical information and must be converted to string: `RatecodeID`, `PULocationID`, `DOLocationID`.\n",
        "2. Write a for loop that converts each column in `cols_to_str` to string.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "FbB4AfATHqjC"
      },
      "outputs": [],
      "source": [
        "# 1. Define list of cols to convert to string\n",
        "cols_to_str = ['RatecodeID', 'PULocationID', 'DOLocationID', 'VendorID']\n",
        "\n",
        "# 2. Convert each column to string\n",
        "for col in cols_to_str:\n",
        "    df1[col] = df1[col].astype('str')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_5Ubw8O1pKRO"
      },
      "source": [
        "Now convert all the categorical columns to binary.\n",
        "\n",
        "1. Call `get_dummies()` on the dataframe and assign the results back to a new dataframe called `df2`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "H94yLzUMHqgB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1e4a0664-8731-40fb-c64b-a22b4d50cf25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 15265 entries, 0 to 22698\n",
            "Columns: 341 entries, passenger_count to month_sep\n",
            "dtypes: float64(3), int64(6), uint8(332)\n",
            "memory usage: 6.0 MB\n"
          ]
        }
      ],
      "source": [
        "df2 = pd.get_dummies(df1, drop_first=True)\n",
        "df2.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ch-4mMKvzTPO",
        "outputId": "a180de83-f2fc-4d11-fbc1-0c63c3e16f65"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['passenger_count', 'mean_duration', 'mean_distance', 'predicted_fare',\n",
              "       'generous', 'am_rush', 'day_time', 'pm_rush', 'nighttime', 'VendorID_2',\n",
              "       ...\n",
              "       'month_dec', 'month_feb', 'month_jan', 'month_jul', 'month_jun',\n",
              "       'month_mar', 'month_may', 'month_nov', 'month_oct', 'month_sep'],\n",
              "      dtype='object', length=341)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nZfNE37b-LlJ"
      },
      "source": [
        "##### Evaluation metric\n",
        "\n",
        "Before modeling, we must decide on an evaluation metric.\n",
        "\n",
        "1. Examining the class balance of your target variable:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "4mRefXCF-K_c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3fa98aa5-6bdd-4006-bef7-000fa51c4d4e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    0.526368\n",
              "0    0.473632\n",
              "Name: generous, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df2[\"generous\"].value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TjgkLrOf_OrE"
      },
      "source": [
        "A little over half of the customers in this dataset were \"generous\" (tipped ≥ 20%). The dataset is very nearly balanced.\n",
        "\n",
        "To determine a metric, consider the cost of both kinds of model error:\n",
        "* False positives (the model predicts a tip ≥ 20%, but the customer does not give one)\n",
        "* False negatives (the model predicts a tip < 20%, but the customer gives more)\n",
        "\n",
        "False positives are worse for cab drivers, because they would pick up a customer expecting a good tip and then not receive one, frustrating the driver.\n",
        "\n",
        "False negatives are worse for customers, because a cab driver would likely pick up a different customer who was predicted to tip more&mdash;even when the original customer would have tipped generously.\n",
        "\n",
        "**The stakes are relatively even. We want to help taxi drivers make more money, but we don't want this to anger customers. The metric should weigh both precision and recall equally. Which metric is this?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5RWa6wbYHK2"
      },
      "source": [
        "F<sub>1</sub> score is the metric that places equal weight on true postives and false positives, and so therefore on precision and recall."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3n1eikFh8akS"
      },
      "source": [
        "## **PACE: Construct**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G5jzGjOS8iiv"
      },
      "source": [
        "### **Task 3. Modeling**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nx41bVxX89Fe"
      },
      "source": [
        "##### **Split the data**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "qLbapbSWDUL-"
      },
      "outputs": [],
      "source": [
        "X = df2.drop([\"generous\"], axis = 1)\n",
        "y = df2[\"generous\"]\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,stratify=y,random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(X_test.columns) == len(X_train.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UNFS61hYzbam",
        "outputId": "07fdf3f9-cc4b-4ae1-ce5b-15cc7455f2eb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vynZs5het1b_"
      },
      "source": [
        "##### **Random forest**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "XIyZU6mCYHK3"
      },
      "outputs": [],
      "source": [
        "# 1. Instantiate the random forest classifier\n",
        "rf = RandomForestClassifier(random_state=42)\n",
        "\n",
        "# 2. Create a dictionary of hyperparameters to tune\n",
        "cv_params = {'max_depth': [None],\n",
        "             'max_features': [1.0],\n",
        "             'max_samples': [0.7],\n",
        "             'min_samples_leaf': [1],\n",
        "             'min_samples_split': [2],\n",
        "             'n_estimators': [300]\n",
        "             }\n",
        "\n",
        "\n",
        "# 3. Define a dictionary of scoring metrics to capture\n",
        "scoring = {'accuracy', 'precision', 'recall', 'f1'}\n",
        "\n",
        "# 4. Instantiate the GridSearchCV object\n",
        "rf_cv = GridSearchCV(rf, cv_params, scoring=scoring, cv=4, refit='f1')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "OXuBiTGi5ZHn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "outputId": "a40a3bc1-2d5d-4fc5-8fdf-d7e5675d8cc1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 5min 21s, sys: 257 ms, total: 5min 21s\n",
            "Wall time: 5min 25s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=4, estimator=RandomForestClassifier(random_state=42),\n",
              "             param_grid={'max_depth': [None], 'max_features': [1.0],\n",
              "                         'max_samples': [0.7], 'min_samples_leaf': [1],\n",
              "                         'min_samples_split': [2], 'n_estimators': [300]},\n",
              "             refit='f1', scoring={'accuracy', 'precision', 'recall', 'f1'})"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=4, estimator=RandomForestClassifier(random_state=42),\n",
              "             param_grid={&#x27;max_depth&#x27;: [None], &#x27;max_features&#x27;: [1.0],\n",
              "                         &#x27;max_samples&#x27;: [0.7], &#x27;min_samples_leaf&#x27;: [1],\n",
              "                         &#x27;min_samples_split&#x27;: [2], &#x27;n_estimators&#x27;: [300]},\n",
              "             refit=&#x27;f1&#x27;, scoring={&#x27;accuracy&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;, &#x27;f1&#x27;})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=4, estimator=RandomForestClassifier(random_state=42),\n",
              "             param_grid={&#x27;max_depth&#x27;: [None], &#x27;max_features&#x27;: [1.0],\n",
              "                         &#x27;max_samples&#x27;: [0.7], &#x27;min_samples_leaf&#x27;: [1],\n",
              "                         &#x27;min_samples_split&#x27;: [2], &#x27;n_estimators&#x27;: [300]},\n",
              "             refit=&#x27;f1&#x27;, scoring={&#x27;accuracy&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;, &#x27;f1&#x27;})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "%%time\n",
        "#rf_cv.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5wHi_YJduQOH"
      },
      "source": [
        "If you get a warning that a metric is 0 due to no predicted samples, think about how many features you're sampling with `max_features`. How many features are in the dataset? How many are likely predictive enough to give good predictions within the number of splits you've allowed (determined by the `max_depth` hyperparameter)? Consider increasing `max_features`.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ChZsXw2sksDF"
      },
      "source": [
        "Using `pickle` to save the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "zDIhuuJtYHK4"
      },
      "outputs": [],
      "source": [
        "def write_pickle(model_object, save_name:str):\n",
        "  from google.colab import files\n",
        "  with open(save_name , 'wb') as to_write:\n",
        "    pickle.dump(model_object, to_write)\n",
        "  files.download(save_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "81LmSzKQYHK4"
      },
      "outputs": [],
      "source": [
        "def read_pickle(path, saved_model_name:str):\n",
        "  with open(path + saved_model_name, 'rb') as to_read:\n",
        "    model = pickle.load(to_read)\n",
        "    return model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#write_pickle(rf_cv,\"rf_cv_automatidata\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "ehfCK9CbZoRm",
        "outputId": "94aaf0f4-d1b9-4eba-c514-ffbb8674fd92"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_c9a00a06-fc54-4dd5-b4ab-d04a4b319fd4\", \"rf_cv_automatidata\", 79230819)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path_ = '/content/drive/MyDrive/Data Analytics/Main Projects/Automatidata project/Phase 6: Random Forest and XGB models/'\n",
        "saved_model_ = 'rf_cv_automatidata'\n",
        "rf_cv = read_pickle(path_,saved_model_)"
      ],
      "metadata": {
        "id": "RcaPa9-Ub1md"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QIaRiZW4hf-6"
      },
      "source": [
        "Examining the best average score across all the validation folds:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "29kGUegqhviL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a742240-4822-4a5a-9da3-048d287e4899"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7048191472699186"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "rf_cv.best_score_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "heGb51fHh3E5"
      },
      "source": [
        "Examining the best combination of hyperparameters:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_cv.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pXlPqE83crl6",
        "outputId": "5291d6fe-fb51-4713-ccd3-a05ecf5ee649"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'max_depth': None,\n",
              " 'max_features': 1.0,\n",
              " 'max_samples': 0.7,\n",
              " 'min_samples_leaf': 1,\n",
              " 'min_samples_split': 2,\n",
              " 'n_estimators': 300}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qZZnem5yiAau"
      },
      "source": [
        "Using the `make_results()` function to output all of the scores of the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "u-UodWEOedxz"
      },
      "outputs": [],
      "source": [
        "def make_results(model_name:str, model_object, metric:str):\n",
        "    '''\n",
        "    Arguments:\n",
        "    model_name (string): what you want the model to be called in the output table\n",
        "    model_object: a fit GridSearchCV object\n",
        "    metric (string): precision, recall, f1, or accuracy\n",
        "\n",
        "    Returns a pandas df with the F1, recall, precision, and accuracy scores\n",
        "    for the model with the best mean 'metric' score across all validation folds.\n",
        "    '''\n",
        "\n",
        "    # Create dictionary that maps input metric to actual metric name in GridSearchCV\n",
        "    metric_dict = {'precision': 'mean_test_precision',\n",
        "                 'recall': 'mean_test_recall',\n",
        "                 'f1': 'mean_test_f1',\n",
        "                 'accuracy': 'mean_test_accuracy',\n",
        "                 }\n",
        "\n",
        "    # Get all the results from the CV and put them in a df\n",
        "    cv_results = pd.DataFrame(model_object.cv_results_)\n",
        "\n",
        "    # Isolate the row of the df with the max(metric) score\n",
        "    best_estimator_results = cv_results.iloc[cv_results[metric_dict[metric]].idxmax(), :]\n",
        "\n",
        "    # Extract Accuracy, precision, recall, and f1 score from that row\n",
        "    f1 = best_estimator_results.mean_test_f1\n",
        "    recall = best_estimator_results.mean_test_recall\n",
        "    precision = best_estimator_results.mean_test_precision\n",
        "    accuracy = best_estimator_results.mean_test_accuracy\n",
        "\n",
        "    # Create table of results\n",
        "    table = pd.DataFrame({'model': [model_name],\n",
        "                        'precision': [precision],\n",
        "                        'recall': [recall],\n",
        "                        'F1': [f1],\n",
        "                        'accuracy': [accuracy],\n",
        "                        },\n",
        "                       )\n",
        "\n",
        "    return table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cI84Xo37ZLy0"
      },
      "source": [
        "Calling `make_results()` on the GridSearch object:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "qAYb2QigiT_h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "c32ea2d8-dc17-4064-d8bd-fa02239cdfba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              model  precision    recall        F1  accuracy\n",
              "0  Random Forest CV   0.670789  0.742688  0.704819  0.672699"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd8b85bd-5fad-4c1f-a547-fe66722919e3\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest CV</td>\n",
              "      <td>0.670789</td>\n",
              "      <td>0.742688</td>\n",
              "      <td>0.704819</td>\n",
              "      <td>0.672699</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bd8b85bd-5fad-4c1f-a547-fe66722919e3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bd8b85bd-5fad-4c1f-a547-fe66722919e3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-bd8b85bd-5fad-4c1f-a547-fe66722919e3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_17223871-9e35-4827-bec5-1c59a4df72cf\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Random Forest CV\"\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 0.6707893744432629,\n        \"max\": 0.6707893744432629,\n        \"samples\": [\n          0.6707893744432629\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 0.7426882389545738,\n        \"max\": 0.7426882389545738,\n        \"samples\": [\n          0.7426882389545738\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 0.7048191472699186,\n        \"max\": 0.7048191472699186,\n        \"samples\": [\n          0.7048191472699186\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 0.6726989846053062,\n        \"max\": 0.6726989846053062,\n        \"samples\": [\n          0.6726989846053062\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "results = make_results(\"Random Forest CV\",rf_cv,\"f1\")\n",
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SB-yhW9uu7dO"
      },
      "source": [
        "The results should produce an acceptable model across the board. Typically scores of 0.65 or better are considered acceptable, but this is always dependent on the use case. Optional: try to improve the scores. It's worth trying, especially to practice searching over different hyperparameters.\n",
        "\n",
        "\n",
        "**For example, if the available values for `min_samples_split` were [2, 3, 4] and GridSearch identified the best value as 4, consider trying [4, 5, 6] this time.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Aa4-n-SiYHK7"
      },
      "source": [
        "> For this project, several models to predict on the test data will be used. Remember that this decision comes with a trade-off. What is the benefit of this? What is the drawback?\n",
        "\n",
        "> * The benefit of using multiple models to predict on the test data is that you can compare models using data that was not used to train/tune hyperparameters. This reduces the risk of selecting a model based on how well it fit the training data.\n",
        "\n",
        "> * The drawback of using the final test data to select a model is that, by using the unseen data to make a decision about which model to use, you no longer have a truly unbiased idea of how your model would be expected to perform on new data. In this case, think of final model selection as another way of \"tuning\" your model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-vOtik1YHK7"
      },
      "source": [
        "Using the model to predict on the test data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "M-o491bKYHK8"
      },
      "outputs": [],
      "source": [
        "rf_cv_preds = rf_cv.best_estimator_.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HrOCkBrdYHK9"
      },
      "source": [
        "Use the below `get_test_scores()` function to output the scores of the model on the test data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "woRFh6XIYHK9"
      },
      "outputs": [],
      "source": [
        "def get_test_scores(model_name:str, preds, y_test_data):\n",
        "    '''\n",
        "    Generate a table of test scores.\n",
        "\n",
        "    In:\n",
        "    model_name (string): Your choice: how the model will be named in the output table\n",
        "    preds: numpy array of test predictions\n",
        "    y_test_data: numpy array of y_test data\n",
        "\n",
        "    Out:\n",
        "    table: a pandas df of precision, recall, f1, and accuracy scores for your model\n",
        "    '''\n",
        "    accuracy = accuracy_score(y_test_data, preds)\n",
        "    precision = precision_score(y_test_data, preds)\n",
        "    recall = recall_score(y_test_data, preds)\n",
        "    f1 = f1_score(y_test_data, preds)\n",
        "\n",
        "    table = pd.DataFrame({'model': [model_name],\n",
        "                        'precision': [precision],\n",
        "                        'recall': [recall],\n",
        "                        'F1': [f1],\n",
        "                        'accuracy': [accuracy]\n",
        "                        })\n",
        "\n",
        "    return table"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fou632B3YHK9"
      },
      "source": [
        "###### RF test results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "Iil1LjabiT5x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 125
        },
        "outputId": "858ff304-6dea-40d9-a33f-cad8c1016025"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   model  precision    recall        F1  accuracy\n",
              "0       Random Forest CV   0.670789  0.742688  0.704819  0.672699\n",
              "0  Random Forest CV test   0.667756  0.762912  0.712170  0.675401"
            ],
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              "      <th>model</th>\n",
              "      <th>precision</th>\n",
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              "        document.querySelector('#id_00e88e67-6e16-4dd5-aa28-975bdc736e43 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Random Forest CV test\",\n          \"Random Forest CV\"\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002144925800859754,\n        \"min\": 0.6677559912854031,\n        \"max\": 0.6707893744432629,\n        \"samples\": [\n          0.6677559912854031,\n          0.6707893744432629\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.014300541623249946,\n        \"min\": 0.7426882389545738,\n        \"max\": 0.7629122588674548,\n        \"samples\": [\n          0.7629122588674548,\n          0.7426882389545738\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0051975687714445286,\n        \"min\": 0.7048191472699186,\n        \"max\": 0.7121696195178623,\n        \"samples\": [\n          0.7121696195178623,\n          0.7048191472699186\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0019107864214834966,\n        \"min\": 0.6726989846053062,\n        \"max\": 0.6754012446773665,\n        \"samples\": [\n          0.6754012446773665,\n          0.6726989846053062\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "rf_test_scores = get_test_scores('Random Forest CV test', rf_cv_preds, y_test)\n",
        "results = pd.concat([results, rf_test_scores], axis=0)\n",
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E4JiP5VRz2un"
      },
      "source": [
        "**Question:** How do your test results compare to your validation results?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dE6oXEJJiT2R"
      },
      "source": [
        "All scores increased by at most ~0.02."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DOyBdkrlYHK-"
      },
      "source": [
        "##### **XGBoost**\n",
        "\n",
        " Trying to improve the scores using an XGBoost model:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "7wZKVr2qYHK-"
      },
      "outputs": [],
      "source": [
        "# 1. Instantiate the XGBoost classifier\n",
        "xgb = XGBClassifier(objective='binary:logistic', random_state=0)\n",
        "\n",
        "# 2. Create a dictionary of hyperparameters to tune\n",
        "# Note that this example only contains 1 value for each parameter for simplicity,\n",
        "# but you should assign a dictionary with ranges of values\n",
        "cv_params = {'learning_rate': [0.1],\n",
        "             'max_depth': [8],\n",
        "             'min_child_weight': [2],\n",
        "             'n_estimators': [500]\n",
        "             }\n",
        "\n",
        "# 3. Define a set of scoring metrics to capture\n",
        "scoring = {'accuracy', 'precision', 'recall', 'f1'}\n",
        "\n",
        "# 4. Instantiate the GridSearchCV object\n",
        "xgb_cv = GridSearchCV(xgb, cv_params, scoring=scoring, cv=4, refit='f1')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RCvJ3qKpYHK-"
      },
      "source": [
        "Now fit the model to the `X_train` and `y_train` data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "rq6lCafqYHK-",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "outputId": "37cb8b4f-0ddc-4a2b-dba4-29520c5e7447"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 50.7 s, sys: 224 ms, total: 51 s\n",
            "Wall time: 35 s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=4,\n",
              "             estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                     callbacks=None, colsample_bylevel=None,\n",
              "                                     colsample_bynode=None,\n",
              "                                     colsample_bytree=None, device=None,\n",
              "                                     early_stopping_rounds=None,\n",
              "                                     enable_categorical=False, eval_metric=None,\n",
              "                                     feature_types=None, gamma=None,\n",
              "                                     grow_policy=None, importance_type=None,\n",
              "                                     interaction_constraints=None,\n",
              "                                     learning_rate=None,...\n",
              "                                     max_delta_step=None, max_depth=None,\n",
              "                                     max_leaves=None, min_child_weight=None,\n",
              "                                     missing=nan, monotone_constraints=None,\n",
              "                                     multi_strategy=None, n_estimators=None,\n",
              "                                     n_jobs=None, num_parallel_tree=None,\n",
              "                                     random_state=0, ...),\n",
              "             param_grid={'learning_rate': [0.1], 'max_depth': [8],\n",
              "                         'min_child_weight': [2], 'n_estimators': [500]},\n",
              "             refit='f1', scoring={'accuracy', 'precision', 'recall', 'f1'})"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=4,\n",
              "             estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                     callbacks=None, colsample_bylevel=None,\n",
              "                                     colsample_bynode=None,\n",
              "                                     colsample_bytree=None, device=None,\n",
              "                                     early_stopping_rounds=None,\n",
              "                                     enable_categorical=False, eval_metric=None,\n",
              "                                     feature_types=None, gamma=None,\n",
              "                                     grow_policy=None, importance_type=None,\n",
              "                                     interaction_constraints=None,\n",
              "                                     learning_rate=None,...\n",
              "                                     max_delta_step=None, max_depth=None,\n",
              "                                     max_leaves=None, min_child_weight=None,\n",
              "                                     missing=nan, monotone_constraints=None,\n",
              "                                     multi_strategy=None, n_estimators=None,\n",
              "                                     n_jobs=None, num_parallel_tree=None,\n",
              "                                     random_state=0, ...),\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.1], &#x27;max_depth&#x27;: [8],\n",
              "                         &#x27;min_child_weight&#x27;: [2], &#x27;n_estimators&#x27;: [500]},\n",
              "             refit=&#x27;f1&#x27;, scoring={&#x27;accuracy&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;, &#x27;f1&#x27;})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=4,\n",
              "             estimator=XGBClassifier(base_score=None, booster=None,\n",
              "                                     callbacks=None, colsample_bylevel=None,\n",
              "                                     colsample_bynode=None,\n",
              "                                     colsample_bytree=None, device=None,\n",
              "                                     early_stopping_rounds=None,\n",
              "                                     enable_categorical=False, eval_metric=None,\n",
              "                                     feature_types=None, gamma=None,\n",
              "                                     grow_policy=None, importance_type=None,\n",
              "                                     interaction_constraints=None,\n",
              "                                     learning_rate=None,...\n",
              "                                     max_delta_step=None, max_depth=None,\n",
              "                                     max_leaves=None, min_child_weight=None,\n",
              "                                     missing=nan, monotone_constraints=None,\n",
              "                                     multi_strategy=None, n_estimators=None,\n",
              "                                     n_jobs=None, num_parallel_tree=None,\n",
              "                                     random_state=0, ...),\n",
              "             param_grid={&#x27;learning_rate&#x27;: [0.1], &#x27;max_depth&#x27;: [8],\n",
              "                         &#x27;min_child_weight&#x27;: [2], &#x27;n_estimators&#x27;: [500]},\n",
              "             refit=&#x27;f1&#x27;, scoring={&#x27;accuracy&#x27;, &#x27;precision&#x27;, &#x27;recall&#x27;, &#x27;f1&#x27;})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=0, ...)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=None, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=0, ...)</pre></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "%%time\n",
        "xgb_cv.fit(X_train,y_train)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "39YyW6_yYHK_"
      },
      "source": [
        "Geting the best score from this model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "_A5bLa4cYHK_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fc0cd00-b511-4de3-ff1d-8eab981b1f26"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6967988957326091"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "xgb_cv.best_score_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2bB-QyGz0RcU"
      },
      "source": [
        "And the best parameters."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "JiLja3YViTzj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "160e4a11-05cc-4bdd-a321-71f7907758cf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.1,\n",
              " 'max_depth': 8,\n",
              " 'min_child_weight': 2,\n",
              " 'n_estimators': 500}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "xgb_cv.best_params_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eTE2QdNP0eEP"
      },
      "source": [
        "##### XGB CV Results\n",
        "\n",
        "Using the `make_results()` function to output all of the scores of the model:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "L4TSYXJWiTxs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        },
        "outputId": "5844539a-5a09-46c2-f5e5-0dcbc771e6a3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        model  precision    recall        F1  accuracy\n",
              "0  XGBoost CV   0.671798  0.723864  0.696799  0.668441"
            ],
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              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>precision</th>\n",
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              "      <td>XGBoost CV</td>\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-465e372c-ab9b-4ae0-ad5b-83004013a27d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-465e372c-ab9b-4ae0-ad5b-83004013a27d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_d8ebbf60-52d4-4c4f-bfb9-e89146847850\">\n",
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              "        border-radius: 50%;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
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              "\n",
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              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('xgb_train_scores')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "xgb_train_scores",
              "summary": "{\n  \"name\": \"xgb_train_scores\",\n  \"rows\": 1,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"XGBoost CV\"\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 0.6717984711795344,\n        \"max\": 0.6717984711795344,\n        \"samples\": [\n          0.6717984711795344\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 0.7238643434971997,\n        \"max\": 0.7238643434971997,\n        \"samples\": [\n          0.7238643434971997\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 0.6967988957326091,\n        \"max\": 0.6967988957326091,\n        \"samples\": [\n          0.6967988957326091\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": NaN,\n        \"min\": 0.66844087782509,\n        \"max\": 0.66844087782509,\n        \"samples\": [\n          0.66844087782509\n        ],\n        \"num_unique_values\": 1,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "xgb_train_scores = make_results(\"XGBoost CV\",xgb_cv,\"f1\")\n",
        "xgb_train_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "5Y2giCN32Dwc"
      },
      "outputs": [],
      "source": [
        "xgb_preds = xgb_cv.best_estimator_.predict(X_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cEwnNMMP2Nbb"
      },
      "source": [
        "###### XGB test results\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "g7jShC2TiTvx"
      },
      "outputs": [],
      "source": [
        "xgb_test_scores = get_test_scores('XGBoost CV test', xgb_preds, y_test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "saM8YwbAyi-F"
      },
      "source": [
        "**Question:** Compare these scores to the random forest test scores. What do you notice? Which model would you choose?"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = pd.concat([results, xgb_test_scores,xgb_train_scores], axis=0)\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Usldy82QigAL",
        "outputId": "d8f7531c-5a87-4291-e20f-c638c4d4db50"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   model  precision    recall        F1  accuracy\n",
              "0       Random Forest CV   0.670789  0.742688  0.704819  0.672699\n",
              "0  Random Forest CV test   0.667756  0.762912  0.712170  0.675401\n",
              "0        XGBoost CV test   0.674379  0.743622  0.707310  0.676056\n",
              "0             XGBoost CV   0.671798  0.723864  0.696799  0.668441"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-707f38d5-4379-4a22-90a0-4913d54e9b07\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>precision</th>\n",
              "      <th>recall</th>\n",
              "      <th>F1</th>\n",
              "      <th>accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest CV</td>\n",
              "      <td>0.670789</td>\n",
              "      <td>0.742688</td>\n",
              "      <td>0.704819</td>\n",
              "      <td>0.672699</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Random Forest CV test</td>\n",
              "      <td>0.667756</td>\n",
              "      <td>0.762912</td>\n",
              "      <td>0.712170</td>\n",
              "      <td>0.675401</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XGBoost CV test</td>\n",
              "      <td>0.674379</td>\n",
              "      <td>0.743622</td>\n",
              "      <td>0.707310</td>\n",
              "      <td>0.676056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XGBoost CV</td>\n",
              "      <td>0.671798</td>\n",
              "      <td>0.723864</td>\n",
              "      <td>0.696799</td>\n",
              "      <td>0.668441</td>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-20677713-a0ad-4efd-b34c-b18955243a87 button');\n",
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              "      buttonEl.onclick = () => {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "results",
              "summary": "{\n  \"name\": \"results\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"samples\": [\n          \"Random Forest CV test\",\n          \"XGBoost CV\",\n          \"Random Forest CV\"\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"precision\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.002738248641800459,\n        \"min\": 0.6677559912854031,\n        \"max\": 0.6743792325056434,\n        \"samples\": [\n          0.6677559912854031,\n          0.6717984711795344,\n          0.6707893744432629\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"recall\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015946367763057405,\n        \"min\": 0.7238643434971997,\n        \"max\": 0.7629122588674548,\n        \"samples\": [\n          0.7629122588674548,\n          0.7238643434971997,\n          0.7426882389545738\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006422059815859348,\n        \"min\": 0.6967988957326091,\n        \"max\": 0.7121696195178623,\n        \"samples\": [\n          0.7121696195178623,\n          0.6967988957326091,\n          0.7048191472699186\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.003459005071018724,\n        \"min\": 0.66844087782509,\n        \"max\": 0.676056338028169,\n        \"samples\": [\n          0.6754012446773665,\n          0.66844087782509,\n          0.6726989846053062\n        ],\n        \"num_unique_values\": 4,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HyEbR3EbYHLA"
      },
      "source": [
        "The F<sub>1</sub> score is ~0.01 lower than the random forest model. Both models are acceptable, but the random forest model is the champion."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eCNH80Ku9TpO"
      },
      "source": [
        "Plotting a confusion matrix of the champion model's predictions on the test data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "5iUyZWjWvqOd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "outputId": "a82a1f9d-a27e-4874-91e6-9b0db3705845"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "cm = confusion_matrix(y_test, rf_cv_preds, labels=rf_cv.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n",
        "                             display_labels=rf_cv.classes_)\n",
        "disp.plot();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dW-3_eWW-k2u"
      },
      "source": [
        "**Question:** What type of errors are more common for your model?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzzE1HVmYHLB"
      },
      "source": [
        "The model is almost twice as likely to predict a false positive than it is to predict a false negative. Therefore, type I errors are more common. This is less desirable, because it's better for a driver to be pleasantly surprised by a generous tip when they weren't expecting one than to be disappointed by a low tip when they were expecting a generous one. However, the overall performance of this model is satisfactory."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XNexnwvy09PK"
      },
      "source": [
        "##### Feature importance\n",
        "\n",
        "Using the `feature_importances_` attribute of the best estimator object to inspect the features of your final model. You can then sort them and plot the most important ones:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "kz5T1gHc1R2x",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "0247bbec-073d-4a2e-aad6-be42c68cd20f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "importances = rf_cv.best_estimator_.feature_importances_\n",
        "rf_importances = pd.Series(importances, index=X_test.columns)\n",
        "rf_importances = rf_importances.sort_values(ascending=False)[:15]\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8,5))\n",
        "rf_importances.plot.bar(ax=ax)\n",
        "ax.set_title('Feature importances')\n",
        "ax.set_ylabel('Mean decrease in impurity')\n",
        "fig.tight_layout();"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "display = PrecisionRecallDisplay.from_estimator(\n",
        "    rf_cv.best_estimator_, X_test, y_test, name='RF model'\n",
        "    )\n",
        "plt.title('Precision-recall curve, RF model');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "1LYCXG__kNy_",
        "outputId": "4709ff75-db34-4551-bb59-6e13af1869bc"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_HGsWfEOeWPm"
      },
      "source": [
        "\n",
        "## **PACE: Execute**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ill21hQ4ej9-"
      },
      "source": [
        "### **Task 4. Conclusion**\n",
        "\n",
        "Consider the following questions:\n",
        "\n",
        "1. **Would you recommend using this model? Why or why not?**  \n",
        "Yes, this is model performs acceptably. Its F<sub>1</sub> score was 0.7235 and it had an overall accuracy of 0.6865. It correctly identified ~78% of the actual responders in the test set, which is 48% better than a random guess. It may be worthwhile to test the model with a select group of taxi drivers to get feedback.  \n",
        "\n",
        "\n",
        "2. **What was your highest scoring model doing? Can you explain how it was making predictions?**   \n",
        "Unfortunately, random forest is not the most transparent machine learning algorithm. We know that `VendorID`, `predicted_fare`, `mean_duration`, and `mean_distance` are the most important features, but we don't know how they influence tipping. This would require further exploration. It is interesting that `VendorID` is the most predictive feature. This seems to indicate that one of the two vendors tends to attract more generous customers. It may be worth performing statistical tests on the different vendors to examine this further.  \n",
        "\n",
        "\n",
        "3. **Are there new features that you can engineer that might improve model performance?**  \n",
        "There are almost always additional features that can be engineered, but hopefully the most obvious ones were generated during the first round of modeling. In our case, we could try creating three new columns that indicate if the trip distance is short, medium, or far. We could also engineer a column that gives a ratio that represents (the amount of money from the fare amount to the nearest higher multiple of \\\\$5) / fare amount. For example, if the fare were \\\\$12, the value in this column would be 0.25, because \\\\$12 to the nearest higher multiple of \\\\$5 (\\\\$15) is \\\\$3, and \\\\$3 divided by \\\\$12 is 0.25. The intuition for this feature is that people might be likely to simply round up their tip, so journeys with fares with values just under a multiple of \\\\$5 may have lower tip percentages than those with fare values just over a multiple of \\\\$5. We could also do the same thing for fares to the nearest \\\\$10.\n",
        "\n",
        "$$\n",
        "round5\\_ratio = \\frac{amount\\ of\\ money\\ from\\ the\\ fare\\ amount\\ to\\ the\\ nearest\\ higher\\ multiple\\ of\\ \\$5}{fare\\ amount}\n",
        "$$\n",
        "\n",
        "<br>\n",
        "\n",
        "<details>\n",
        "    <summary><h5>HINT</h5></summary>\n",
        "    $$ = \\frac{5 - (fare\\ mod\\ 5)}{fare\\ amount}$$\n",
        "</details>\n",
        "\n",
        "<br>\n",
        "\n",
        "4. **What features would you want to have that would likely improve the performance of your model?**   \n",
        "It would probably be very helpful to have past tipping behavior for each customer. It would also be valuable to have accurate tip values for customers who pay with cash.\n",
        "It would be helpful to have a lot more data. With enough data, we could create a unique feature for each pickup/dropoff combination.\n",
        "\n",
        "\n",
        "Remember, sometimes your data simply will not be predictive of your chosen target. This is common. Machine learning is a powerful tool, but it is not magic. If your data does not contain predictive signal, even the most complex algorithm will not be able to deliver consistent and accurate predictions. Do not be afraid to draw this conclusion. Even if you cannot use the model to make strong predictions, the work was not done in vain."
      ]
    }
  ],
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