Activity_Build a decision tree (1).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "700acefd-8120-42a9-a650-c610467e2a4c"
   },
   "source": [
    "# Activity: Build a decision tree\n",
    "\n",
    "## Introduction\n",
    "\n",
    "A decision tree model can makes predictions for a target based on multiple features. Because decision trees are used across a wide array of industries, becoming proficient in the process of building one will help you expand your skill set in a widely-applicable way.   \n",
    "\n",
    "For this activity, you work as a consultant for an airline. The airline is interested in predicting whether a future customer would be satisfied with their services given customer feedback given previous customer feedback about their flight experience. The airline would like you to construct and evaluate a model that can accomplish this goal. Specifically, they are interested in knowing which features are most important to customer satisfaction.\n",
    "\n",
    "The data for this activity includes survey responses from 129,880 customers. It includes data points such as class, flight distance, and in-flight entertainment, among others. In a previous activity, you utilized a binomial logistic regression model to help the airline better understand this data. In this activity, your goal will be to utilize a decision tree model to predict whether or not a customer will be satisfied with their flight experience. \n",
    "\n",
    "Because this activity uses a dataset from the industry, you will need to conduct basic EDA, data cleaning, and other manipulations to prepare the data for modeling.\n",
    "\n",
    "In this activity, you’ll practice the following skills:\n",
    "\n",
    "* Importing packages and loading data\n",
    "* Exploring the data and completing the cleaning process\n",
    "* Building a decision tree model \n",
    "* Tuning hyperparameters using `GridSearchCV`\n",
    "* Evaluating a decision tree model using a confusion matrix and various other plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b42252b9-b980-4ee0-8cfd-82f4239b6d1a"
   },
   "source": [
    "## Step 1: Imports\n",
    "\n",
    "Import relevant Python packages. Use `DecisionTreeClassifier`,` plot_tree`, and various imports from `sklearn.metrics` to build, visualize, and evaluate the model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d871f2b3"
   },
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "aacf7b0d-d652-46c4-a9fc-f1ff377bdd0c"
   },
   "outputs": [],
   "source": [
    "### YOUR CODE HERE ###\n",
    "# Standard operational package imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# Important imports for modeling and evaluation\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.tree import plot_tree\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import sklearn.metrics as metrics\n",
    "# Visualization package imports\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6b7f8803-44ee-47cf-b97a-efaf5c0fdd59",
    "tags": []
   },
   "source": [
    "### Load the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a60990fd-4723-4a2b-a0db-9dfa6c7c881c"
   },
   "source": [
    "`Pandas` is used to load the **Invistico_Airline.csv** dataset. The resulting pandas DataFrame is saved in a variable named `df_original`. As shown in this cell, the dataset has been automatically loaded in for you. You do not need to download the .csv file, or provide more code, in order to access the dataset and proceed with this lab. Please continue with this activity by completing the following instructions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "80535d2f-8b1a-4715-b07a-f3734d8cc4c5"
   },
   "outputs": [],
   "source": [
    "# RUN THIS CELL TO IMPORT YOUR DATA.\n",
    "\n",
    "### YOUR CODE HERE ###\n",
    "\n",
    "df_original = pd.read_csv(\"Invistico_Airline.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7bc1c9bb"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Use a function from the pandas library to read in the csv file.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d8807846"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "Use the `read_csv` function and pass in the file name as a string. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6035eeb5"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 3</strong></h4></summary>\n",
    "\n",
    "Use `pd.read_csv(\"insertfilenamehere\")`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "46448192-f2f8-4f78-9f76-6e4bb699da7b"
   },
   "source": [
    "### Output the first 10 rows of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "U8JZjN-zJwx6"
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   "outputs": [
    {
     "data": {
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       "<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>satisfaction</th>\n",
       "      <th>Customer Type</th>\n",
       "      <th>Age</th>\n",
       "      <th>Type of Travel</th>\n",
       "      <th>Class</th>\n",
       "      <th>Flight Distance</th>\n",
       "      <th>Seat comfort</th>\n",
       "      <th>Departure/Arrival time convenient</th>\n",
       "      <th>Food and drink</th>\n",
       "      <th>Gate location</th>\n",
       "      <th>...</th>\n",
       "      <th>Online support</th>\n",
       "      <th>Ease of Online booking</th>\n",
       "      <th>On-board service</th>\n",
       "      <th>Leg room service</th>\n",
       "      <th>Baggage handling</th>\n",
       "      <th>Checkin service</th>\n",
       "      <th>Cleanliness</th>\n",
       "      <th>Online boarding</th>\n",
       "      <th>Departure Delay in Minutes</th>\n",
       "      <th>Arrival Delay in Minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>65</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>265</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>Loyal Customer</td>\n",
       "      <td>47</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>2464</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>310</td>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>15</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>2138</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>Loyal Customer</td>\n",
       "      <td>60</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>623</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>70</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>30</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1894</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>66</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>227</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>10</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1812</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>56</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Business</td>\n",
       "      <td>73</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>satisfied</td>\n",
       "      <td>Loyal Customer</td>\n",
       "      <td>22</td>\n",
       "      <td>Personal Travel</td>\n",
       "      <td>Eco</td>\n",
       "      <td>1556</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>26.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  satisfaction   Customer Type  Age   Type of Travel     Class  \\\n",
       "0    satisfied  Loyal Customer   65  Personal Travel       Eco   \n",
       "1    satisfied  Loyal Customer   47  Personal Travel  Business   \n",
       "2    satisfied  Loyal Customer   15  Personal Travel       Eco   \n",
       "3    satisfied  Loyal Customer   60  Personal Travel       Eco   \n",
       "4    satisfied  Loyal Customer   70  Personal Travel       Eco   \n",
       "5    satisfied  Loyal Customer   30  Personal Travel       Eco   \n",
       "6    satisfied  Loyal Customer   66  Personal Travel       Eco   \n",
       "7    satisfied  Loyal Customer   10  Personal Travel       Eco   \n",
       "8    satisfied  Loyal Customer   56  Personal Travel  Business   \n",
       "9    satisfied  Loyal Customer   22  Personal Travel       Eco   \n",
       "\n",
       "   Flight Distance  Seat comfort  Departure/Arrival time convenient  \\\n",
       "0              265             0                                  0   \n",
       "1             2464             0                                  0   \n",
       "2             2138             0                                  0   \n",
       "3              623             0                                  0   \n",
       "4              354             0                                  0   \n",
       "5             1894             0                                  0   \n",
       "6              227             0                                  0   \n",
       "7             1812             0                                  0   \n",
       "8               73             0                                  0   \n",
       "9             1556             0                                  0   \n",
       "\n",
       "   Food and drink  Gate location  ...  Online support  Ease of Online booking  \\\n",
       "0               0              2  ...               2                       3   \n",
       "1               0              3  ...               2                       3   \n",
       "2               0              3  ...               2                       2   \n",
       "3               0              3  ...               3                       1   \n",
       "4               0              3  ...               4                       2   \n",
       "5               0              3  ...               2                       2   \n",
       "6               0              3  ...               5                       5   \n",
       "7               0              3  ...               2                       2   \n",
       "8               0              3  ...               5                       4   \n",
       "9               0              3  ...               2                       2   \n",
       "\n",
       "   On-board service  Leg room service  Baggage handling  Checkin service  \\\n",
       "0                 3                 0                 3                5   \n",
       "1                 4                 4                 4                2   \n",
       "2                 3                 3                 4                4   \n",
       "3                 1                 0                 1                4   \n",
       "4                 2                 0                 2                4   \n",
       "5                 5                 4                 5                5   \n",
       "6                 5                 0                 5                5   \n",
       "7                 3                 3                 4                5   \n",
       "8                 4                 0                 1                5   \n",
       "9                 2                 4                 5                3   \n",
       "\n",
       "   Cleanliness  Online boarding  Departure Delay in Minutes  \\\n",
       "0            3                2                           0   \n",
       "1            3                2                         310   \n",
       "2            4                2                           0   \n",
       "3            1                3                           0   \n",
       "4            2                5                           0   \n",
       "5            4                2                           0   \n",
       "6            5                3                          17   \n",
       "7            4                2                           0   \n",
       "8            4                4                           0   \n",
       "9            4                2                          30   \n",
       "\n",
       "   Arrival Delay in Minutes  \n",
       "0                       0.0  \n",
       "1                     305.0  \n",
       "2                       0.0  \n",
       "3                       0.0  \n",
       "4                       0.0  \n",
       "5                       0.0  \n",
       "6                      15.0  \n",
       "7                       0.0  \n",
       "8                       0.0  \n",
       "9                      26.0  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tooWlpMBHqoi"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Use the `head()` function.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l8_h8SKmX8yu"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "If only five rows are output, it is because the function by default returns five rows. To change this, specify how many rows `(n = )` you want to output.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ec269f85-6622-4f82-bdcd-1874224a4052",
    "tags": []
   },
   "source": [
    "## Step 2: Data exploration, data cleaning, and model preparation\n",
    "\n",
    "### Prepare the data\n",
    "\n",
    "After loading the dataset, prepare the data to be suitable for decision tree classifiers. This includes: \n",
    "\n",
    "*   Exploring the data\n",
    "*   Checking for missing values\n",
    "*   Encoding the data\n",
    "*   Renaming a column\n",
    "*   Creating the training and testing data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d7ccd368"
   },
   "source": [
    "### Explore the data\n",
    "\n",
    "Check the data type of each column. Note that decision trees expect numeric data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "c84ecc35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction                          object\n",
       "Customer Type                         object\n",
       "Age                                    int64\n",
       "Type of Travel                        object\n",
       "Class                                 object\n",
       "Flight Distance                        int64\n",
       "Seat comfort                           int64\n",
       "Departure/Arrival time convenient      int64\n",
       "Food and drink                         int64\n",
       "Gate location                          int64\n",
       "Inflight wifi service                  int64\n",
       "Inflight entertainment                 int64\n",
       "Online support                         int64\n",
       "Ease of Online booking                 int64\n",
       "On-board service                       int64\n",
       "Leg room service                       int64\n",
       "Baggage handling                       int64\n",
       "Checkin service                        int64\n",
       "Cleanliness                            int64\n",
       "Online boarding                        int64\n",
       "Departure Delay in Minutes             int64\n",
       "Arrival Delay in Minutes             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "338fa42f"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Use the `dtypes` attribute on the DataFrame.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "768367ea"
   },
   "source": [
    "### Output unique values \n",
    "\n",
    "The `Class` column is ordinal (meaning there is an inherent order that is significant). For example, airlines typically charge more for 'Business' than 'Eco Plus' and 'Eco'. Output the unique values in the `Class` column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "52205295"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Eco', 'Business', 'Eco Plus'], dtype=object)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original[\"Class\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "40a58c8b"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1 </strong></h4></summary>\n",
    "\n",
    "Use the `unique()` function on the column `'Class'`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "381c04eb"
   },
   "source": [
    "### Check the counts of the predicted labels\n",
    "\n",
    "In order to predict customer satisfaction, verify if the dataset is imbalanced. To do this, check the counts of each of the predicted labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "8098d8a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfied       71087\n",
       "dissatisfied    58793\n",
       "Name: satisfaction, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original['satisfaction'].value_counts(dropna = False)\n",
    "### YOUR CODE HERE ###"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9221f9a4"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1</strong> </h4></summary>\n",
    "\n",
    "Use a function from the pandas library that returns a pandas series containing counts of unique values. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "62d8c90f"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 2</strong> </h4></summary>\n",
    "\n",
    "Use the `value_counts()` function. Set the `dropna` parameter passed in to this function to `False` if you want to examine how many NaN values there are. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "868c4ea3"
   },
   "source": [
    "**Question:** How many satisfied and dissatisfied customers were there?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "M8nBc7uC6Gkw"
   },
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b6bd7c91"
   },
   "source": [
    "**Question:** What percentage of customers were satisfied? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pgSK9GlM6J15"
   },
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4222ca54"
   },
   "source": [
    "### Check for missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TgANHJBVxpbI"
   },
   "source": [
    "The sklearn decision tree implementation does not support missing values. Check for missing values in the rows of the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "842031fb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction                           0\n",
       "Customer Type                          0\n",
       "Age                                    0\n",
       "Type of Travel                         0\n",
       "Class                                  0\n",
       "Flight Distance                        0\n",
       "Seat comfort                           0\n",
       "Departure/Arrival time convenient      0\n",
       "Food and drink                         0\n",
       "Gate location                          0\n",
       "Inflight wifi service                  0\n",
       "Inflight entertainment                 0\n",
       "Online support                         0\n",
       "Ease of Online booking                 0\n",
       "On-board service                       0\n",
       "Leg room service                       0\n",
       "Baggage handling                       0\n",
       "Checkin service                        0\n",
       "Cleanliness                            0\n",
       "Online boarding                        0\n",
       "Departure Delay in Minutes             0\n",
       "Arrival Delay in Minutes             393\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_original.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ptn9xlIjJOAL"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</h4></summary></strong>\n",
    "\n",
    "Use the `isnull` function and the `sum` function. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "307f50c5"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 2 </strong> </h4></summary>\n",
    "\n",
    "To get the number of rows in the data with missing values, use the `isnull` function followed by the `sum` function.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5c11852b"
   },
   "source": [
    "**Question:** Why is it important to check how many rows and columns there are in the dataset?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check the number of rows and columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "a5e89463"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129880, 22)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "df_original.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "10900f58"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1 </strong> </h4></summary>\n",
    "\n",
    "Use the `shape` attribute on the DataFrame.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eb005a73-4621-45b9-8114-30472ce20327"
   },
   "source": [
    "### Drop the rows with missing values\n",
    "\n",
    "Drop the rows with missing values and save the resulting pandas DataFrame in a variable named `df_subset`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "1cee438e-8b1f-4855-855e-3723d1958b69"
   },
   "outputs": [],
   "source": [
    "df_subset = df_original.dropna(axis = 0).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dec2a35e-ba31-4ec8-b8fc-1c0bdf075d19",
    "tags": []
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1 </strong> </h4></summary>\n",
    "\n",
    "Use the `dropna` function.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "374b2b21"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "Set the axis parameter passed into the `dropna` function to `0` if you want to drop rows containing missing values, or `1` if you want to drop columns containing missing values. Optionally, use reset_index to avoid a SettingWithCopy warning later in the notebook. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ymfmX-WvW31z"
   },
   "source": [
    "### Check for missing values\n",
    "\n",
    "Check that `df_subset` does not contain any missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "n36d4yztW4BA"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction                         0\n",
       "Customer Type                        0\n",
       "Age                                  0\n",
       "Type of Travel                       0\n",
       "Class                                0\n",
       "Flight Distance                      0\n",
       "Seat comfort                         0\n",
       "Departure/Arrival time convenient    0\n",
       "Food and drink                       0\n",
       "Gate location                        0\n",
       "Inflight wifi service                0\n",
       "Inflight entertainment               0\n",
       "Online support                       0\n",
       "Ease of Online booking               0\n",
       "On-board service                     0\n",
       "Leg room service                     0\n",
       "Baggage handling                     0\n",
       "Checkin service                      0\n",
       "Cleanliness                          0\n",
       "Online boarding                      0\n",
       "Departure Delay in Minutes           0\n",
       "Arrival Delay in Minutes             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "df_subset.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "8ed62a28"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1</strong></h4></summary>\n",
    "\n",
    "Use the `isna()`function and the `sum()` function. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d5a45cd9"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 2</strong> </h4></summary>\n",
    "\n",
    "To get the number of rows in the data with missing values, use the `isna()` function followed by the `sum()` function.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "18c6650e"
   },
   "source": [
    "### Check the number of rows and columns in the dataset again\n",
    "\n",
    "Check how many rows and columns are remaining in the dataset. You should now have 393 fewer rows of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "fa951c92"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(129487, 22)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d491a508"
   },
   "source": [
    "### Encode the data\n",
    "\n",
    "Four columns (`satisfaction`, `Customer Type`, `Type of Travel`, `Class`) are the pandas dtype object. Decision trees need numeric columns. Start by converting the ordinal `Class` column into numeric. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "4d580627"
   },
   "outputs": [],
   "source": [
    "df_subset['Class'] = df_subset['Class'].map({\"Business\": 3, \"Eco Plus\": 2, \"Eco\": 1}) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d07b3ae9"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1 </strong> </h4></summary>\n",
    "\n",
    "Use the `map()` or `replace()` function. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fa6d2042"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "For both functions, you will need to pass in a dictionary of class mappings `{\"Business\": 3, \"Eco Plus\": 2, \"Eco\": 1})`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LOy2vjgVyMw-"
   },
   "source": [
    "### Represent the data in the target variable numerically\n",
    "\n",
    "To represent the data in the target variable numerically, assign `\"satisfied\"` to the label `1` and `\"dissatisfied\"` to the label `0` in the `satisfaction` column. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "q1o64I02zay7"
   },
   "outputs": [],
   "source": [
    "df_subset['satisfaction'] = df_subset['satisfaction'].map({'satisfied': 1, 'dissatisfied': 0})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gPed8NIG1DYR"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1 </strong> </h4></summary>\n",
    "\n",
    "Use the `map()` function to assign existing values in a column to new values.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5m6ZF0yp1DYT"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 2 </strong></h4></summary>\n",
    "\n",
    "Call `map()` on the `satisfaction` column and pass in a dictionary specifying that `\"satisfied\"` should be assigned to `1` and `\"dissatisfied\"` should be assigned to `0`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "s_Jf_sDy2Mmp"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 3 </strong></h4></summary>\n",
    "\n",
    "Update the `satisfaction` column in `df_subset` with the newly assigned values.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WzdzBLmB2j-e"
   },
   "source": [
    "### Convert categorical columns into numeric\n",
    "\n",
    "There are other columns in the dataset that are still categorical. Be sure to convert categorical columns in the dataset into numeric."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "pKog_5f22j-g"
   },
   "outputs": [],
   "source": [
    "### YOUR CODE HERE ###\n",
    "df_subset = pd.get_dummies(df_subset, drop_first = True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pPiAhRMq2j-i"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1 </strong> </h4></summary>\n",
    "\n",
    "Use the `get_dummies()` function. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F4ybCX5t2j-j"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 2 </strong></h4></summary>\n",
    "\n",
    "Set the `drop_first` parameter to `True`. This removes redundant data.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "06bbe8dd"
   },
   "source": [
    "### Check column data types\n",
    "\n",
    "Now that you have converted categorical columns into numeric, check your column data types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "d39eecf9",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "satisfaction                           int64\n",
       "Age                                    int64\n",
       "Class                                  int64\n",
       "Flight Distance                        int64\n",
       "Seat comfort                           int64\n",
       "Departure/Arrival time convenient      int64\n",
       "Food and drink                         int64\n",
       "Gate location                          int64\n",
       "Inflight wifi service                  int64\n",
       "Inflight entertainment                 int64\n",
       "Online support                         int64\n",
       "Ease of Online booking                 int64\n",
       "On-board service                       int64\n",
       "Leg room service                       int64\n",
       "Baggage handling                       int64\n",
       "Checkin service                        int64\n",
       "Cleanliness                            int64\n",
       "Online boarding                        int64\n",
       "Departure Delay in Minutes             int64\n",
       "Arrival Delay in Minutes             float64\n",
       "Customer Type_disloyal Customer        uint8\n",
       "Type of Travel_Personal Travel         uint8\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "67c44d42"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1</strong></h4></summary>\n",
    "\n",
    "Use the `dtypes` attribute on the DataFrame.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0aaf5733"
   },
   "source": [
    "### Create the training and testing data\n",
    "\n",
    "Put 75% of the data into a training set and the remaining 25% into a testing set. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "77bffb4e"
   },
   "outputs": [],
   "source": [
    "y = df_subset['satisfaction']\n",
    "X = df_subset.copy()\n",
    "X=X.drop('satisfaction', axis = 1)\n",
    "X_train,X_test, y_train,y_test = train_test_split(X, y,test_size = 0.25, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "acae11f0"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1</strong></h4></summary>\n",
    "\n",
    "Use `train_test_split`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TVTdzUmKfY3g"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 2</strong></h4></summary>\n",
    "\n",
    "Pass in `0` to `random_state`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "101f3580"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 3</strong></h4></summary>\n",
    "\n",
    "If you named your features matrix X and your target y, then it would be `train_test_split(X, y, test_size=0.25, random_state=0)`.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "58bb9bf2"
   },
   "source": [
    "## Step 3: Model building"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "a81f66ae"
   },
   "source": [
    "### Fit a decision tree classifier model to the data\n",
    "\n",
    "Make a decision tree instance called `decision_tree` and pass in `0` to the `random_state` parameter. This is only so that if other data professionals run this code, they get the same results. Fit the model on the training set, use the `predict()` function on the testing set, and assign those predictions to the variable `dt_pred`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "5a4211cb"
   },
   "outputs": [],
   "source": [
    "classifier = DecisionTreeClassifier(random_state =42).fit(X_train, y_train)\n",
    "y_pred = classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ad9dac6b"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1</strong></h4></summary>\n",
    "\n",
    "Use `DecisionTreeClassifier`, the `fit()` function, and the `predict()` function.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dac3da82"
   },
   "source": [
    "**Question:** What are some advantages of using decision trees versus other models you have learned about? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RaOxZ4_66j4D"
   },
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d638b5bb"
   },
   "source": [
    "## Step 4: Results and evaluation\n",
    "\n",
    "Print out the decision tree model's accuracy, precision, recall, and F1 score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "6de9be28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:  0.935036\n",
      "precision:  0.938108\n",
      "recall:  0.943256\n",
      "F1 Score: 0.940675\n"
     ]
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "print('accuracy: ', \"%.6f\" %metrics. accuracy_score(y_test, y_pred))\n",
    "print (\"precision: \", \"%.6f\" %metrics.precision_score(y_test,y_pred))\n",
    "print ('recall: ', \"%.6f\"%metrics.recall_score(y_test,y_pred))\n",
    "print(\"F1 Score:\", \"%.6f\" % metrics.f1_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3c6c01ff"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1</strong></h4></summary>\n",
    "\n",
    "Use four different functions from `metrics` to get the accuracy, precision, recall, and F1 score.\n",
    "    \n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "72550972"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "Input `y_test` and `y_pred` into the `metrics.accuracy_score`, `metrics.precision_score`, `metrics.recall_score` and `metrics.f1_score` functions.\n",
    "    \n",
    "</details> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "14c49f02"
   },
   "source": [
    "**Question:** Are there any additional steps you could take to improve the performance or function of your decision tree?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YTIryEN26nqo"
   },
   "source": [
    "[Write your response here. Double-click (or enter) to edit.]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2d63fb90"
   },
   "source": [
    "### Produce a confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0524c798"
   },
   "source": [
    "Data professionals often like to know the types of errors made by an algorithm. To obtain this information, produce a confusion matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "df15d57b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f7b35641e10>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(y_test, y_pred, labels = classifier.classes_)\n",
    "disp = metrics.ConfusionMatrixDisplay(confusion_matrix = cm,display_labels = classifier.classes_)\n",
    "disp.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "13e29875"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Refer to [the content about plotting a confusion matrix](https://www.coursera.org/learn/regression-analysis-simplify-complex-data-relationships/lecture/SpRqe/evaluate-a-binomial-logistic-regression-model).\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4fdef8f5"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "Use `metrics.confusion_matrix`, `metrics.ConfusionMatrixDisplay`, and the `plot()` function.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "90154f0f"
   },
   "source": [
    "**Question:** What patterns can you identify between true positives and true negatives, as well as false positives and false negatives?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2NVELuWM6sPT"
   },
   "source": [
    "In the confusion matrix, there are a high proportion of true positives and true negatives (where the matrix accurately predicted that the customer would be satisfied or dissatified, respectively).\n",
    "\n",
    "The matrix also had a relatively low number of false positives and false negatives (where the matrix innacurately predicted that the customer would be satisfied or dissatified, respectively.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3e2c1c7d"
   },
   "source": [
    "### Plot the decision tree\n",
    "\n",
    "Examine the decision tree. Use `plot_tree` function to produce a visual representation of the tree to pinpoint where the splits in the data are occurring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "9ef5e9c3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(20,12))\n",
    "plot_tree(classifier, max_depth=2, fontsize=14, feature_names=X.columns);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2cfefc38"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "If your tree is hard to read, pass `2` or `3` in the parameter `max_depth`. \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9a84e65a"
   },
   "source": [
    "### Hyperparameter tuning\n",
    "\n",
    "Knowing how and when to adjust or tune a model can help a data professional significantly increase performance. In this section, you will find the best values for the hyperparameters `max_depth` and `min_samples_leaf` using grid search and cross validation. Below are some values for the hyperparameters `max_depth` and `min_samples_leaf`.   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "7b47fcab"
   },
   "outputs": [],
   "source": [
    "tree_para = {'max_depth':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,30,40,50],\n",
    "             'min_samples_leaf': [2,3,4,5,6,7,8,9, 10, 15, 20, 50]}\n",
    "\n",
    "scoring = {'accuracy', 'precision', 'recall', 'f1'}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "22672b1f"
   },
   "source": [
    "### Check combinations of values\n",
    "\n",
    "Check every combination of values to examine which pair has the best evaluation metrics. Make a decision tree instance called `tuned_decision_tree` with `random_state=0`, make a `GridSearchCV` instance called `clf`, make sure to refit the estimator using `\"f1\"`, and fit the model on the training set. \n",
    "\n",
    "**Note:** This cell may take up to 15 minutes to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "ca41e241"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort='deprecated',\n",
       "                                              random_state=42,\n",
       "                                              splitter='best'),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
       "                                       13, 14, 15, 16, 17, 18, 19, 20, 30, 40,\n",
       "                                       50],\n",
       "                         'min_samples_leaf': [2, 3, 4, 5, 6, 7, 8, 9, 10, 15,\n",
       "                                              20, 50]},\n",
       "             pre_dispatch='2*n_jobs', refit='f1', return_train_score=False,\n",
       "             scoring={'f1', 'recall', 'accuracy', 'precision'}, verbose=0)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuned_decision_tree  = DecisionTreeClassifier(random_state = 42)\n",
    "clf = GridSearchCV (tuned_decision_tree, tree_para, scoring = scoring, cv =5, refit = 'f1' )\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ab2eca46"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Refer to [the content about decision trees and grid search](https://www.coursera.org/learn/the-nuts-and-bolts-of-machine-learning/lecture/afopk/tune-a-decision-tree). \n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6122d0d0"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 2</strong></h4></summary>\n",
    "\n",
    "Use `DecisionTreeClassifier()`, `GridSearchCV()`, and the `clf.fit()` function.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "59b8a1db"
   },
   "source": [
    "**Question:** How can you determine the best combination of values for the hyperparameters? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the best estimator tool to help uncover the best pair combination. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute the best combination of values for the hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "213279f3",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=15, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=9, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=42, splitter='best')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2519f981"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong> Hint 1</strong></h4></summary>\n",
    "\n",
    "Use the `best_estimator_` attribute.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question:** What is the best combination of values for the hyperparameters? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After running the DecisionTreeClassifier, the maximum depth is 18 and the minimum number of samples is two, meaning this is the best combination of values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6b001fad"
   },
   "source": [
    "<strong> Question: What was the best average validation score? </strong>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "e615523c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9432694032101511\n"
     ]
    }
   ],
   "source": [
    "print(clf.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kOBEhD6Z65oM"
   },
   "source": [
    "\n",
    "The best validation score is 0.943269"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "19a7569f"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong> </h4></summary>\n",
    "\n",
    "Use the `.best_score_` attribute.\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dfad3e62"
   },
   "source": [
    "### Determine the \"best\" decision tree model's accuracy, precision, recall, and F1 score\n",
    "\n",
    "Print out the decision tree model's accuracy, precision, recall, and F1 score. This task can be done in a number of ways. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "id": "e8b96047"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>F1</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Tuned Decision Tree</td>\n",
       "      <td>0.943269</td>\n",
       "      <td>0.934876</td>\n",
       "      <td>0.951828</td>\n",
       "      <td>0.938393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Model        F1    Recall  Precision  Accuracy\n",
       "0  Tuned Decision Tree  0.943269  0.934876   0.951828  0.938393"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### YOUR CODE HERE ###\n",
    "\n",
    "results = pd.DataFrame(columns=['Model', 'F1', 'Recall', 'Precision', 'Accuracy'])\n",
    "\n",
    "def make_results(model_name, model_object):\n",
    "    \"\"\"\n",
    "    Accepts as arguments a model name (your choice - string) and\n",
    "    a fit GridSearchCV model object.\n",
    "\n",
    "    Returns a pandas df with the F1, recall, precision, and accuracy scores\n",
    "    for the model with the best mean F1 score across all validation folds.  \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(mean f1 score).\n",
    "    best_estimator_results = cv_results.iloc[cv_results['mean_test_f1'].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 a table of results.\n",
    "    table = pd.DataFrame()\n",
    "    table = table.append({'Model': model_name,\n",
    "                          'F1': f1,\n",
    "                          'Recall': recall,\n",
    "                          'Precision': precision,\n",
    "                          'Accuracy': accuracy},\n",
    "                         ignore_index=True)\n",
    "\n",
    "    return table\n",
    "\n",
    "result_table = make_results(\"Tuned Decision Tree\", clf)\n",
    "\n",
    "result_table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bb7d7d85"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 1</strong></h4></summary>\n",
    "\n",
    "Get all the results (`.cv_results_`) from the GridSearchCV instance (`clf`).\n",
    "\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "28e0535f"
   },
   "source": [
    "<details>\n",
    "  <summary><h4><strong>Hint 2</strong></h4></summary>\n",
    "\n",
    "Output `mean_test_f1`, `mean_test_recall`, `mean_test_precision`, and `mean_test_accuracy` from `clf.cv_results_`.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ac4e8ee6"
   },
   "source": [
    "**Question:** Was the additional performance improvement from hyperparameter tuning worth the computational cost? Why or why not?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "DGJjCpVt7T6w"
   },
   "source": [
    "The F1 score for the decision tree that was not hyperparameter tuned is 0.940940 and the F1 score for the hyperparameter-tuned decision tree is 0.945422. While ensuring that overfitting doesn't occur is necessary for some models, it didn't make a meaningful difference in improving this model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "46b88d61"
   },
   "source": [
    "### Plot the \"best\" decision tree\n",
    "\n",
    "Use the `plot_tree` function to produce a representation of the tree to pinpoint where the splits in the data are occurring. This will allow you to review the \"best\" decision tree."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "e5ab9b48"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,12))\n",
    "plot_tree(clf.best_estimator_, max_depth=2, fontsize=14, feature_names=X.columns);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "65d32f07"
   },
   "source": [
    "Which features did the model use first to sort the samples?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "090f4bd3"
   },
   "source": [
    "## Conclusion\n",
    "\n",
    "**What are some key takeaways that you learned from this lab?**\n",
    "\n",
    "Accuracy tends to be the metric that the stakeholders can best understand.\n",
    "\n",
    "Precision measures what proportion of predicted positives is truly positive. For example, if you wanted to not falsely claiming a customer is satisfied, precision would be a good metric. Assuming a customer is happy when they are really not might lead to customer churn.\n",
    "\n",
    "Recall measures the percentage of actual positives a model correctly identified (true positive). For this dataset, the airline might want to limit false negatives (actually satisfied people who are predicted to be unsatisfied). Assuming a customer is unhappy when the customer is happy can lead to the airline wasting resources trying to improve the customer experience of an already happy customer.\n",
    "\n",
    "F1 balances precision and recall. It is the harmonic mean of precision and recall, or their product divided by their sum."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Congratulations!** You've completed this lab. However, you may not notice a green check mark next to this item on Coursera's platform. Please continue your progress regardless of the check mark. Just click on the \"save\" icon at the top of this notebook to ensure your work has been logged\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "### BONUS STEP: Build another feature importance graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = clf.best_estimator_.feature_importances_\n",
    "\n",
    "forest_importances = pd.Series(importances, index=X.columns).sort_values(ascending=False)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "forest_importances.plot.bar(ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "provenance": []
  },
  "gpuClass": "standard",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}