{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesson 8 - Classification\n",
    "\n",
    "> In this lesson we bring together all the knowledge we have gained about Random Forests and apply it to a new type of supervised learning task: binary classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/lvwerra/dslectures/master?urlpath=lab/tree/notebooks%2Flesson08_classification.ipynb)[![slides](https://img.shields.io/static/v1?label=slides&message=2021-lesson08.pdf&color=blue&logo=Google-drive)](https://drive.google.com/open?id=1bu4Y859CpBFfHYKucuNeHVm0p3vMZB6-)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning objectives"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Know how to apply Random Forests to classification tasks\n",
    "* Understand the performance metrics associated with binary classification\n",
    "* Gain an introduction to fast.ai's data preprocessing functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This lesson is inspired by the following textbooks and online courses:\n",
    "\n",
    "* Chapter 3 of _Hands-On Machine Learning with Scikit-Learn and TensorFlow_ by Aurèlien Geron\n",
    "* Chapter 7 of _Data Science for Business_ by Provost and Fawcett\n",
    "* Lessons 1 - 4 of Jeremy Howard's fantastic online course [_Introduction to Machine Learning for Coders_](https://course18.fast.ai/ml)\n",
    "\n",
    "You may also find the following blog post useful:\n",
    "\n",
    "* [Grumpy, euphoric, and smart classifiers (interactive)](https://christian.bock.ml/posts/metrics/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Homework\n",
    "\n",
    "* Solve the exercises included in this notebook\n",
    "* Read chapter 3 of _Hands-On Machine Learning with Scikit-Learn and TensorFlow_ by Aurèlien Geron\n",
    "* Read chapter 7 of _Data Science for Business_ by Provost and Fawcett\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is customer churn?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"text-align: center\">\n",
    "<img src='images/churn.png' width='300'>\n",
    "    <p style=\"text-align: center;\"> <b>Figure reference:</b> https://s16353.pcdn.co/wp-content/uploads/2018/06/Churn.png </p>\n",
    "</div>\n",
    "\n",
    "We will explore [IBM's telecommunications dataset](https://www.kaggle.com/blastchar/telco-customer-churn) and determine which attributes are most informative for predicting customer retention (also known as customer churn). As described by IBM, the problem setting is as follows:\n",
    "\n",
    "> A telecommunications company is concerned about the number of customers leaving their landline business for cable competitors. They need to understand who is leaving. Imagine that you’re an analyst at this company and you have to find out who is leaving and why.\n",
    "\n",
    "The kind of questions we'd like to find answers to are:\n",
    "\n",
    "* Which customers are likely to leave?\n",
    "* Which attributes influence customers who leave?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As noted above, in this lesson we will analyse IBM's customer churn dataset:\n",
    "\n",
    "* `churn.csv`\n",
    "\n",
    "The dataset includes information about:\n",
    "\n",
    "* Customers who left within the last month – the column is called `Churn`\n",
    "* Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device protection, tech support, and streaming TV and movies\n",
    "* Customer account information – how long they’ve been a customer (tenure), contract, payment method, paperless billing, monthly charges, and total charges\n",
    "* Demographic info about customers – gender, whether they're a senior citizen or not, and if they have partners and dependents\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reload modules before executing user code\n",
    "%load_ext autoreload\n",
    "# reload all modules every time before executing Python code\n",
    "%autoreload 2\n",
    "# render plots in notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to update the library if working locally\n",
    "# !pip install dslectures --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from dslectures.core import (\n",
    "    get_dataset,\n",
    "    display_large,\n",
    "    convert_strings_to_categories,\n",
    "    rf_feature_importance,\n",
    "    plot_feature_importance,\n",
    "    plot_dendogram,\n",
    ")\n",
    "from dslectures.structured import proc_df\n",
    "from pathlib import Path\n",
    "\n",
    "# data viz\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import plot_confusion_matrix, plot_roc_curve\n",
    "from sklearn.tree import plot_tree\n",
    "\n",
    "sns.set(color_codes=True)\n",
    "sns.set_palette(sns.color_palette(\"muted\"))\n",
    "\n",
    "# ml magic\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import scipy\n",
    "from scipy.cluster import hierarchy as hc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Download of churn.csv dataset complete.\n"
     ]
    }
   ],
   "source": [
    "get_dataset(\"churn.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also make use of the `pathlib` library to handle our filepaths:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "churn.csv                  housing_merged.csv\n",
      "churn_processed.csv        housing_processed.csv\n",
      "housing.csv                submission.csv\n",
      "housing_addresses.csv      test.csv\n",
      "housing_gmaps_data_raw.csv train.csv\n"
     ]
    }
   ],
   "source": [
    "DATA = Path('../data/')\n",
    "!ls {DATA}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data = pd.read_csv(DATA / \"churn.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preview the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes you will find that the dataset has too many columns to be displayed with the standard `DataFrame.head()` method and just shows `...` for intermediate columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>...</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>Churn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity  ... DeviceProtection  \\\n",
       "0  No phone service             DSL             No  ...               No   \n",
       "1                No             DSL            Yes  ...              Yes   \n",
       "2                No             DSL            Yes  ...               No   \n",
       "3  No phone service             DSL            Yes  ...              Yes   \n",
       "4                No     Fiber optic             No  ...               No   \n",
       "\n",
       "  TechSupport StreamingTV StreamingMovies        Contract PaperlessBilling  \\\n",
       "0          No          No              No  Month-to-month              Yes   \n",
       "1          No          No              No        One year               No   \n",
       "2          No          No              No  Month-to-month              Yes   \n",
       "3         Yes          No              No        One year               No   \n",
       "4          No          No              No  Month-to-month              Yes   \n",
       "\n",
       "               PaymentMethod MonthlyCharges  TotalCharges Churn  \n",
       "0           Electronic check          29.85         29.85    No  \n",
       "1               Mailed check          56.95        1889.5    No  \n",
       "2               Mailed check          53.85        108.15   Yes  \n",
       "3  Bank transfer (automatic)          42.30       1840.75    No  \n",
       "4           Electronic check          70.70        151.65   Yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To fix that we can configure the [options in pandas](https://pandas.pydata.org/pandas-docs/version/0.15/options.html) which we can wrap inside a simple function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
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       "      <th>Dependents</th>\n",
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       "      <th>Churn</th>\n",
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       "      <th>0</th>\n",
       "      <td>7590-VHVEG</td>\n",
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       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
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       "      <td>Electronic check</td>\n",
       "      <td>29.85</td>\n",
       "      <td>29.85</td>\n",
       "      <td>No</td>\n",
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       "      <th>1</th>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>34</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>53.85</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>Male</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>45</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>One year</td>\n",
       "      <td>No</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9237-HQITU</td>\n",
       "      <td>Female</td>\n",
       "      <td>0</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Fiber optic</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Electronic check</td>\n",
       "      <td>70.70</td>\n",
       "      <td>151.65</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen Partner Dependents  tenure PhoneService  \\\n",
       "0  7590-VHVEG  Female              0     Yes         No       1           No   \n",
       "1  5575-GNVDE    Male              0      No         No      34          Yes   \n",
       "2  3668-QPYBK    Male              0      No         No       2          Yes   \n",
       "3  7795-CFOCW    Male              0      No         No      45           No   \n",
       "4  9237-HQITU  Female              0      No         No       2          Yes   \n",
       "\n",
       "      MultipleLines InternetService OnlineSecurity OnlineBackup  \\\n",
       "0  No phone service             DSL             No          Yes   \n",
       "1                No             DSL            Yes           No   \n",
       "2                No             DSL            Yes          Yes   \n",
       "3  No phone service             DSL            Yes           No   \n",
       "4                No     Fiber optic             No           No   \n",
       "\n",
       "  DeviceProtection TechSupport StreamingTV StreamingMovies        Contract  \\\n",
       "0               No          No          No              No  Month-to-month   \n",
       "1              Yes          No          No              No        One year   \n",
       "2               No          No          No              No  Month-to-month   \n",
       "3              Yes         Yes          No              No        One year   \n",
       "4               No          No          No              No  Month-to-month   \n",
       "\n",
       "  PaperlessBilling              PaymentMethod  MonthlyCharges TotalCharges  \\\n",
       "0              Yes           Electronic check           29.85        29.85   \n",
       "1               No               Mailed check           56.95       1889.5   \n",
       "2              Yes               Mailed check           53.85       108.15   \n",
       "3               No  Bank transfer (automatic)           42.30      1840.75   \n",
       "4              Yes           Electronic check           70.70       151.65   \n",
       "\n",
       "  Churn  \n",
       "0    No  \n",
       "1    No  \n",
       "2   Yes  \n",
       "3    No  \n",
       "4   Yes  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_large(churn_data.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you can take the transpose to see all the columns more easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <td>7590-VHVEG</td>\n",
       "      <td>5575-GNVDE</td>\n",
       "      <td>3668-QPYBK</td>\n",
       "      <td>7795-CFOCW</td>\n",
       "      <td>9237-HQITU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gender</th>\n",
       "      <td>Female</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Male</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partner</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dependents</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PhoneService</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MultipleLines</th>\n",
       "      <td>No phone service</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No phone service</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>InternetService</th>\n",
       "      <td>DSL</td>\n",
       "      <td>DSL</td>\n",
       "      <td>DSL</td>\n",
       "      <td>DSL</td>\n",
       "      <td>Fiber optic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OnlineBackup</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DeviceProtection</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TechSupport</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingTV</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>StreamingMovies</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contract</th>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>One year</td>\n",
       "      <td>Month-to-month</td>\n",
       "      <td>One year</td>\n",
       "      <td>Month-to-month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PaymentMethod</th>\n",
       "      <td>Electronic check</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Mailed check</td>\n",
       "      <td>Bank transfer (automatic)</td>\n",
       "      <td>Electronic check</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>29.85</td>\n",
       "      <td>56.95</td>\n",
       "      <td>53.85</td>\n",
       "      <td>42.3</td>\n",
       "      <td>70.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>29.85</td>\n",
       "      <td>1889.5</td>\n",
       "      <td>108.15</td>\n",
       "      <td>1840.75</td>\n",
       "      <td>151.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Churn</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0             1               2  \\\n",
       "customerID              7590-VHVEG    5575-GNVDE      3668-QPYBK   \n",
       "gender                      Female          Male            Male   \n",
       "SeniorCitizen                    0             0               0   \n",
       "Partner                        Yes            No              No   \n",
       "Dependents                      No            No              No   \n",
       "tenure                           1            34               2   \n",
       "PhoneService                    No           Yes             Yes   \n",
       "MultipleLines     No phone service            No              No   \n",
       "InternetService                DSL           DSL             DSL   \n",
       "OnlineSecurity                  No           Yes             Yes   \n",
       "OnlineBackup                   Yes            No             Yes   \n",
       "DeviceProtection                No           Yes              No   \n",
       "TechSupport                     No            No              No   \n",
       "StreamingTV                     No            No              No   \n",
       "StreamingMovies                 No            No              No   \n",
       "Contract            Month-to-month      One year  Month-to-month   \n",
       "PaperlessBilling               Yes            No             Yes   \n",
       "PaymentMethod     Electronic check  Mailed check    Mailed check   \n",
       "MonthlyCharges               29.85         56.95           53.85   \n",
       "TotalCharges                 29.85        1889.5          108.15   \n",
       "Churn                           No            No             Yes   \n",
       "\n",
       "                                          3                 4  \n",
       "customerID                       7795-CFOCW        9237-HQITU  \n",
       "gender                                 Male            Female  \n",
       "SeniorCitizen                             0                 0  \n",
       "Partner                                  No                No  \n",
       "Dependents                               No                No  \n",
       "tenure                                   45                 2  \n",
       "PhoneService                             No               Yes  \n",
       "MultipleLines              No phone service                No  \n",
       "InternetService                         DSL       Fiber optic  \n",
       "OnlineSecurity                          Yes                No  \n",
       "OnlineBackup                             No                No  \n",
       "DeviceProtection                        Yes                No  \n",
       "TechSupport                             Yes                No  \n",
       "StreamingTV                              No                No  \n",
       "StreamingMovies                          No                No  \n",
       "Contract                           One year    Month-to-month  \n",
       "PaperlessBilling                         No               Yes  \n",
       "PaymentMethod     Bank transfer (automatic)  Electronic check  \n",
       "MonthlyCharges                         42.3              70.7  \n",
       "TotalCharges                        1840.75            151.65  \n",
       "Churn                                    No               Yes  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.head().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The shape of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7043"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get number of rows\n",
    "len(churn_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 21)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get tuples of (n_rows, n_columns)\n",
    "churn_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, we see that we have 7043 customers and 21 variables or attributes that describe their telecom subscription. Let's have a look at the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['customerID', 'gender', 'SeniorCitizen', 'Partner', 'Dependents',\n",
       "       'tenure', 'PhoneService', 'MultipleLines', 'InternetService',\n",
       "       'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport',\n",
       "       'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling',\n",
       "       'PaymentMethod', 'MonthlyCharges', 'TotalCharges', 'Churn'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: As explained in the summary, the _**target attribute**_ is `Churn` and thus we have a _**classification problem**_ (rather than regression) because the target is a _**category**_ (Yes or No) rather than a coninuous number."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unique values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Whenever we see an ID column like `Id`, it is useful to perform a sanity check that each value is unique. Otherwise it may be possible that you have duplicates in your data that can bias your models and hence conclusions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7043"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data[\"customerID\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good! The number of unique IDs matches the number of rows in our DataFrame."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID           object\n",
       "gender               object\n",
       "SeniorCitizen         int64\n",
       "Partner              object\n",
       "Dependents           object\n",
       "tenure                int64\n",
       "PhoneService         object\n",
       "MultipleLines        object\n",
       "InternetService      object\n",
       "OnlineSecurity       object\n",
       "OnlineBackup         object\n",
       "DeviceProtection     object\n",
       "TechSupport          object\n",
       "StreamingTV          object\n",
       "StreamingMovies      object\n",
       "Contract             object\n",
       "PaperlessBilling     object\n",
       "PaymentMethod        object\n",
       "MonthlyCharges      float64\n",
       "TotalCharges         object\n",
       "Churn                object\n",
       "dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm, `TotalCharges` is of type **object** (i.e. string) even though it is clearly a float. Since null values or NaNs don't produce this behaviour, there are presumably empty strings lurking in this column. Let's test this hypothesis using `DataFrame.value_counts()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20.2       11\n",
       "           11\n",
       "19.75       9\n",
       "20.05       8\n",
       "19.9        8\n",
       "           ..\n",
       "514.75      1\n",
       "676.35      1\n",
       "6510.45     1\n",
       "428.45      1\n",
       "6004.85     1\n",
       "Name: TotalCharges, Length: 6531, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data[\"TotalCharges\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will deal with this empty strings in the preprocessing steps below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall in our housing analysis, we needed to perform three main steps to bring out DataFrame to a form suitable for training a Random Forest on:\n",
    "\n",
    "* Convert strings to categorical data type\n",
    "* Fill missing values\n",
    "* Numericalise the DataFrame and create a features matrix $X$ and target vector $y$\n",
    "* Create train and validation sets\n",
    "\n",
    "Let's perform each of those steps below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert strings to categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we convert all the string columns to pandas' categorical data type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "convert_strings_to_categories(churn_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          category\n",
       "gender              category\n",
       "SeniorCitizen          int64\n",
       "Partner             category\n",
       "Dependents          category\n",
       "tenure                 int64\n",
       "PhoneService        category\n",
       "MultipleLines       category\n",
       "InternetService     category\n",
       "OnlineSecurity      category\n",
       "OnlineBackup        category\n",
       "DeviceProtection    category\n",
       "TechSupport         category\n",
       "StreamingTV         category\n",
       "StreamingMovies     category\n",
       "Contract            category\n",
       "PaperlessBilling    category\n",
       "PaymentMethod       category\n",
       "MonthlyCharges       float64\n",
       "TotalCharges        category\n",
       "Churn               category\n",
       "dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is almost correct, although a closer look at `SeniorCitizen` reveals that it refers to a binary feature and thus should also be categorical:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data[\"SeniorCitizen\"].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can fix this easily by simply changing the data type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_data[\"SeniorCitizen\"] = churn_data[\"SeniorCitizen\"].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID          category\n",
       "gender              category\n",
       "SeniorCitizen       category\n",
       "Partner             category\n",
       "Dependents          category\n",
       "tenure                 int64\n",
       "PhoneService        category\n",
       "MultipleLines       category\n",
       "InternetService     category\n",
       "OnlineSecurity      category\n",
       "OnlineBackup        category\n",
       "DeviceProtection    category\n",
       "TechSupport         category\n",
       "StreamingTV         category\n",
       "StreamingMovies     category\n",
       "Contract            category\n",
       "PaperlessBilling    category\n",
       "PaymentMethod       category\n",
       "MonthlyCharges       float64\n",
       "TotalCharges        category\n",
       "Churn               category\n",
       "dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sanity check on the transformation\n",
    "churn_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fill missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A quick way to test for missing values is to apply the `isna` method from pandas and calculate the sum of missing values in our DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn               0.0\n",
       "OnlineSecurity      0.0\n",
       "gender              0.0\n",
       "SeniorCitizen       0.0\n",
       "Partner             0.0\n",
       "Dependents          0.0\n",
       "tenure              0.0\n",
       "PhoneService        0.0\n",
       "MultipleLines       0.0\n",
       "InternetService     0.0\n",
       "OnlineBackup        0.0\n",
       "TotalCharges        0.0\n",
       "DeviceProtection    0.0\n",
       "TechSupport         0.0\n",
       "StreamingTV         0.0\n",
       "StreamingMovies     0.0\n",
       "Contract            0.0\n",
       "PaperlessBilling    0.0\n",
       "PaymentMethod       0.0\n",
       "MonthlyCharges      0.0\n",
       "customerID          0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(churn_data.isna().sum() / len(churn_data)).sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, it looks like we're lucky and have a pre-cleaned dataset!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create feature matrix and target vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have done some basic preprocessing, the final step is to numericalise the `pandas.DataFrame` and create the feature matrix $X$ and target vector $y$. In previous lessons we created some functions to automate these steps. Below we use fast.ai's utility function `proc_df` to wrap all these steps into a single step:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y, nas = proc_df(churn_data, \"Churn\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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>customerID</th>\n",
       "      <th>gender</th>\n",
       "      <th>SeniorCitizen</th>\n",
       "      <th>Partner</th>\n",
       "      <th>Dependents</th>\n",
       "      <th>tenure</th>\n",
       "      <th>PhoneService</th>\n",
       "      <th>MultipleLines</th>\n",
       "      <th>InternetService</th>\n",
       "      <th>OnlineSecurity</th>\n",
       "      <th>OnlineBackup</th>\n",
       "      <th>DeviceProtection</th>\n",
       "      <th>TechSupport</th>\n",
       "      <th>StreamingTV</th>\n",
       "      <th>StreamingMovies</th>\n",
       "      <th>Contract</th>\n",
       "      <th>PaperlessBilling</th>\n",
       "      <th>PaymentMethod</th>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5376</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>29.85</td>\n",
       "      <td>2506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3963</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>56.95</td>\n",
       "      <td>1467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2565</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>53.85</td>\n",
       "      <td>158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5536</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>42.30</td>\n",
       "      <td>1401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6512</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>70.70</td>\n",
       "      <td>926</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  gender  SeniorCitizen  Partner  Dependents  tenure  \\\n",
       "0        5376       1              1        2           1       1   \n",
       "1        3963       2              1        1           1      34   \n",
       "2        2565       2              1        1           1       2   \n",
       "3        5536       2              1        1           1      45   \n",
       "4        6512       1              1        1           1       2   \n",
       "\n",
       "   PhoneService  MultipleLines  InternetService  OnlineSecurity  OnlineBackup  \\\n",
       "0             1              2                1               1             3   \n",
       "1             2              1                1               3             1   \n",
       "2             2              1                1               3             3   \n",
       "3             1              2                1               3             1   \n",
       "4             2              1                2               1             1   \n",
       "\n",
       "   DeviceProtection  TechSupport  StreamingTV  StreamingMovies  Contract  \\\n",
       "0                 1            1            1                1         1   \n",
       "1                 3            1            1                1         2   \n",
       "2                 1            1            1                1         1   \n",
       "3                 3            3            1                1         2   \n",
       "4                 1            1            1                1         1   \n",
       "\n",
       "   PaperlessBilling  PaymentMethod  MonthlyCharges  TotalCharges  \n",
       "0                 2              3           29.85          2506  \n",
       "1                 1              4           56.95          1467  \n",
       "2                 2              4           53.85           158  \n",
       "3                 1              1           42.30          1401  \n",
       "4                 2              3           70.70           926  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display_large(X.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7043, 20), (7043,))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape, y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For future use we can save our processed quantities:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "churn_processed = X.join(pd.Series(y, name=\"Churn\"))\n",
    "\n",
    "churn_processed.to_csv(DATA / \"churn_processed.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create train and validation sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5634 train rows + 1409 valid rows\n"
     ]
    }
   ],
   "source": [
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42\n",
    ")\n",
    "print(f\"{len(X_train)} train rows + {len(X_valid)} valid rows\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select a performance measure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating classifiers is often significantly trickier than evaluating a regressor. One way to do this is to compare the accuracy of each classifier, where \n",
    "\n",
    "$$ \\mbox{accuracy} = \\frac{\\mbox{Number of correct decisions made}}{\\mbox{Total number of decisions made}} $$\n",
    "\n",
    "In general, however, accuracy is _**not**_ the preferred performance measures for classifiers, especially when you are dealing with _**skewed datasets**_ (i.e. when some classes are much more frequent than others). For our churn example, supose we build a model that generates 75% accuracy. Is this any good? Let's have a look at the distribution of churn in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Churn\", data=churn_data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No     0.73463\n",
       "Yes    0.26537\n",
       "Name: Churn, dtype: float64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "churn_data[\"Churn\"].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot and numbers we see that the \"No Churn\" and \"Churn\" classes appear in approximately a 3:1 ratio. If we built a dumb classifier that just classifies every single customer as \"No Churn\", then we would be right about 73.5% of the time! In practice skews of 99:1 are common, for which a report of 99% accuracy is somewhat meaningless."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A much better way to evaluate the performance of a classifier is to look at the _**confusion matrix.**_ Recall that a confusion matrix for a problem involving $n$ classes is an $n\\times n$ matrix with the rows labelled by the _**actual**_ classes and the columns with the _**predicted**_ classes. Our churn example is a two-class problem (\"Churn\" vs \"No Churn\"), so the confusion matrix is $2\\times 2$.\n",
    "\n",
    "If we denote the true classes as $\\mathbf{p}$(positive) and $\\mathbf{n}$(egative), and the classes predicted by the model as $\\mathbf{Y}$(es) and $\\mathbf{N}$(o) then the confusion matrix has the form:\n",
    "\n",
    "| &nbsp; | **N** | **Y** | \n",
    "| :---: | :---: | :---: |\n",
    "| **n** | True negatives | False positives | \n",
    "| **p** | False negatives | True positives |\n",
    "\n",
    "The main diagonal contains the counts of correct decisions. The errors of the classifier are the _**false negatives**_ (positives classified as negative) and **false positives** (negatives classified as positive)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**You should know**\n",
    "\n",
    "In the _Data Science for Business_ textbook, the confusion matrix is given a different layout, namely the columns a relabelled by the actual classes and the rows by the predicted classes: \n",
    "\n",
    "| &nbsp; | **p** | **n** | \n",
    "| :---: | :---: | :---: |\n",
    "| **Y** | True positives | False positives | \n",
    "| **P** | False negatives | True negatives |\n",
    "\n",
    "The layout adopted above and in this notebook is the one produced by scikit-learn, since we'd like to make use of the in-built functions included in that library.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Baseline model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's begin by creating a baseline Random Forest model to build upon. First we need a scoring function for classifiers, similar to our $R^2$ and RMSE function for regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_scores(fitted_model):\n",
    "    res = {\n",
    "        \"Accuracy on train:\": accuracy_score(fitted_model.predict(X_train), y_train),\n",
    "        \"ROC AUC on train:\": roc_auc_score(\n",
    "            y_train, fitted_model.predict_proba(X_train)[:, 1]\n",
    "        ),\n",
    "        \"Accuracy on valid:\": accuracy_score(fitted_model.predict(X_valid), y_valid),\n",
    "        \"ROC AUC on valid:\": roc_auc_score(\n",
    "            y_valid, fitted_model.predict_proba(X_valid)[:, 1]\n",
    "        ),\n",
    "    }\n",
    "    if hasattr(fitted_model, \"oob_score_\"):\n",
    "        res[\"OOB accuracy:\"] = fitted_model.oob_score_\n",
    "\n",
    "    for k, v in res.items():\n",
    "        print(k, round(v, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
       "                       oob_score=False, random_state=42, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(n_estimators=10, n_jobs=-1, random_state=42)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train: 0.984\n",
      "ROC AUC on train: 0.999\n",
      "Accuracy on valid: 0.771\n",
      "ROC AUC on valid: 0.801\n"
     ]
    }
   ],
   "source": [
    "print_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC Curves and AUC\n",
    "Note that in addition to accuracy, we also show a second value, the _**Area Under the ROC Curve**_ (AUC). The AUC is a good summary statistic of the predictiveness of a binary classifier. It varies from zero to one. A value of 0.5 corresponds to randomness (the classifier cannot distinguish at all between \"churn\" and \"no churn\") and a value of 1.0 means that it is perfect.\n",
    "\n",
    "The \"ROC\" refers to the Receiver Operating Characteristic (ROC) curve which plots the _true positive rate_ \n",
    "\n",
    "$$ \\mbox{TPR} = \\frac{\\mbox{TP}}{\\mbox{TP} + \\mbox{FP}} \\,, \\qquad \\mbox{TP (FP)}  = \\mbox{number of true (false) positives}\\,,$$\n",
    "\n",
    "against the _false positive rate_ FPR, where the FPR is the ratio of negative instances that are incorrectly classified as positive. In general there is a tradeoff between these two quantities: the higher the TPR, the more false positives (FPR) the classifier produces. A good classifiers stays as close to the top-left corner of a ROC curve plot as possible.\n",
    "\n",
    "In scikit-learn we can visualise the ROC curve of an estimator using the plotting API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(model, X_valid, y_valid)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we plot the confusion matrix using scikit-learn's plotting API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# extract labels for classes\n",
    "class_names = churn_data[\"Churn\"].cat.categories\n",
    "\n",
    "plot_confusion_matrix(\n",
    "    model,\n",
    "    X_valid,\n",
    "    y_valid,\n",
    "    display_labels=class_names,\n",
    "    cmap=plt.cm.Blues,\n",
    "    normalize=\"true\",\n",
    ")\n",
    "plt.grid(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the confusion matrix we see that our baseline model is able to identify churners only 42% of the time and incorrectly classifies people who churned 58% of the time. We can do better, but first let's inspect how a single decision tree is making decisions on this data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Single tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have a baseline model, let's make a single tree so we can gain some insight into how the decisions are being made:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=3, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=1, n_jobs=-1,\n",
       "                       oob_score=False, random_state=42, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier(\n",
    "    n_estimators=1, max_depth=3, bootstrap=False, n_jobs=-1, random_state=42\n",
    ")\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get column names\n",
    "feature_names = X_train.columns\n",
    "# we need to specify the background color because of a bug in sklearn\n",
    "fig, ax = plt.subplots(figsize=(30, 10), facecolor=\"k\")\n",
    "# generate tree plot\n",
    "plot_tree(model.estimators_[0], filled=True, feature_names=feature_names, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main difference here compared to our housing example is that the splitting criterion is no longer the mean squared error, but instead is something known as the **_Gini index_**:\n",
    "\n",
    "$$ G = 1 - \\sum_{i=1}^n p_i^2 $$\n",
    "\n",
    "where $p_i$ is the probability of an object being classified to a particular class (in our case \"Yes\" or \"No\"). For classification tasks, the goal is to _minimise_ the Gini index across each split, which amounts to finding which segments are most \"pure\".\n",
    "\n",
    "From the figure, we can already start seeing some features that might be interesting for predicting churn, e.g. `TotalCharges`, `tenure`, and `TechSupport` seem like good indicators."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our baseline model has an accuracy of 0.771 and ROC AUC score of 0.801 on the validation set. Let's now examine whether we can improve this by tuning the:\n",
    "\n",
    "* number of trees in the forest\n",
    "* minimum number of samples per leaf\n",
    "* maximum number of features per split\n",
    "\n",
    "In our previous lessons we manually inspected how the performance evolved when we changed these hyperparameters one at a time. Instead we can automate this process using scikit-learn's `GridSearchCV` to search for the best combination of hyperparameter values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# define range of values for each hyperparameter\n",
    "param_grid = [\n",
    "    {\n",
    "        \"n_estimators\": [10, 20, 40, 80, 100],\n",
    "        \"max_features\": [0.5, 1.0, \"sqrt\", \"log2\"],\n",
    "        \"min_samples_leaf\": [1, 3, 5, 10, 25],\n",
    "    }\n",
    "]\n",
    "\n",
    "# instantiate baseline model\n",
    "model = RandomForestClassifier(n_estimators=10, n_jobs=-1, random_state=42)\n",
    "\n",
    "# initialise grid search with cross-validation\n",
    "grid_search = GridSearchCV(\n",
    "    model, param_grid=param_grid, cv=3, scoring=\"roc_auc\", n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.15 s, sys: 285 ms, total: 2.44 s\n",
      "Wall time: 17.3 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, error_score=nan,\n",
       "             estimator=RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
       "                                              class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features='auto',\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              max_samples=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",
       "                                              n_estimators=10, n_jobs=-1,\n",
       "                                              oob_score=False, random_state=42,\n",
       "                                              verbose=0, warm_start=False),\n",
       "             iid='deprecated', n_jobs=-1,\n",
       "             param_grid=[{'max_features': [0.5, 1.0, 'sqrt', 'log2'],\n",
       "                          'min_samples_leaf': [1, 3, 5, 10, 25],\n",
       "                          'n_estimators': [10, 20, 40, 80, 100]}],\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring='roc_auc', verbose=0)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%time grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the search is finished, we can get the best combination of parameters as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_features': 'sqrt', 'min_samples_leaf': 25, 'n_estimators': 80}"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params = grid_search.best_params_\n",
    "best_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly we can get the best model in the search:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='sqrt',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=25, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=80, n_jobs=-1,\n",
       "                       oob_score=False, random_state=42, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_model = grid_search.best_estimator_\n",
    "best_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how this model performs on our validation set in terms of metrics and the confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train: 0.819\n",
      "ROC AUC on train: 0.876\n",
      "Accuracy on valid: 0.828\n",
      "ROC AUC on valid: 0.891\n"
     ]
    }
   ],
   "source": [
    "print_scores(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(\n",
    "    best_model,\n",
    "    X_valid,\n",
    "    y_valid,\n",
    "    display_labels=class_names,\n",
    "    cmap=plt.cm.Blues,\n",
    "    normalize=\"true\",\n",
    ")\n",
    "plt.grid(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In terms of the AUC score, we see about a 10% boost over our baseline model - not bad! Our confusion matrix has also visibly improved."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model interpretability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we did in the housing example, we now examine which features were deemed to be important for our Random Forest model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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>Column</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Contract</td>\n",
       "      <td>0.228229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>tenure</td>\n",
       "      <td>0.208876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>OnlineSecurity</td>\n",
       "      <td>0.098802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>MonthlyCharges</td>\n",
       "      <td>0.092676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TechSupport</td>\n",
       "      <td>0.069717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>InternetService</td>\n",
       "      <td>0.056771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>TotalCharges</td>\n",
       "      <td>0.049491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>PaymentMethod</td>\n",
       "      <td>0.033299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>OnlineBackup</td>\n",
       "      <td>0.030297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>DeviceProtection</td>\n",
       "      <td>0.029809</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Column  Importance\n",
       "15          Contract    0.228229\n",
       "5             tenure    0.208876\n",
       "9     OnlineSecurity    0.098802\n",
       "18    MonthlyCharges    0.092676\n",
       "12       TechSupport    0.069717\n",
       "8    InternetService    0.056771\n",
       "19      TotalCharges    0.049491\n",
       "17     PaymentMethod    0.033299\n",
       "10      OnlineBackup    0.030297\n",
       "11  DeviceProtection    0.029809"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# expected shape - (n_features, 2)\n",
    "feature_importance = rf_feature_importance(best_model, X)\n",
    "\n",
    "# peek at top 10\n",
    "feature_importance[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(feature_importance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot, it seems that the feature importance flattens out around 0.005, so let's use that as a threshold to remove some uninformative features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_threshold = 0.008\n",
    "cols_to_keep = feature_importance[\n",
    "    feature_importance[\"Importance\"] > feature_importance_threshold\n",
    "][\"Column\"]\n",
    "\n",
    "len(cols_to_keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15            Contract\n",
       "5               tenure\n",
       "9       OnlineSecurity\n",
       "18      MonthlyCharges\n",
       "12         TechSupport\n",
       "8      InternetService\n",
       "19        TotalCharges\n",
       "17       PaymentMethod\n",
       "10        OnlineBackup\n",
       "11    DeviceProtection\n",
       "0           customerID\n",
       "16    PaperlessBilling\n",
       "7        MultipleLines\n",
       "14     StreamingMovies\n",
       "Name: Column, dtype: object"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_keep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's retrain with this subset and see if the score drop or not (for reference, we have accuracy of 0.807 and AUC of  0.871 on the validation set):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a copy of the data with selected columns and create new train / test set\n",
    "X_keep = X.copy()[cols_to_keep]\n",
    "X_train, X_valid = train_test_split(X_keep, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train: 0.802\n",
      "ROC AUC on train: 0.852\n",
      "Accuracy on valid: 0.798\n",
      "ROC AUC on valid: 0.862\n",
      "OOB accuracy: 0.793\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(\n",
    "    n_estimators=best_params[\"n_estimators\"],\n",
    "    min_samples_leaf=best_params[\"n_estimators\"],\n",
    "    max_features=best_params[\"max_features\"],\n",
    "    n_jobs=-1,\n",
    "    oob_score=True,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "print_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The score has not changed significantly, so let's continue and replot the feature importances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = rf_feature_importance(model, X_keep)\n",
    "plot_feature_importance(feature_importance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One hot encoding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also examine whether one-hot encoding produces a better representation of the data, and hence a better model. As we did in the housing example, we need to create a new feature matrix and target vector, along with a threshold on the maximum number of categories allowed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train: 0.813\n",
      "ROC AUC on train: 0.869\n",
      "Accuracy on valid: 0.806\n",
      "ROC AUC on valid: 0.863\n",
      "OOB accuracy: 0.797\n"
     ]
    }
   ],
   "source": [
    "X2, y2, nas = proc_df(churn_data, \"Churn\", max_n_cat=7)\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X2, y2, test_size=0.2, random_state=42\n",
    ")\n",
    "\n",
    "model = RandomForestClassifier(\n",
    "    n_estimators=best_params[\"n_estimators\"],\n",
    "    min_samples_leaf=best_params[\"min_samples_leaf\"],\n",
    "    max_features=best_params[\"max_features\"],\n",
    "    n_jobs=-1,\n",
    "    oob_score=True,\n",
    "    random_state=42,\n",
    ")\n",
    "model.fit(X_train, y_train)\n",
    "print_scores(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = rf_feature_importance(model, X2)\n",
    "plot_feature_importance(feature_importance[:30])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This encoding actually gives us a slightly better model and has the added bonus of being slightly more interpretable since we can easily read off which characteristics about the customers are influencing the prediction. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Note: In general, you will not know in advance whether one-hot encoding will improve the performance of your model - the only way to know is to test it out eexperimentally. In our example, the increase in performance suggests that some individual categories are more predictive than the rest, thus allowing the Random Forest to focus on these features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's trim the features as before and see if we can improve this model further still:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance_threshold = 0.007\n",
    "cols_to_keep = feature_importance[\n",
    "    feature_importance[\"Importance\"] > feature_importance_threshold\n",
    "][\"Column\"]\n",
    "\n",
    "len(cols_to_keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a copy of the data with selected columns and create new train / test set\n",
    "X_keep = X2.copy()[cols_to_keep]\n",
    "X_train, X_valid = train_test_split(X_keep, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train: 0.813\n",
      "ROC AUC on train: 0.871\n",
      "Accuracy on valid: 0.809\n",
      "ROC AUC on valid: 0.863\n",
      "OOB accuracy: 0.798\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(\n",
    "    n_estimators=best_params[\"n_estimators\"],\n",
    "    min_samples_leaf=best_params[\"min_samples_leaf\"],\n",
    "    max_features=best_params[\"max_features\"],\n",
    "    n_jobs=-1,\n",
    "    oob_score=True,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "print_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, the model has not improved, but we have managed to reduce the number of features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = rf_feature_importance(model, X_keep)\n",
    "plot_feature_importance(feature_importance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove redundant features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still have a lot of features to contend with, so let's see if any of them are redundant. As in the housing example, we use clustering to work out which pairs of features are \"close\" in some sense:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_dendogram(X_keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_oob(df):\n",
    "    model = RandomForestClassifier(\n",
    "        n_estimators=best_params[\"n_estimators\"],\n",
    "        min_samples_leaf=best_params[\"min_samples_leaf\"],\n",
    "        max_features=best_params[\"max_features\"],\n",
    "        n_jobs=-1,\n",
    "        oob_score=True,\n",
    "        random_state=42,\n",
    "    )\n",
    "    X, _ = train_test_split(df, test_size=0.2, random_state=42)\n",
    "    model.fit(X, y_train)\n",
    "    return model.oob_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7976570820021299"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_oob(X_keep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TechSupport_Yes 0.7976570820021299\n",
      "OnlineSecurity_Yes 0.7971246006389776\n",
      "Contract_Two year 0.7955271565495208\n",
      "tenure 0.7834575789847356\n",
      "OnlineSecurity_No internet service 0.7974795882144124\n",
      "DeviceProtection_No internet service 0.7974795882144124\n",
      "StreamingTV_No internet service 0.7969471068512602\n",
      "InternetService_No 0.7969471068512602\n",
      "TechSupport_No 0.7980120695775648\n",
      "OnlineSecurity_No 0.7996095136670217\n",
      "MonthlyCharges 0.7946396876109336\n",
      "InternetService_Fiber optic 0.7941072062477813\n"
     ]
    }
   ],
   "source": [
    "for c in (\n",
    "    \"TechSupport_Yes\",\n",
    "    \"OnlineSecurity_Yes\",\n",
    "    \"Contract_Two year\",\n",
    "    \"tenure\",\n",
    "    \"OnlineSecurity_No internet service\",\n",
    "    \"DeviceProtection_No internet service\",\n",
    "    \"StreamingTV_No internet service\",\n",
    "    \"InternetService_No\",\n",
    "    \"TechSupport_No\",\n",
    "    \"OnlineSecurity_No\",\n",
    "    \"MonthlyCharges\",\n",
    "    \"InternetService_Fiber optic\",\n",
    "):\n",
    "    print(c, get_oob(X_keep.drop(c, axis=1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7978345757898474"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_to_drop = [\n",
    "    \"OnlineSecurity_Yes\",\n",
    "    \"DeviceProtection_No internet service\",\n",
    "    \"StreamingTV_No internet service\",\n",
    "    \"OnlineSecurity_No\",\n",
    "]\n",
    "get_oob(X_keep.drop(cols_to_drop, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good, our OOB accuracy has not changed and we've managed to remove a few features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_keep.drop(cols_to_drop, axis=1, inplace=True)\n",
    "X_train, X_valid = train_test_split(X_keep, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train: 0.817\n",
      "ROC AUC on train: 0.873\n",
      "Accuracy on valid: 0.804\n",
      "ROC AUC on valid: 0.864\n",
      "OOB accuracy: 0.798\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier(\n",
    "    n_estimators=best_params[\"n_estimators\"],\n",
    "    min_samples_leaf=best_params[\"min_samples_leaf\"],\n",
    "    max_features=best_params[\"max_features\"],\n",
    "    n_jobs=-1,\n",
    "    oob_score=True,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "model.fit(X_train, y_train)\n",
    "print_scores(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = rf_feature_importance(model, X_keep)\n",
    "plot_feature_importance(feature_importance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering and our final model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One thing we consistently see in our feature importance is the role of the `tenure` attribute. One way to gain a deeper insight into this attribute is to consider converting it to a categorical variable. Continuous data is often discretised or separated into \"bins\" for analysis. This is typically done when you want to group people into discrete age buckets or in the case of our data, discrete `tenure` buckets. Let's have a look at the distribution of `tenure` values to get an idea about how to discretise the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(X_keep[\"tenure\"], kde=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot, we could reasonably divide the `tenure` values into bins of 0 to 6, 7 to 20, 21 to 40, 41 to 60, and finally 61 and over. To do so we use the `cut` function from pandas: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = [0, 6, 20, 40, 60, 100]\n",
    "group_names = [\"<=6\", \"7-20\", \"21-40\", \"41-60\", \"60+\"]\n",
    "X_keep[\"tenure_categories\"] = pd.cut(\n",
    "    X_keep[\"tenure\"], bins, labels=group_names, include_lowest=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the object pandas returns is also a special `Categorical` object. We can also examine the value counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<=6      1481\n",
       "21-40    1408\n",
       "60+      1407\n",
       "7-20     1397\n",
       "41-60    1350\n",
       "Name: tenure_categories, dtype: int64"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_keep[\"tenure_categories\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally let's drop the `tenure` column since it's redundant:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_keep.drop([\"tenure\"], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since `tenure_cats` is a string, we also need to one-hot encode it like the rest of our features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_keep = pd.get_dummies(X_keep)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we train our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on train: 0.815\n",
      "ROC AUC on train: 0.866\n",
      "Accuracy on valid: 0.805\n",
      "ROC AUC on valid: 0.861\n",
      "OOB accuracy: 0.799\n"
     ]
    }
   ],
   "source": [
    "X_train, X_valid = train_test_split(X_keep, test_size=0.2, random_state=42)\n",
    "\n",
    "model = RandomForestClassifier(\n",
    "    n_estimators=1000,\n",
    "    min_samples_leaf=best_params[\"min_samples_leaf\"],\n",
    "    max_features=best_params[\"max_features\"],\n",
    "    n_jobs=-1,\n",
    "    oob_score=True,\n",
    "    random_state=42,\n",
    ")\n",
    "model.fit(X_train, y_train)\n",
    "print_scores(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although categorising the `tenure` feature did not boost performance, it does help considerably with the interpretation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_importance = rf_feature_importance(model, X_keep)\n",
    "plot_feature_importance(feature_importance)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot we can see that the following 3 features are the most important indicators of churn:\n",
    "\n",
    "* Customers with month to month contracts\n",
    "* Customers who have been with the Telco for less than 6 months\n",
    "* Customers who do not have tech support\n",
    "\n",
    "From our analysis we could advise the client to focus on customers with these subscriptions and thereby minimise the risk that they churn."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's wrap up by plotting the confusion matrix of our final model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(\n",
    "    model,\n",
    "    X_valid,\n",
    "    y_valid,\n",
    "    display_labels=class_names,\n",
    "    cmap=plt.cm.Blues,\n",
    "    normalize=\"true\",\n",
    ")\n",
    "plt.grid(None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this figure we can conclude that:\n",
    "\n",
    "* If our model predicts that a customer will churn, then we can expect it to be correct 51% of the time (true positive)\n",
    "* If our model predict that a customer will _not_ churn, the we can expect the prediction to be correct 91% of the time (true negative)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Exercise\n",
    "\n",
    "Use the techniques in this lesson to solve Kaggle's famous [Titanic Challenge](https://www.kaggle.com/c/titanic). As in the housing challenge from lesson 6, the goal is not to build a perfect model, but to get familiar with building models on new datasets.\n",
    "\n",
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}