customer churn survival analysis.ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "sns.set_style(\"whitegrid\")\n",
    "sns.set_palette(\"muted\")\n",
    "\n",
    "import patsy\n",
    "import lifelines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A Crash Course in Survival Analysis: Analyzing Customer Churn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "Customer churn is familiar to many companies offering subscriptions services. Simply put, customer churn is the event of a customer opting out of his subscription. They may do so for different reasons: dissatisfaction of their mobile plan because of consistently poor reception, the allure of better subscription packages from competitors, or other reasons.\n",
    "\n",
    "Businesses want to understand how and why their customers churn to improve their profits. In this blog post, we'll explore a branch of statistics called *survival analysis* to uncover insights that will be useful to curb churn. We'll use a churn dataset from a [blog from IBM Watson analytics community](https://www.ibm.com/communities/analytics/watson-analytics-blog/predictive-insights-in-the-telco-customer-churn-data-set/) that describes a fictitious telco's customers and how long they stayed before they churned. "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival Analysis\n",
    "\n",
    "Survival analysis has been traditionally used in medicine in the life sciences to analyze how long it takes before a person dies  - hence the \"survival\" in survival analysis. The field however can be used to model other events that organizations care about, such as the failure of a machine, or customer churn. Okay cool. But what are the kinds of insights we can get from survival analysis? \n",
    "\n",
    "We'll talk about two main ideas: survival curves, and survival regression. We'll discuss what they are, and what kinds of insights they bring to the table.\n",
    "\n",
    "1. Survival Curves\n",
    "<img src = \"images/survival_curve.gif\">\n",
    "    - What we can do with it: \n",
    "        - Show how the likelihood of customer churn changes **over time**.\n",
    "        - Determining optimal intervention point.\n",
    "    - Questions it can answer:\n",
    "        - How many years/months on average do our customers stay?\n",
    "        - How long do male customers stay compared to female customers?\n",
    "        - Is our understanding of our customer lifecycle accurate with reality?\n",
    "2. Survival Regression\n",
    "    - What we can do with it: \n",
    "        - Model the relationship between customer churn, time, and other customer characteristics.\n",
    "    - Questions it can answer:\n",
    "        - What's the probability that this customer who is a female non-senior citizen with dependents will stay for 2 years?\n",
    "        - What are the significant factors that drive churn?\n",
    "        \n",
    "Survival analysis can be applied to industries other than telco [(source)](http://www.barryanalytics.com/Downloads/Presentations/Survival%20Analysis.pdf):\n",
    "> \n",
    "- Insurance - time to lapsing on policy.\n",
    "- Mortgages - time to mortgage redemption\n",
    "- Mail Order Catalogue - time to next purchase\n",
    "- Retail - time till food customer starts purchasing non-food\n",
    "- Manufacturing - lifetime of a machine component\n",
    "- Public Sector - time intervals to critical events"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The Dataset\n",
    "\n",
    "Let's get started by examining our sample churn dataset from IBM. Our dataset has 7043 customers and 21 variables. Most of the variables are categorical. "
   ]
  },
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   "source": [
    "df = pd.read_csv(\"data/Telco-Customer-Churn.csv\")\n",
    "df.replace(r\"^\\s+\", np.nan, inplace = True, regex = True)\n",
    "df[\"TotalCharges\"] = df.TotalCharges.apply(lambda x: float(x))"
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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    {
     "data": {
      "text/html": [
       "<div>\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>1889.50</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       1889.50     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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "customerID ['7590-VHVEG' '5575-GNVDE' '3668-QPYBK' ..., '4801-JZAZL' '8361-LTMKD'\n",
      " '3186-AJIEK']\n",
      "gender ['Female' 'Male']\n",
      "SeniorCitizen [0 1]\n",
      "Partner ['Yes' 'No']\n",
      "Dependents ['No' 'Yes']\n",
      "tenure [ 1 34  2 45  8 22 10 28 62 13 16 58 49 25 69 52 71 21 12 30 47 72 17 27  5\n",
      " 46 11 70 63 43 15 60 18 66  9  3 31 50 64 56  7 42 35 48 29 65 38 68 32 55\n",
      " 37 36 41  6  4 33 67 23 57 61 14 20 53 40 59 24 44 19 54 51 26  0 39]\n",
      "PhoneService ['No' 'Yes']\n",
      "MultipleLines ['No phone service' 'No' 'Yes']\n",
      "InternetService ['DSL' 'Fiber optic' 'No']\n",
      "OnlineSecurity ['No' 'Yes' 'No internet service']\n",
      "OnlineBackup ['Yes' 'No' 'No internet service']\n",
      "DeviceProtection ['No' 'Yes' 'No internet service']\n",
      "TechSupport ['No' 'Yes' 'No internet service']\n",
      "StreamingTV ['No' 'Yes' 'No internet service']\n",
      "StreamingMovies ['No' 'Yes' 'No internet service']\n",
      "Contract ['Month-to-month' 'One year' 'Two year']\n",
      "PaperlessBilling ['Yes' 'No']\n",
      "PaymentMethod ['Electronic check' 'Mailed check' 'Bank transfer (automatic)'\n",
      " 'Credit card (automatic)']\n",
      "MonthlyCharges [ 29.85  56.95  53.85 ...,  63.1   44.2   78.7 ]\n",
      "TotalCharges [   29.85  1889.5    108.15 ...,   346.45   306.6   6844.5 ]\n",
      "Churn ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "# printing out all the unique values the variables take to get some perspective of what we're dealing with\n",
    "for column in df.columns:\n",
    "    print(column, df[column].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a simple exploratory plot - a histogram. We can see how monthly charges are distributed across customers. A large proportion of customers are paying around $20 per month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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    {
     "data": {
      "image/png": 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YsGCBzbITERGRbTlccbnaRZVffPFFp7Ho6Gjs2rWr2/vMnTsXc+fOtUo2IiIisi/HvVCB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\nRqOR0dHRSfs3GAxERUV5XoeGhuJ2uxkeHvapO3v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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13395f93a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.MonthlyCharges.values)\n",
    "plt.xlabel(\"Monthly Charge\")\n",
    "plt.ylabel(\"Number of Customers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do some data cleaning before we draw our survival curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# converting binary variables to \"Yes\" and \"No\" from 1 and 0 to be consistent\n",
    "df.loc[:,\"Churn\"] = df.Churn.apply(lambda x: 1 if x == \"Yes\" else 0)\n",
    "df.loc[:,\"SeniorCitizen\"] = df.SeniorCitizen.apply(lambda x: \"Yes\" if x == 1 else \"No\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# remove the customer who just started\n",
    "df = df[df.tenure != 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival Curves\n",
    "\n",
    "The survival curve is fundamental in survival analysis. It tells us the probability that a customer will still be subscribed to the company. The longer the time, the smaller the probability of *surviving*.  \n",
    "\n",
    "One way of estimating the survival curve is through using the Weibull distribution. The Weibull distribution is a natural choice for modelling time-to-death data. Using this, we can sketch the survival curve of a typical customer in our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# defining a function that plots a Weibull survival curve since there isn't one in lifelines\n",
    "def plot_surv_weibull(weibull_model, ax, max_time = 150):\n",
    "    max_time = np.floor(max_time).astype(int)\n",
    "    x = np.array(range(max_time))\n",
    "    y = np.exp(-(weibull_model.lambda_*x)**weibull_model.rho_)\n",
    "    ax.plot(x,y, label = weibull_model._label)\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmercado\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\optimize.py:1876: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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JxCamYWassS+HEKKEKWOgjf07ZdQdxisZGhpiZ2fHnj17aNCgAfr6/1so4Ozs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\niBBCCCFU8/8BUxp1N4a/ldcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df4f443b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weib = lifelines.WeibullFitter()\n",
    "weib.fit(durations = df.tenure, event_observed = df.Churn)\n",
    "ax = plt.subplot(111)\n",
    "plot_surv_weibull(weib, ax)\n",
    "plt.title(\"Survival Curve of Telco's Customers\")\n",
    "plt.xlabel(\"Timeline (in months)\")\n",
    "plt.ylabel(\"Probability\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's our survival curve. It isn't steep. For instance, there's around a 50% chance that a customer longer than 120 months (almost 10 years). It is also important to check if this checks in with the business' understanding of their customer lifecycle. If their customers are churning much earlier/later than the business perceives them to be, then the business may have to tweak its customer lifecycle management.\n",
    "\n",
    "It also may be a good idea to intervene and incentivize customers who have already stayed for 10 years. Since their probability of staying is dipping below 50%, then without intervention, they are more likely to churn than not to churn.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Survival Curves\n",
    "\n",
    "We can also compare survival curves of customer segments. For example, do females and males churn differently? \n",
    "\n",
    "We can first look at total counts. How many females have churned versus males?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oV69eLi4PAAC0Zk0KIlcEBwcrODjYVbUAAIBbTJPmiAAAALgCQQQAABhDEAEA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\nZWWlMjIyNGfOHA0ePFjBwcF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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df50aaf940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"gender\", hue = \"Churn\", data = df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like males and females churn in similar proportions. We expect the survival curves to also be almost the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmercado\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\optimize.py:1876: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n"
     ]
    },
    {
     "data": {
      "image/png": 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yi2TlTnPmj6iBUqkwdHlCCFEiPPzGXxSGJ+zs7KhQoUKOY4mJiQDMnz+fypUr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\nhBBCiA6R0CCEEEKIDvn/AedJnPcH79GhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df50b0b7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_male = (df.gender == \"Male\")\n",
    "\n",
    "weib_male = lifelines.WeibullFitter()\n",
    "weib_female = lifelines.WeibullFitter()\n",
    "\n",
    "ax = plt.subplot(111)\n",
    "\n",
    "weib_male.fit(durations = df.tenure[is_male], event_observed = df.Churn[is_male], label = \"Male\")\n",
    "plot_surv_weibull(weib_male, ax)\n",
    "weib_female.fit(durations = df.tenure[~is_male], event_observed = df.Churn[~is_male], label = \"Female\")\n",
    "plot_surv_weibull(weib_female, ax)\n",
    "\n",
    "plt.title(\"Male vs Female Survival Curve\")\n",
    "plt.xlabel(\"Tenure in Months\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The survival curves are indeed the same, and furthermore they look identical to the survival group of the whole population. This means we can expect males and females to last around 9.2 years with the telco."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmercado\\Anaconda3\\lib\\site-packages\\scipy\\optimize\\optimize.py:1876: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  tmp2 = (x - v) * (fx - fw)\n",
      "C:\\Users\\jmercado\\Anaconda3\\lib\\site-packages\\lifelines\\fitters\\weibull_fitter.py:117: RuntimeWarning: divide by zero encountered in power\n",
      "  return self.lambda_ * self.rho_ * (self.lambda_ * times) ** (self.rho_ - 1)\n",
      "C:\\Users\\jmercado\\Anaconda3\\lib\\site-packages\\lifelines\\fitters\\weibull_fitter.py:173: RuntimeWarning: divide by zero encountered in log\n",
      "  return np.log(lambda_ * T) * (lambda_ * T) ** rho\n",
      "C:\\Users\\jmercado\\Anaconda3\\lib\\site-packages\\lifelines\\fitters\\weibull_fitter.py:173: RuntimeWarning: invalid value encountered in multiply\n",
      "  return np.log(lambda_ * T) * (lambda_ * T) ** rho\n"
     ]
    },
    {
     "data": {
      "image/png": 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X4L1tvAHIL8znRvb1+77eM73dGN7TDYDI+BymrrlCWIwsuCSEEKJsHBwcmDlz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\nIYQQ7fp/XmaWW5oAMsMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df50bb7978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "has_dependents = (df.Dependents == \"Yes\")\n",
    "\n",
    "weib_has_dependents = lifelines.WeibullFitter()\n",
    "weib_no_dependents = lifelines.WeibullFitter()\n",
    "\n",
    "ax = plt.subplot(111)\n",
    "max_time = 325/12\n",
    "weib_has_dependents.fit(durations = df.tenure[has_dependents]/12, event_observed = df.Churn[has_dependents], label = \"Yes\")\n",
    "plot_surv_weibull(weib_has_dependents, ax, max_time)\n",
    "weib_no_dependents.fit(durations = df.tenure[~has_dependents]/12, event_observed = df.Churn[~has_dependents], label = \"No\")\n",
    "plot_surv_weibull(weib_no_dependents, ax, max_time)\n",
    "\n",
    "plt.title(\"Has Dependents or Not\")\n",
    "plt.xlabel(\"Tenure in Years\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those without dependents are going to last around 7.5 years. On the other hand, those with dependents last significantly longer, around 25 years. Performing a log-rank test will confirm that these survival curves are indeed different from each other. The result of the test (given alpha = 0.95) should be a rejection of the null hypothesis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jmercado\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:224: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return reshape(newshape, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<lifelines.StatisticalResult: \n",
       "Results\n",
       "   t 0: -1\n",
       "   df: 1\n",
       "   alpha: 0.95\n",
       "   test: logrank\n",
       "   null distribution: chi squared\n",
       "\n",
       "   __ p-value ___|__ test statistic __|____ test result ____|__ is significant __\n",
       "         0.00000 |            232.699 |      Reject Null    |        True       \n",
       ">"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lifelines.statistics import logrank_test\n",
    "logrank_test(event_times_A = df.tenure[has_dependents].values,\n",
    "             event_times_B = df.tenure[~has_dependents].values,\n",
    "             event_observed_A = df.Churn[has_dependents].values,\n",
    "             event_observed_B = df.Churn[~has_dependents].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Survival Regression\n",
    "\n",
    "Knowing how long customers stay is all well and good, but what if we want to know the factors that influence churn? What if we want to predict how long a given customer will churn? Survival regression can do that for us.\n",
    "\n",
    "Here we'll specifically focus on **Cox regression**, which uses Cox's Proportional Hazard Model to perform survival regression. The main insight the Cox regression model gives us are its coefficients. The (exponential of the) coefficients correspond to the hazard ratios. What does the hazard ratio mean? *It is a relative measure of the instantaneous rate of failure*. Don't worry if that sounds confusing, it's better to consider an example.  \n",
    "\n",
    "For example, let's say we've fitted a Cox regression model, and one of the variables is `gender`. This variable takes on two values: 1 for male, and 0 for female. What does it mean if hazard ratio of `gender` is 1.10? It means that *at any time*, whether it's 6 months since signing up or 12 months since signing up, males are 10% more likely to churn versus females.\n",
    "\n",
    "The hazard ratio is a relative measure, not an absolute measure. So it should be understood as how females fare in relation to males (or vice versa).  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unfortunately the `lifelines` package in Python can't fit a Cox regression model using our data, so I did it in R instead.\n",
    "\n",
    "---\n",
    "Note: `lifelines` kept on complaining that the matrix it was handling was singular."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "keep_cols = [\"tenure\", \"Churn\", \"PaperlessBilling\", \"SeniorCitizen\", \"Dependents\", \"gender\"]\n",
    "df_clean = df.loc[:,keep_cols]\n",
    "df_mat = patsy.dmatrix(\" + \".join(df_clean.columns.tolist()) ,df_clean, return_type = \"dataframe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Can't fit the model using lifelines.\n"
     ]
    }
   ],
   "source": [
    "coxph = lifelines.CoxPHFitter()\n",
    "try:\n",
    "    coxph.fit(df_mat, duration_col = \"tenure\", event_col = \"Churn\", show_progress = True)\n",
    "    coxph.print_summary()\n",
    "except:\n",
    "    print(\"Can't fit the model using lifelines.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now instead let's fit a Cox regression model with variable stratification."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "keep_cols = [\"tenure\", \"Churn\", \"PaperlessBilling\", \"SeniorCitizen\", \"Dependents\", \"gender\", \"PaymentMethod\", \"Partner\", \"InternetService\", \"PhoneService\", \"Contract\"]\n",
    "strata = [\"PaymentMethod\", \"Partner\", \"InternetService\", \"PhoneService\", \"Contract\"]\n",
    "df_clean = df.loc[:,keep_cols]\n",
    "df_mat = patsy.dmatrix(\" + \".join(df_clean.columns.tolist()) + \" - 1\" ,df_clean, return_type = \"dataframe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Still can't fit the CPX model using lifelines.\n"
     ]
    }
   ],
   "source": [
    "strata_mat = []\n",
    "for col_long in df_mat.columns.tolist():\n",
    "    for col_short in strata:\n",
    "        if col_short in col_long:\n",
    "            strata_mat.append(col_long)\n",
    "            continue\n",
    "coxph = lifelines.CoxPHFitter()\n",
    "try: \n",
    "    coxph.fit(df_mat, duration_col = \"tenure\", event_col = \"Churn\", strata = strata_mat, show_progress = True)\n",
    "    coxph.print_summary()\n",
    "except:\n",
    "    print(\"Still can't fit the CPX model using lifelines.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Proportional hazards assumption\n",
    "\n",
    "Cox regression has a very important assumption, the **proportional hazards assumption**. The variables in this model should first be tested on whether or not they follow this assumption.\n",
    "\n",
    "It means that the **hazard ratio** of all variables should be constant over time. For example, we have the variable \"dependents\" that is 1 if the customer has dependents (e.g. children) and 0 if he/she doesn't have dependents. If the proportional hazards assumption holds, then, **at any time**, those with dependents are 30% more likely to churn than those without dependents. It **doesn't** happen that for the first month up to the third month, those with dependents are 30% more likely, then from the third month to the sixth month, it changes to 15%. The hazard ratio should be more or less the same **across time**.\n",
    "\n",
    "<img src = \"images/prop_hazard_assumption.jpg\">\n",
    "\n",
    "It's possible to test this assumption using a statistical test in R. \n",
    "\n",
    "---\n",
    "Note: It may be easy to confuse the hazard ratio with the odds ratio (seen in logistic regression). Both measure the chances of an outcome (e.g. death) occurring for category (male) versus the outcome occurring for another category (female). The main difference is that the hazard ratio incorporates an element of time, while the odds ratio has no concept of time. More specifically, the hazard ratio is the ratio of instantaneous risk.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We found that only the following variables satisfy the proportional hazards assumption: `PaperlessBilling`, `SeniorCitizen`, `Dependents`, and `gender`. Let's now use these variables to fit a model. \n",
    "\n",
    "---\n",
    "Note: It's unfortunate that we'll have to leave out the other variables, but there are other methods (stratified cox regression, cox regression with time-dependent covariates, pseudo-observations) that can incorporate variables that *don't* follow the proportional hazards assumption.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R's Cox regression results\n",
    "\n",
    "After filtering the variables, we can (finally) fit a model and interpret its results.\n",
    "\n",
    "`\n",
    "Call:\n",
    "coxph(formula = survival ~ gender + SeniorCitizen + Dependents + \n",
    "    PaperlessBilling, data = df)\n",
    "\n",
    "  n= 7043, number of events= 1869 \n",
    "`\n",
    "\n",
    "`\n",
    "                        coef exp(coef) se(coef)       z Pr(>|z|)    \n",
    "genderMale          -0.02079   0.97942  0.04627  -0.449    0.653    \n",
    "SeniorCitizen        0.26499   1.30342  0.05457   4.856  1.2e-06 ***\n",
    "DependentsYes       -0.78207   0.45746  0.06233 -12.547  < 2e-16 ***\n",
    "PaperlessBillingYes  0.61206   1.84423  0.05399  11.336  < 2e-16 ***\n",
    "`\n",
    "\n",
    "`\n",
    "Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1\n",
    "`\n",
    "\n",
    "`\n",
    "                    exp(coef) exp(-coef) lower .95 upper .95\n",
    "genderMale             0.9794     1.0210    0.8945    1.0724\n",
    "SeniorCitizen          1.3034     0.7672    1.1712    1.4505\n",
    "DependentsYes          0.4575     2.1860    0.4048    0.5169\n",
    "PaperlessBillingYes    1.8442     0.5422    1.6590    2.0501\n",
    "`\n",
    "\n",
    "`\n",
    "Concordance= 0.628  (se = 0.007 )\n",
    "Rsquare= 0.061   (max possible= 0.988 )\n",
    "Likelihood ratio test= 441.4  on 4 df,   p=0\n",
    "Wald test            = 386.5  on 4 df,   p=0\n",
    "Score (logrank) test = 411.2  on 4 df,   p=0\n",
    "`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Here's what the cox regression model (finally) tells us:\n",
    "- Gender isn't a good indicator of churn. This confirms what we saw earlier when we compared the survival curves between female and male customers. They're equally likely to churn.\n",
    "- Senior citizenship, having dependents, and having paperless billing **are indicative** of churn. We can also quantify their effects.\n",
    "    1. Senior citizens are **30% more likely** to churn than non-senior citizens\n",
    "    2. Customers without dependents are **twice as likely** to churn more than those with dependents. This also validates the survival curves earlier. The difference is that now, we have a number to compare both groups.\n",
    "    3. Customers with paperless billing are **1.8 times more likely** to churn than those without paperless billing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Taking these insights to action\n",
    "\n",
    "We saw three significant factors to churn. We can leave out the Senior citizen factor and that leaves us with two. Senior citizens might be more at risk of churning not because they're willingly opting out of the subscription service, but because they're passing away. Furthermore, the hazard ratio of the senior citizen factor is lower (30%) compared to the other two anyway.\n",
    "\n",
    "The paperless billing factor is surprising since you'd expect customers to prefer the convenience and speed of receiving their bills online. There are various possibilities as to why this is happening. However, the business should investigate if their paperless billing processes are properly implemented. Also, majority of the customers are under paperless billing so this is an important issue.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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VK1YYY48ePZrKykrS09Opq6tj1KhRpKWlmTJPEREROZ9bY2Njo9lNuKra2lr27t1LWFhY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\nTPN/AXG01hxGkdGSAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df50986240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"PaperlessBilling\", data = df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see from the graph below that there are more than twice as many customers **without dependents** than with dependents. At the same time, those without dependents are **twice as likely** to churn. This means that there should be more efforts to retain this segment of customers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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eXq7AwEBJ0pkzZ1RRUWEd31i9e/ducrxcTGVlpfT5Ny16TqA1CgwMVEhIiOkxAFxmVVVV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\nAIAxhAgAADDm/wO8npgVcCJNoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1df50a219e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x = \"Dependents\", data = df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are just some of the ways that survival analysis can be used to address business problems. [Barry Leventhal](http://www.barryanalytics.com/Downloads/Presentations/Survival%20Analysis.pdf) has recommended other use cases:\n",
    "> \n",
    "- Business Planning\n",
    "    - Forecast monthly number of lapses and use to monitor current lapse rates.\n",
    "- Lifetime Value (LTV) prediction\n",
    "    - Derive LTV predictions by combining expected survival times with monthly revenues.\n",
    "- Active customers\n",
    "    - Predict each customer's time to next purchase, and use to identify \"active\" vs. \"inactive\" customers.\n",
    "- Campaign evaluation\n",
    "    - Monitor effects of campaigns on survival rates.\n",
    "    \n",
    "With that, I hope this branch of statistics can be useful to a problem that you're solving."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resource materials\n",
    "\n",
    "The following resources were extremely helpful in making this post. Check them out if you want to learn more about survival analysis.\n",
    "\n",
    "- [Customer Churn Dataset](https://www.ibm.com/communities/analytics/watson-analytics-blog/predictive-insights-in-the-telco-customer-churn-data-set/)\n",
    "- [Dayne Batten's blog posts on survival analysis in the business setting](http://daynebatten.com/category/survival-analysis/)\n",
    "- Here's a good resource on [stratified cox regression](https://stat.ethz.ch/education/semesters/ss2011/seminar/contents/presentation_5.pdf), and this [course on survival analysis](https://stat.ethz.ch/education/semesters/ss2011/seminar/) in general has great content.\n",
    "- [Barry Leventhal's presentation with business insights](http://www.barryanalytics.com/Downloads/Presentations/Survival%20Analysis.pdf)\n",
    "- [The Survival Package in R](https://cran.r-project.org/web/packages/survival/survival.pdf)\n",
    "- [The Lifelines Package in Python](https://lifelines.readthedocs.io/en/latest/)"
   ]
  }
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