Churn Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Churn prediction model\n",
    "## Developing and Deploying a model in Watson Studio\n",
    "\n",
    "#### Summary\n",
    "\n",
    "This project teaches the basic about developing and deploying a machine learning model in from IBM Watson Studio to the IBM cloud. It consist in using data client data from a telecommunication company to create a model that predicts if a customer is going to churn or not, we will go through the basic steps of model building but have a focus on training and deploying the model with diferent libraries to make sure all the common cases are covered.\n",
    "\n",
    "As for the datasets characteristics, it containes 7043 rows (customers) and 21 columns (features) which includes gender, tenure, if the customer is a senior citizen and many others so there is information for our model to learn. The data, as seen during the cleaning, is mostly out of null values and ready to use. Afterwards we check the correlation between features but since not all of them are of thesame type then it was necessary to use two types of correlation measures. It also is important to state that there is a heavy overrepresentation of \"No churn\" cases which would need to be oversampled to correctly train the model. Finally it was decided on a Logistic Regresion model since it is was on the simplest to apply and most common to use.\n",
    "\n",
    "#### Packages\n",
    "\n",
    "- numpy\n",
    "- pandas\n",
    "- matplotlib\n",
    "- seaborn\n",
    "- watson_machine_learning_client\n",
    "- botocore\n",
    "- ibm_boto3\n",
    "- scikit-learn -> 0.20.0\n",
    "- imbalanced-learn -> 0.4.3\n",
    "\n",
    "#### Remark\n",
    "\n",
    "The Watson ML library it only able to deploy scikit-learn models from version <= 0.20.0 so keep this in mind\n",
    "\n",
    "#### Links and Data Source\n",
    "\n",
    "Data source: https://www.kaggle.com/blastchar/telco-customer-churn\n",
    "\n",
    "Correlation between variables: https://medium.com/@outside2SDs/an-overview-of-correlation-measures-between-categorical-and-continuous-variables-4c7f85610365\n",
    "\n",
    "#### Personal conclusion and acknowledgments\n",
    "\n",
    "As a small conclusion I would like to say that I had a lot of fun making this tutorial, Data Analysis and model building are very scary areas to get into since there is so much to learn and heavy math involved but luckily modern tools and libraries can save us some work. Although I had some training in the area before hand but it is always good to refresh knwoledge and learn new stuff(stackoverflow is the way). As a final work I would like to say thank you to my data preparation professor Robert Nisbet at UCI, his class helped me a lot and gave me a clear view for this project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import scipy.stats as ss\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from imblearn.over_sampling import SMOTENC\n",
    "from watson_machine_learning_client import WatsonMachineLearningAPIClient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading data from source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#From local computer\n",
    "data = pd.read_csv(\"churn-dataset.csv\")\n",
    "\"\"\"\n",
    "#From IBM COS\n",
    "import types\n",
    "import pandas as pd\n",
    "from botocore.client import Config\n",
    "import ibm_boto3\n",
    "\n",
    "def __iter__(self): return 0\n",
    "\n",
    "# @hidden_cell\n",
    "# The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials.\n",
    "# You might want to remove those credentials before you share the notebook.\n",
    "client_1d7fd3936d244337a5cb0e975018dfb0 = ibm_boto3.client(service_name='s3',\n",
    "    ibm_api_key_id='**************************',\n",
    "    ibm_auth_endpoint=\"**********************\",\n",
    "    config=Config(signature_version='oauth'),\n",
    "    endpoint_url='https://s3-api.us-geo.objectstorage.service.networklayer.com')\n",
    "\n",
    "body = client_1d7fd3936d244337a5cb0e975018dfb0.get_object(Bucket='telcouserchurn-donotdelete-pr-ekk0uryv60v8ov',Key='churn-dataset.csv')['Body']\n",
    "# add missing __iter__ method, so pandas accepts body as file-like object\n",
    "if not hasattr(body, \"__iter__\"): body.__iter__ = types.MethodType( __iter__, body )\n",
    "\n",
    "data = pd.read_csv(body)\n",
    "\"\"\"\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping uneeded index column\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7043, 20)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['customerID'], axis=1)\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for nulls values\n",
    "It is always important to look out for null values that we could fix or erase to increase available information from where to train the model. We didn't find any at this time so we can omit this step for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Churn               0\n",
       "TotalCharges        0\n",
       "SeniorCitizen       0\n",
       "Partner             0\n",
       "Dependents          0\n",
       "tenure              0\n",
       "PhoneService        0\n",
       "MultipleLines       0\n",
       "InternetService     0\n",
       "OnlineSecurity      0\n",
       "OnlineBackup        0\n",
       "DeviceProtection    0\n",
       "TechSupport         0\n",
       "StreamingTV         0\n",
       "StreamingMovies     0\n",
       "Contract            0\n",
       "PaperlessBilling    0\n",
       "PaymentMethod       0\n",
       "MonthlyCharges      0\n",
       "gender              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = data\n",
    "df.isnull().sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data type correction and cleaning\n",
    "\n",
    "1. We should always check the datatype to make sure all data was imported correctly as pandas is making assumptions about feature types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. We can appreciate that \"TotalCharges\" is assumed as an object but its values look like numbers, it was wrongly assumed and has to be converted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender -> ['Female' 'Male']\n",
      "SeniorCitizen -> [0 1]\n",
      "Partner -> ['Yes' 'No']\n",
      "Dependents -> ['No' 'Yes']\n",
      "tenure -> [ 1 34  2 45  8]\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 42.3  70.7 ]\n",
      "TotalCharges -> ['29.85' '1889.5' '108.15' '1840.75' '151.65']\n",
      "Churn -> ['No' 'Yes']\n"
     ]
    }
   ],
   "source": [
    "for col in df.columns:\n",
    "    print(col, \"->\",df[col].unique()[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Use \"pd.to_numeric\" to covert a column from its original type to a number and the parameter \"error\" with the coerce values sets all unconvertable values as null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_refined = df\n",
    "for col in df.columns:\n",
    "    if not (col in [\"MonthlyCharges\", \"TotalCharges\", \"tenure\"]):\n",
    "        df_refined[col] = df[col].astype('category')\n",
    "    else:\n",
    "        df_refined[col] = pd.to_numeric(df[col], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. From the last line we have created 11 null values for the \"TotalCharges\" column\n",
    "\n",
    "##### Remark: It is always a good idea to fill the missing values to obtain more information but since theey are a really small portion of the whole dataset we are just going to omit them(A tutorial regarding filling null values is on the way)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_refined[\"TotalCharges\"].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_refined = df_refined.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis\n",
    "\n",
    "1. Now that we made sure that our data has no missing values, it is time to take a look at the value distribution. As  expected in real data, target variable is unbalanced and this damage the training process of our model.\n",
    "\n",
    "##### Remark: You should always take a look at all features and no only the target variables. Use your business knowledge to help accelerate the features selection process.(To save time and space, and keep in scope of the tutorial, only the target variable was visualized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.countplot(x=\"Churn\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Next is to measure the correlation between features to make sure there are no overlapping between them. As our model has categorical and continous features, we cannot use the regular pearson formula for all. Below there is a matrix of posible metrics to use depending on the situation(The link to the source is at the top of the tutorial)\n",
    "\n",
    "\n",
    "<img src=\"Images/IMG_BD_Corr_CatvsCon.png\" width=\"60%\">."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cramers_v(x, y):\n",
    "    confusion_matrix = pd.crosstab(x,y)\n",
    "    chi2 = ss.chi2_contingency(confusion_matrix)[0]\n",
    "    n = confusion_matrix.sum().sum()\n",
    "    phi2 = chi2/n\n",
    "    r,k = confusion_matrix.shape\n",
    "    phi2corr = max(0, phi2-((k-1)*(r-1))/(n-1))\n",
    "    rcorr = r-((r-1)**2)/(n-1)\n",
    "    kcorr = k-((k-1)**2)/(n-1)\n",
    "    return np.sqrt(phi2corr/min((kcorr-1),(rcorr-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PhoneService -> MultipleLines = 0.9999288737151917\n",
      "MultipleLines -> PhoneService = 0.9999288737151917\n",
      "StreamingTV -> StreamingMovies = 0.7711317093428717\n",
      "StreamingMovies -> StreamingTV = 0.7711317093428717\n"
     ]
    }
   ],
   "source": [
    "df_categorical = df_refined.drop([\"MonthlyCharges\", \"TotalCharges\", \"tenure\"], axis=1)\n",
    "corr_matrix = pd.DataFrame ({}, columns = df_categorical.columns)\n",
    "for col1 in df_categorical.columns:\n",
    "    v = []\n",
    "    i = []\n",
    "    for col2 in df_categorical.columns:\n",
    "        c = cramers_v(df_categorical[col1], df_categorical[col2])\n",
    "        if(c > 0.75 and col1 != col2):\n",
    "            print(col1, \"->\", col2, \"=\", c)\n",
    "        v.append(c)\n",
    "        i.append(col2)\n",
    "    corr_matrix[col1] = pd.Series(v, index =i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. As we can see there are a 2 features that share almost 100% correlation so there is no value in keeping them both. \"PhoneService\" has more attributes so we are keeping that one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8ba3fd1128>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(corr_matrix, annot = True, fmt='.1g', linewidths=.5, ax=ax, cmap= 'coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>MonthlyCharges</th>\n",
       "      <th>TotalCharges</th>\n",
       "      <th>tenure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MonthlyCharges</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.651065</td>\n",
       "      <td>0.246862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalCharges</th>\n",
       "      <td>0.651065</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.825880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tenure</th>\n",
       "      <td>0.246862</td>\n",
       "      <td>0.825880</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                MonthlyCharges  TotalCharges    tenure\n",
       "MonthlyCharges        1.000000      0.651065  0.246862\n",
       "TotalCharges          0.651065      1.000000  0.825880\n",
       "tenure                0.246862      0.825880  1.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_refined[[\"MonthlyCharges\", \"TotalCharges\", \"tenure\"]].corr(method=\"pearson\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting data\n",
    "\n",
    "The data is being divided into 70%-30% for the training and testing dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_data = df_refined.drop([\"PhoneService\", \"Churn\"], axis=1)\n",
    "Y_data = df_refined[\"Churn\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender              category\n",
      "SeniorCitizen       category\n",
      "Partner             category\n",
      "Dependents          category\n",
      "tenure                 int64\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         float64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X_data, Y_data, test_size=0.3, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Oversampling of data\n",
    "\n",
    "Before we saw that our data had an underrepresentation of \"Churn=yes\" cases, it important to equalize this situation to ensure that our model learns correctly. The SMOTE technique was used over the training dataset to solve this situation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = list(X_data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender              object\n",
      "SeniorCitizen       object\n",
      "Partner             object\n",
      "Dependents          object\n",
      "tenure              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      object\n",
      "TotalCharges        object\n",
      "dtype: object\n",
      "length of oversampled data is  7216\n",
      "Number of Churn 3608\n",
      "Number of No Churn 3608\n",
      "Proportion of Churn data in oversampled data is  0.5\n",
      "Proportion of No Churn data in oversampled data is  0.5\n"
     ]
    }
   ],
   "source": [
    "os = SMOTENC(random_state=0, categorical_features=[0,1,2,3,5,6,7,8,9,10,11,12,13,14,15])\n",
    "os_data_X,os_data_y=os.fit_resample(X_train, y_train)\n",
    "os_data_X = pd.DataFrame(data=os_data_X,columns=cols)\n",
    "os_data_y = pd.DataFrame(data=os_data_y,columns=['Churn'])\n",
    "\n",
    "print(\"length of oversampled data is \",len(os_data_X))\n",
    "print(\"Number of Churn\",len(os_data_y[os_data_y['Churn']==\"Yes\"]))\n",
    "print(\"Number of No Churn\",len(os_data_y[os_data_y['Churn']==\"No\"]))\n",
    "print(\"Proportion of Churn data in oversampled data is \",len(os_data_y[os_data_y['Churn']==\"Yes\"])/len(os_data_X))\n",
    "print(\"Proportion of No Churn data in oversampled data is \",len(os_data_y[os_data_y['Churn']==\"No\"])/len(os_data_X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train_num = os_data_X[[\"MonthlyCharges\", \"TotalCharges\", \"tenure\"]]\n",
    "for col in X_train_num.columns:\n",
    "    X_train_num[col] = pd.to_numeric(X_train_num[col], errors=\"coerce\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-Hot Encoding categorical variables\n",
    "\n",
    "The next step is to encode our categorical features since ML models require numbers but at the same time our features are not continous."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "temp_cat = pd.get_dummies(os_data_X.drop([\"MonthlyCharges\", \"TotalCharges\", \"tenure\"], axis=1), drop_first=True)\n",
    "X_train = pd.concat([temp_cat, X_train_num], axis=1, join='inner')\n",
    "Y_train = pd.get_dummies(os_data_y, drop_first=True)\n",
    "\n",
    "temp_cat = pd.get_dummies(X_test.drop([\"MonthlyCharges\", \"TotalCharges\", \"tenure\"], axis=1), drop_first=True)\n",
    "X_test = pd.concat([temp_cat, X_test[[\"MonthlyCharges\", \"TotalCharges\", \"tenure\"]]], axis=1, join='inner')\n",
    "Y_test = pd.get_dummies(y_test, drop_first=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training ML model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=300, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "logreg = LogisticRegression(max_iter=300)\n",
    "logreg.fit(X_train, Y_train.values.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.77\n"
     ]
    }
   ],
   "source": [
    "y_pred = logreg.predict(X_test)\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logreg.score(X_test, Y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1209  346]\n",
      " [ 144  411]]\n"
     ]
    }
   ],
   "source": [
    "conf_matrix = confusion_matrix(Y_test, y_pred)\n",
    "print(conf_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.83      0.75      0.79      1064\n",
      "           1       0.78      0.85      0.81      1101\n",
      "\n",
      "   micro avg       0.80      0.80      0.80      2165\n",
      "   macro avg       0.80      0.80      0.80      2165\n",
      "weighted avg       0.80      0.80      0.80      2165\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploying to as API to Watson ML\n",
    "\n",
    "Ok, finally we have arrived at the deploying of the model. The credentilas of the instance are declared and the model stored in the Watson ML storage, then we use the Uid to deploy it(It does not appear in the deployment section but it is there)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "wml_credentials={\n",
    "    \"apikey\": \"*****************************\",\n",
    "    \"instance_id\": \"************************\",\n",
    "    \"url\": \"********************************\"\n",
    "}\n",
    "\n",
    "client = WatsonMachineLearningAPIClient(wml_credentials)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Store model in WML repository"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_props={\n",
    "    client.repository.ModelMetaNames.NAME: \"Logistic Regression Churn model\", \n",
    "    client.repository.ModelMetaNames.AUTHOR_EMAIL: \"diegoramirez@gmail.com\",\n",
    "    client.repository.ModelMetaNames.FRAMEWORK_VERSION: \"0.20\",\n",
    "    client.repository.ModelMetaNames.FRAMEWORK_NAME: \"scikit-learn\"\n",
    "}\n",
    "\n",
    "model_artifact=client.repository.store_model(logreg, meta_props=model_props)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "------------------------------------  -------------------------------  ------------------------  -----------------  -----\n",
      "GUID                                  NAME                             CREATED                   FRAMEWORK          TYPE\n",
      "f1cf615d-d9a9-436c-9771-88df97c7e6ec  Logistic Regression Churn model  2020-05-20T02:43:02.470Z  scikit-learn-0.20  model\n",
      "------------------------------------  -------------------------------  ------------------------  -----------------  -----\n"
     ]
    }
   ],
   "source": [
    "client.repository.list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy model to Watson Machine Learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "#######################################################################################\n",
      "\n",
      "Synchronous deployment creation for uid: 'f1cf615d-d9a9-436c-9771-88df97c7e6ec' started\n",
      "\n",
      "#######################################################################################\n",
      "\n",
      "\n",
      "INITIALIZING\n",
      "DEPLOY_SUCCESS\n",
      "\n",
      "\n",
      "------------------------------------------------------------------------------------------------\n",
      "Successfully finished deployment creation, deployment_uid='f9e80285-841e-4783-bea0-0c76bf8a8ec4'\n",
      "------------------------------------------------------------------------------------------------\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Get model UID\n",
    "published_model_uid = client.repository.get_model_uid(model_artifact)\n",
    "#Deploy the model\n",
    "created_deployment = client.deployments.create(published_model_uid, name=\"ChurnModelDeployment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring_endpoint = client.deployments.get_scoring_url(created_deployment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'fields': ['prediction', 'probability'], 'values': [[0, [0.9849121857890184, 0.01508781421098155]], [0, [0.7668201230777614, 0.23317987692223857]], [0, [0.9977147998805967, 0.0022852001194032597]], [0, [0.975127668806959, 0.024872331193040997]], [0, [0.7327641504178833, 0.26723584958211666]], [0, [0.9916415671999173, 0.008358432800082749]], [1, [0.37651074677061636, 0.6234892532293836]], [0, [0.9986890733149208, 0.0013109266850791436]], [0, [0.9828675236249786, 0.017132476375021317]]]}\n"
     ]
    }
   ],
   "source": [
    "scoring_payload = {\"fields\": list(X_test.columns),\n",
    "    \"values\":X_test.iloc[11:20].values.tolist()}\n",
    "predictions = client.deployments.score(scoring_endpoint, scoring_payload)\n",
    "print(predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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