19. Subscription Retention Rate.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4156dcc",
   "metadata": {},
   "source": [
    "# Goal of Analysis\n",
    "\n",
    "## 1. Decide a model that predicts monthly retention rate for the different subscription price points\n",
    "\n",
    "## 2. Based on your model, for each price point, what percentage of users is still subscribed after at least 12 months?\n",
    "\n",
    "## 3. How do user country and source affect subscription retention rate? How would you use these findings to improve the company revenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "44219b96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f265ba73",
   "metadata": {},
   "source": [
    "### Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "98364b1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>subscription_signup_date</th>\n",
       "      <th>subscription_monthly_cost</th>\n",
       "      <th>country</th>\n",
       "      <th>source</th>\n",
       "      <th>billing_cycles</th>\n",
       "      <th>is_active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1459</td>\n",
       "      <td>January, 2015</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>ads</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12474</td>\n",
       "      <td>January, 2015</td>\n",
       "      <td>49</td>\n",
       "      <td>France</td>\n",
       "      <td>ads</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12294</td>\n",
       "      <td>January, 2015</td>\n",
       "      <td>49</td>\n",
       "      <td>Germany</td>\n",
       "      <td>ads</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3878</td>\n",
       "      <td>January, 2015</td>\n",
       "      <td>49</td>\n",
       "      <td>China</td>\n",
       "      <td>ads</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9567</td>\n",
       "      <td>January, 2015</td>\n",
       "      <td>49</td>\n",
       "      <td>UK</td>\n",
       "      <td>ads</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id subscription_signup_date  subscription_monthly_cost  country  \\\n",
       "0     1459            January, 2015                         29    Spain   \n",
       "1    12474            January, 2015                         49   France   \n",
       "2    12294            January, 2015                         49  Germany   \n",
       "3     3878            January, 2015                         49    China   \n",
       "4     9567            January, 2015                         49       UK   \n",
       "\n",
       "  source  billing_cycles  is_active  \n",
       "0    ads               4          0  \n",
       "1    ads               5          0  \n",
       "2    ads               2          0  \n",
       "3    ads               1          0  \n",
       "4    ads               5          0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('subscription.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "de0bedb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Series.unique of 0         January, 2015\n",
       "1         January, 2015\n",
       "2         January, 2015\n",
       "3         January, 2015\n",
       "4         January, 2015\n",
       "              ...      \n",
       "499995    January, 2015\n",
       "499996    January, 2015\n",
       "499997    January, 2015\n",
       "499998    January, 2015\n",
       "499999    January, 2015\n",
       "Name: subscription_signup_date, Length: 500000, dtype: object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.subscription_signup_date.unique"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33278b67",
   "metadata": {},
   "source": [
    "Since the subscription_signup_date has only one value, which is January, 2015, I decide to delete this column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9c254445",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>user_id</th>\n",
       "      <th>subscription_monthly_cost</th>\n",
       "      <th>country</th>\n",
       "      <th>source</th>\n",
       "      <th>billing_cycles</th>\n",
       "      <th>is_active</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1459</td>\n",
       "      <td>29</td>\n",
       "      <td>Spain</td>\n",
       "      <td>ads</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12474</td>\n",
       "      <td>49</td>\n",
       "      <td>France</td>\n",
       "      <td>ads</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12294</td>\n",
       "      <td>49</td>\n",
       "      <td>Germany</td>\n",
       "      <td>ads</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3878</td>\n",
       "      <td>49</td>\n",
       "      <td>China</td>\n",
       "      <td>ads</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9567</td>\n",
       "      <td>49</td>\n",
       "      <td>UK</td>\n",
       "      <td>ads</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   user_id  subscription_monthly_cost  country source  billing_cycles  \\\n",
       "0     1459                         29    Spain    ads               4   \n",
       "1    12474                         49   France    ads               5   \n",
       "2    12294                         49  Germany    ads               2   \n",
       "3     3878                         49    China    ads               1   \n",
       "4     9567                         49       UK    ads               5   \n",
       "\n",
       "   is_active  \n",
       "0          0  \n",
       "1          0  \n",
       "2          0  \n",
       "3          0  \n",
       "4          0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del data['subscription_signup_date']\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12eb353c",
   "metadata": {},
   "source": [
    "### Question 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b77e3af0",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subscription_monthly_cost</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>57441</td>\n",
       "      <td>21818</td>\n",
       "      <td>12037</td>\n",
       "      <td>7787</td>\n",
       "      <td>5505</td>\n",
       "      <td>4136</td>\n",
       "      <td>3111</td>\n",
       "      <td>34527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>185145</td>\n",
       "      <td>53296</td>\n",
       "      <td>21690</td>\n",
       "      <td>11288</td>\n",
       "      <td>6727</td>\n",
       "      <td>4405</td>\n",
       "      <td>3173</td>\n",
       "      <td>14673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>44788</td>\n",
       "      <td>4724</td>\n",
       "      <td>1471</td>\n",
       "      <td>678</td>\n",
       "      <td>399</td>\n",
       "      <td>226</td>\n",
       "      <td>156</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                1      2      3      4     5     6     7  \\\n",
       "subscription_monthly_cost                                                  \n",
       "29                          57441  21818  12037   7787  5505  4136  3111   \n",
       "49                         185145  53296  21690  11288  6727  4405  3173   \n",
       "99                          44788   4724   1471    678   399   226   156   \n",
       "\n",
       "                               8  \n",
       "subscription_monthly_cost         \n",
       "29                         34527  \n",
       "49                         14673  \n",
       "99                           799  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# I first groupby the monthly cost and then count the number for different billing cycles\n",
    "\n",
    "count_by_cost = data.groupby('subscription_monthly_cost').apply(lambda df: df.billing_cycles.value_counts()).unstack()\n",
    "\n",
    "count_by_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd78c8a6",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>subscription_monthly_cost</th>\n",
       "      <th>29</th>\n",
       "      <th>49</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>146362</td>\n",
       "      <td>300397</td>\n",
       "      <td>53241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>88921</td>\n",
       "      <td>115252</td>\n",
       "      <td>8453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67103</td>\n",
       "      <td>61956</td>\n",
       "      <td>3729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>55066</td>\n",
       "      <td>40266</td>\n",
       "      <td>2258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47279</td>\n",
       "      <td>28978</td>\n",
       "      <td>1580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>41774</td>\n",
       "      <td>22251</td>\n",
       "      <td>1181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37638</td>\n",
       "      <td>17846</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>34527</td>\n",
       "      <td>14673</td>\n",
       "      <td>799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subscription_monthly_cost      29      49     99\n",
       "1                          146362  300397  53241\n",
       "2                           88921  115252   8453\n",
       "3                           67103   61956   3729\n",
       "4                           55066   40266   2258\n",
       "5                           47279   28978   1580\n",
       "6                           41774   22251   1181\n",
       "7                           37638   17846    955\n",
       "8                           34527   14673    799"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transpose the above result to get a better look\n",
    "\n",
    "total_by_cost = count_by_cost.apply(lambda s: s.iloc[::-1].cumsum().iloc[::-1],axis=1).transpose()\n",
    "\n",
    "total_by_cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bce160e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 4))\n",
    "total_by_cost.plot(kind='line', ax=ax)\n",
    "ax.set_xlabel('Billing Cycle', fontsize=12)\n",
    "ax.set_ylabel('Retention Rate', fontsize=12)\n",
    "ax.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a68e084b",
   "metadata": {},
   "source": [
    "We can know from the above graph:\n",
    "\n",
    "1. for each 'subscription_monthly_cost', there are only 8 samples.\n",
    "\n",
    "2. Our dataset is small. Since complex model will overfit on samll dataset, so I decide to use a simple Linear Regression model.\n",
    "\n",
    "3. The remaining #subscribers by the end of each billing cycles has a nonlinear relationship with #billing_cycles as shown from the graph, so I need to include some nonlinear transformation of 'billing_cycles'\n",
    "\n",
    "4. The target #subscribers should be non-negative, so I cannot fit on original target, so I will try on log(#subscribers). After obtaining the fitted value, we transform back to '#subscribers' by exp(), which can guarantee a positive result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3fa27f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "def make_time_features(t):\n",
    "    \"\"\"\n",
    "    three features:\n",
    "    1. t: #cycles\n",
    "    2. t-square: square of #cycles\n",
    "    3. logt: log(#cycles)\n",
    "    \"\"\"\n",
    "    return pd.DataFrame({'t': t,'logt': np.log(t),'tsquare':t*t },index = t)\n",
    "\n",
    "def fit_linear_regression(s):\n",
    "    \"\"\"\n",
    "    target:\n",
    "    log(s): s is #subscribers left by the end of each billing cycle\n",
    "    do this transformation, to guarantee that, after tranforming back, the fitted result is always positive\n",
    "    \"\"\"\n",
    "    X = make_time_features(s.index)\n",
    "    return LinearRegression().fit(X,np.log(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0e97f883",
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_by_cost = total_by_cost.apply(fit_linear_regression,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "55debdc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>t</th>\n",
       "      <th>logt</th>\n",
       "      <th>tsquare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.693147</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1.945910</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>2.484907</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     t      logt  tsquare\n",
       "1    1  0.000000        1\n",
       "2    2  0.693147        4\n",
       "3    3  1.098612        9\n",
       "4    4  1.386294       16\n",
       "5    5  1.609438       25\n",
       "6    6  1.791759       36\n",
       "7    7  1.945910       49\n",
       "8    8  2.079442       64\n",
       "9    9  2.197225       81\n",
       "10  10  2.302585      100\n",
       "11  11  2.397895      121\n",
       "12  12  2.484907      144"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allt = np.arange(1,13)\n",
    "Xwhole = make_time_features(allt)\n",
    "Xwhole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c4082892",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>subscription_monthly_cost</th>\n",
       "      <th>29</th>\n",
       "      <th>49</th>\n",
       "      <th>99</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>146289.043583</td>\n",
       "      <td>301551.603773</td>\n",
       "      <td>52816.489536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89078.661927</td>\n",
       "      <td>113569.323151</td>\n",
       "      <td>8700.570325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>67048.510837</td>\n",
       "      <td>62560.448086</td>\n",
       "      <td>3678.850952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54997.715283</td>\n",
       "      <td>40592.309531</td>\n",
       "      <td>2210.169591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>47259.987332</td>\n",
       "      <td>28988.345871</td>\n",
       "      <td>1567.465186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>41802.132010</td>\n",
       "      <td>22106.115832</td>\n",
       "      <td>1208.305347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37703.792361</td>\n",
       "      <td>17713.678002</td>\n",
       "      <td>968.806877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>34484.309114</td>\n",
       "      <td>14771.777457</td>\n",
       "      <td>786.979952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31866.862280</td>\n",
       "      <td>12740.560957</td>\n",
       "      <td>636.784748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>29680.233950</td>\n",
       "      <td>11317.105659</td>\n",
       "      <td>507.336931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>27812.603941</td>\n",
       "      <td>10321.915056</td>\n",
       "      <td>394.719969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26187.753374</td>\n",
       "      <td>9644.813994</td>\n",
       "      <td>298.073051</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subscription_monthly_cost             29             49            99\n",
       "1                          146289.043583  301551.603773  52816.489536\n",
       "2                           89078.661927  113569.323151   8700.570325\n",
       "3                           67048.510837   62560.448086   3678.850952\n",
       "4                           54997.715283   40592.309531   2210.169591\n",
       "5                           47259.987332   28988.345871   1567.465186\n",
       "6                           41802.132010   22106.115832   1208.305347\n",
       "7                           37703.792361   17713.678002    968.806877\n",
       "8                           34484.309114   14771.777457    786.979952\n",
       "9                           31866.862280   12740.560957    636.784748\n",
       "10                          29680.233950   11317.105659    507.336931\n",
       "11                          27812.603941   10321.915056    394.719969\n",
       "12                          26187.753374    9644.813994    298.073051"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# call each cost's model to fit on above features\n",
    "\n",
    "predicts = lr_by_cost.apply(lambda lr: pd.Series(lr.predict(Xwhole),index=allt)).transpose()\n",
    "predicts = predicts.applymap(np.exp)\n",
    "\n",
    "predicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c506d4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1152 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(3,1,sharex=True)\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "monthly_costs = [29,49,99]\n",
    "\n",
    "for index,cost in enumerate(monthly_costs):\n",
    "    ax = axes[index]\n",
    "    total_by_cost.loc[:,cost].plot(ax = ax,label='true values')\n",
    "    predicts.loc[:,cost].plot(ax=ax,label='predictions')\n",
    "    ax.legend(loc='best')\n",
    "    ax.set_title('monthly cost = {}'.format(cost))\n",
    "plt.rc('figure',figsize=(10,16))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c2e43cb",
   "metadata": {},
   "source": [
    "### Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "fed44d75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subscription_monthly_cost\n",
       "29    0.179014\n",
       "49    0.031984\n",
       "99    0.005644\n",
       "dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In order to calculate the percentage, we just need to divide the last predicted value to the total values\n",
    "\n",
    "predicts.loc[12,:]/predicts.loc[1,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c0fb679",
   "metadata": {},
   "source": [
    "### Question 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "398107b0",
   "metadata": {},
   "source": [
    "In order to know the the influence of countr and source to the subscription rate, we first define a new function to calculate the retention rate and then apply to country and source to see the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d746c0e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def retention_rate(df):\n",
    "    total_num = len(df)\n",
    "    rate = []\n",
    "    for i in [1, 2, 3, 4, 5, 6, 7, 8]:\n",
    "        rate.append(len(df[df['billing_cycles'] >= i]) / total_num)\n",
    "        \n",
    "    return pd.Series(rate, index=[1, 2, 3, 4, 5, 6, 7, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6824ad03",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.514831</td>\n",
       "      <td>0.402777</td>\n",
       "      <td>0.360426</td>\n",
       "      <td>0.336274</td>\n",
       "      <td>0.319856</td>\n",
       "      <td>0.307084</td>\n",
       "      <td>0.295453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.334946</td>\n",
       "      <td>0.177732</td>\n",
       "      <td>0.119004</td>\n",
       "      <td>0.088479</td>\n",
       "      <td>0.069935</td>\n",
       "      <td>0.058341</td>\n",
       "      <td>0.049688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.451284</td>\n",
       "      <td>0.278411</td>\n",
       "      <td>0.195910</td>\n",
       "      <td>0.146474</td>\n",
       "      <td>0.115151</td>\n",
       "      <td>0.093868</td>\n",
       "      <td>0.078465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>India</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.446728</td>\n",
       "      <td>0.326962</td>\n",
       "      <td>0.284527</td>\n",
       "      <td>0.261603</td>\n",
       "      <td>0.246736</td>\n",
       "      <td>0.234847</td>\n",
       "      <td>0.225769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italy</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.341684</td>\n",
       "      <td>0.179502</td>\n",
       "      <td>0.119914</td>\n",
       "      <td>0.090015</td>\n",
       "      <td>0.071135</td>\n",
       "      <td>0.057523</td>\n",
       "      <td>0.048695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spain</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.339098</td>\n",
       "      <td>0.178985</td>\n",
       "      <td>0.119879</td>\n",
       "      <td>0.088648</td>\n",
       "      <td>0.070065</td>\n",
       "      <td>0.057617</td>\n",
       "      <td>0.048503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.458938</td>\n",
       "      <td>0.285133</td>\n",
       "      <td>0.198067</td>\n",
       "      <td>0.149421</td>\n",
       "      <td>0.117356</td>\n",
       "      <td>0.095735</td>\n",
       "      <td>0.079494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.456509</td>\n",
       "      <td>0.284255</td>\n",
       "      <td>0.198853</td>\n",
       "      <td>0.149284</td>\n",
       "      <td>0.117398</td>\n",
       "      <td>0.094875</td>\n",
       "      <td>0.078981</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1         2         3         4         5         6         7  \\\n",
       "country                                                                    \n",
       "China    1.0  0.514831  0.402777  0.360426  0.336274  0.319856  0.307084   \n",
       "France   1.0  0.334946  0.177732  0.119004  0.088479  0.069935  0.058341   \n",
       "Germany  1.0  0.451284  0.278411  0.195910  0.146474  0.115151  0.093868   \n",
       "India    1.0  0.446728  0.326962  0.284527  0.261603  0.246736  0.234847   \n",
       "Italy    1.0  0.341684  0.179502  0.119914  0.090015  0.071135  0.057523   \n",
       "Spain    1.0  0.339098  0.178985  0.119879  0.088648  0.070065  0.057617   \n",
       "UK       1.0  0.458938  0.285133  0.198067  0.149421  0.117356  0.095735   \n",
       "US       1.0  0.456509  0.284255  0.198853  0.149284  0.117398  0.094875   \n",
       "\n",
       "                8  \n",
       "country            \n",
       "China    0.295453  \n",
       "France   0.049688  \n",
       "Germany  0.078465  \n",
       "India    0.225769  \n",
       "Italy    0.048695  \n",
       "Spain    0.048503  \n",
       "UK       0.079494  \n",
       "US       0.078981  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the retention rate for each montly cost for different countries\n",
    "\n",
    "grouped = data.groupby('country').apply(retention_rate)\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "be4f1813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "grouped.T.plot(kind='line', marker='o', ax=ax)\n",
    "ax.set_xlabel('Billing Cycle', fontsize=12)\n",
    "ax.set_ylabel('Retention Rate', fontsize=12)\n",
    "ax.grid(True)\n",
    "ax.legend(fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee1d4273",
   "metadata": {},
   "source": [
    "From the country graph, we can see that:\n",
    "\n",
    "1. customers from China and Indian are most loyal ones. Their retention rate is high and the 'dropping rate' is slow. To increase the revenue, we should keep tight touch to customers in China and Indian. for examples, sending coupons to them from time to time.\n",
    "\n",
    "2. UK, US, Germany has medium 'retention rate'. France, Italy, Spain has the lowest 'retention rate'. Possible reasons maybe because the poor economic conditions in these countries. We could consider lowering the 'monthly cost' in these countries, to keep more customers maintain subscription."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79f19da3",
   "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>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>source</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ads</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.388873</td>\n",
       "      <td>0.222094</td>\n",
       "      <td>0.152607</td>\n",
       "      <td>0.114959</td>\n",
       "      <td>0.092043</td>\n",
       "      <td>0.076756</td>\n",
       "      <td>0.066237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friend_referral</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.699443</td>\n",
       "      <td>0.617038</td>\n",
       "      <td>0.561282</td>\n",
       "      <td>0.519854</td>\n",
       "      <td>0.487596</td>\n",
       "      <td>0.461110</td>\n",
       "      <td>0.436507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seo</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.562395</td>\n",
       "      <td>0.420012</td>\n",
       "      <td>0.337594</td>\n",
       "      <td>0.286254</td>\n",
       "      <td>0.247877</td>\n",
       "      <td>0.218680</td>\n",
       "      <td>0.194475</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   1         2         3         4         5         6  \\\n",
       "source                                                                   \n",
       "ads              1.0  0.388873  0.222094  0.152607  0.114959  0.092043   \n",
       "friend_referral  1.0  0.699443  0.617038  0.561282  0.519854  0.487596   \n",
       "seo              1.0  0.562395  0.420012  0.337594  0.286254  0.247877   \n",
       "\n",
       "                        7         8  \n",
       "source                               \n",
       "ads              0.076756  0.066237  \n",
       "friend_referral  0.461110  0.436507  \n",
       "seo              0.218680  0.194475  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the retention rate for each montly cost for different sources\n",
    "\n",
    "grouped = data.groupby('source').apply(retention_rate)\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dbd0a0d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualization\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "grouped.T.plot(kind='line', marker='o', ax=ax)\n",
    "ax.set_xlabel('Billing Cycle', fontsize=12)\n",
    "ax.set_ylabel('Retention Rate', fontsize=12)\n",
    "ax.grid(True)\n",
    "ax.legend(fontsize=16)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dc40bed",
   "metadata": {},
   "source": [
    "From the source influence, we can see that:\n",
    "\n",
    "1. Subscribers from 'friend_referral' are the most loyal, way more loyal then subscribers from advertisement and search engine.\n",
    "\n",
    "2. To improve the revenue, we can launch some program to improve 'user referral'. For example, current subscribers can invite new users to subscribe. If your friends subscribe after your referral, you can get certain amounts of rewards."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2d587c2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}