Predicting-Music-Track-Skipping.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 1 \n",
    "Listening behavior on playlists is analyzed using the following tables:\n",
    "- playlists​ table with columns:\n",
    "    ○ user_id ​ : the user who uploaded the playlist\n",
    "    ○ playlist_id ​ : the unique identifier of the playlist\n",
    "    \n",
    "- listings ​ table with columns:\n",
    "    ○ playlist_id ​ : the unique identifier of the playlist\n",
    "    ○ track_id ​ : the unique identifier of the track\n",
    "    ○ position ​ : the position of this track in the playlist\n",
    "    ○ track_duration ​ : the length of the track recording (in milliseconds)\n",
    "    \n",
    "- plays ​ table with columns:\n",
    "    ○ timestamp ​ : the time the play occurred\n",
    "    ○ user_id ​ : the user who played the given track\n",
    "    ○ track_id ​ : the unique identifier of the track\n",
    "    ○ playlist_id ​ : the unique identifier of the playlist (0 if the play did not happen in a playlist)\n",
    "    ○ listening_duration ​ : the duration that the user listened to the track (in milliseconds)\n",
    "\n",
    "Using this schema, write or describe a SQL query that would help you find out the following:\n",
    "\n",
    "#### What are the 5 most listened to playlists?\n",
    "-----------\n",
    "\n",
    "\n",
    "SELECT playlist_id, SUM(playlist_id) FROM plays\n",
    "\n",
    "WHERE playlist_id != 0\n",
    "\n",
    "GROUPBY playlist_id\n",
    "\n",
    "SORT DESCENDING\n",
    "\n",
    "LIMIT 5;\n",
    "\n",
    "#### Given a user X, what are their 3 most listened to playlists?\n",
    "-----------\n",
    "\n",
    "\n",
    "SELECT playlist_id, SUM(playlist_id) FROM plays\n",
    "\n",
    "WHERE user_id = X \n",
    "\n",
    "AND playlist_id != 0\n",
    "\n",
    "GROUPBY playlist_id\n",
    "\n",
    "SORT DESCENDING\n",
    "\n",
    "LIMIT 3;\n",
    "\n",
    "#### For a user X, recommend 5 playlists they would like that they haven’t heard before. You can attempt this via SQL or any other programming language of your choice. Please explain your logic.\n",
    "-----------\n",
    "\n",
    "\n",
    "SQL isn't a great language for personalized recommender systems. But since this is the SQL section, I'll pick the simplest approach to the question that I can do in SQL. I'm going to recommend the most popular playlists that the user hasn't listened to. This has the major drawback of the fact that there is no personalization involved with this approach. But I can do it quickly and in SQL.\n",
    "\n",
    "We're going to take the top query and exclude the playlists in the second query from the top query.\n",
    "\n",
    "------------\n",
    "\n",
    "SELECT playlist_id FROM plays\n",
    "\n",
    "WHERE playlist_id != 0 \n",
    "\n",
    "AND playlist_id NOT IN \n",
    "\n",
    "(\n",
    "\n",
    "SELECT playlist_id FROM plays\n",
    "\n",
    "WHERE user_id = X\n",
    "\n",
    "GROUPBY playlist_id\n",
    "\n",
    ")\n",
    "\n",
    "GROUPBY playlist_id\n",
    "\n",
    "SORT DESCENDING\n",
    "\n",
    "LIMIT 5;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 2\n",
    "Consider the following: the Growth team at NoiseNimbus has the goal of increasing downloads of\n",
    "the NoiseNimbus mobile apps by 20% within the next 3 months. The product manager has\n",
    "proposed several new features she believes will drive this growth. As a data scientist embedded\n",
    "in this team, how would you work with the team’s product manager and engineers to contribute\n",
    "to the most successful outcome? We’re looking for a short description of any questions you would ask, processes/tools you would put in place, and analyses you would prioritize.\n",
    "\n",
    "#### Answer\n",
    "Are mobile downloads the only KPI? I'd look at historically, what factors have lead to increased mobile downloads. I'd look at past downloads and build a model with downloads as the target and look at the features that contributed to downloads. I'd see if the product manager's features are in line with this. Going forward I would set sub-KPIs that look at the contributing features to download. For instsance, perhaps downloading is correlated with x minutes per month of listening. I'd be monitoring for these signs from users. I'd also look at ways of engaging with users that are in line with achieving the sub-KPIs of user downloads. Working with product managers and engineers, I'd make sure the platform is doing that, monitoring it consistently and adjusting as necessary. I'd want to understand if users from a particular area or demographic are more likely to download and study those users, perhaps surveying them to better understand their behavior and see if there are ways we could influence other users to behave accordingly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Question 3\n",
    "\n",
    "We would like to know what factors affect a listener’s choice to skip a recommended track.\n",
    "In this dataset, each record (i.e., each sub-list of the “data” field) represents the total amount of\n",
    "time the user spent listening to a track that had been recommended before a different track was\n",
    "played or the listening session ended.\n",
    "\n",
    "The provided attributes (in the “columns” field) are:\n",
    "\n",
    "- ts ​ : the time the user listened to the recommended track (as Unix timestamp)\n",
    "- country_code ​ : the two-character country abbreviation where the user was located at the time of listening\n",
    "- client_version ​ : the version of the client that the listener was using\n",
    "- listening_context ​ : the section of the NoiseNimbus web app where the user listened to the recommended track\n",
    "    ○ “collection”: any of the pages in the ​ Collection section\n",
    "    ○ “you”: the listener’s ​ own profile page\n",
    "    ○ “search”: any of the pages in the ​ Search experience\n",
    "    ○ “stream”: the page of the ​ Stream experience\n",
    "    ○ “personal-recommended’: the page of the ​ Discover experience​ for personalized recommendations\n",
    "    ○ “charts”: the page of the ​ Charts experience​ for trending content discovery\n",
    "    ○ “tracks”: the dedicated page of an uploaded track\n",
    "    ○ “users”: the profile page of another user (not the one listening)\n",
    "    ○ “playlists”: the dedicated page of a playlist\n",
    "- recommender_algorithm_name ​ : the name of the algorithm that produced the track recommendation for the listener\n",
    "    ○ “content-based”: an implementation of content-based filtering\n",
    "    ○ “collaborative”: an implementation of collaborative filtering\n",
    "    ○ “hybrid”: a hybrid implementation of content-based and collaborative filtering\n",
    "    ○ “fallback”: a static list of the popular tracks, used only as a fallback strategy\n",
    "- track_id ​ : the unique identifier of the recommended track\n",
    "- track_genre_category ​ : the broad genre category describing the recommended track\n",
    "- track_upload_date ​ : the date the recommended track was uploaded (as Unix timestamp)\n",
    "- track_duration ​ : the length of the recommended track recording (in milliseconds)\n",
    "- listen_duration ​ : the duration that the user listened to the recommended track (in milliseconds)\n",
    "- listener_id ​ : the unique identifier of the user who listened to the recommended track\n",
    "- listener_signup_date ​ : the date the listener created their NoiseNimbus account (as Unix timestamp)\n",
    "- listener_top_genre_category_listened ​ : the genre category that the user has most frequently listened to (based on all their past listening in all contexts)\n",
    "- listener_prev_month_listening_time ​ : the total duration that the user listened in the previous month (in all contexts)\n",
    "- listener_prev_month_avg_daily_tracks_listened ​ : the daily mean number of distinct tracks that the user listened in the previous month (in all contexts) Using this dataset and a programming language of your choice, produce a model that can predict whether or not a user will ​ skip​ past a recommended track.\n",
    "\n",
    "\n",
    "The goal is ​ not​ to produce a model that can predict with >90% accuracy or that is optimized for speed, but rather to produce a model that is both interpretable and more accurate than either a uniform random model or a model that always predicts 0.\n",
    "\n",
    "\n",
    "While there is no official definition of a skip, you can develop an operational definition based on ​ track_duration ​ and ​ listen_duration ​ Based on the model you build, please explain any insights Noise Nimbus’s Recommendations team could use to decrease skip rate, as well as any additional analyses you would perform to better understand skip behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "from statsmodels import robust\n",
    "from scipy.stats import linregress\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "from sklearn.model_selection import train_test_split,  cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression, Lasso, RidgeClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#load the data\n",
    "\n",
    "df = pd.read_json(\"sc_data_science_challenge.json\", orient='split')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>country_code</th>\n",
       "      <th>client_version</th>\n",
       "      <th>listening_context</th>\n",
       "      <th>recommender_algorithm_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>track_genre_category</th>\n",
       "      <th>track_upload_date</th>\n",
       "      <th>track_duration</th>\n",
       "      <th>listen_duration</th>\n",
       "      <th>listener_id</th>\n",
       "      <th>listener_signup_date</th>\n",
       "      <th>listener_top_genre_category_listened</th>\n",
       "      <th>listener_prev_month_listening_time</th>\n",
       "      <th>listener_prev_month_avg_daily_tracks_listened</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1467398683</td>\n",
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       "      <td>HipHop &amp; R&amp;B</td>\n",
       "      <td>617270940.0</td>\n",
       "      <td>22.5</td>\n",
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       "    <tr>\n",
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       "      <td>1467398924</td>\n",
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       "      <td>204.0.20</td>\n",
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       "      <td>617270940.0</td>\n",
       "      <td>22.5</td>\n",
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       "      <td>617270940.0</td>\n",
       "      <td>22.5</td>\n",
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       "      <th>3</th>\n",
       "      <td>1467401951</td>\n",
       "      <td>US</td>\n",
       "      <td>36.0.8</td>\n",
       "      <td>collection</td>\n",
       "      <td>content-based</td>\n",
       "      <td>211cd7be59941679f5ea847879b3b19b</td>\n",
       "      <td>HipHop &amp; R&amp;B</td>\n",
       "      <td>1402272000</td>\n",
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      "text/plain": [
       "           ts country_code client_version listening_context  \\\n",
       "0  1467398683           GB       204.0.20            tracks   \n",
       "1  1467398924           GB       204.0.20            tracks   \n",
       "2  1467820636           GB       204.0.20             users   \n",
       "3  1467401951           US         36.0.8        collection   \n",
       "4  1467402159           US         36.0.8        collection   \n",
       "\n",
       "  recommender_algorithm_name                          track_id  \\\n",
       "0              content-based  b47911d0e80d1a8a959a2b726654bbfa   \n",
       "1              content-based  1f5e4bc82ba7fc802f780870f9b69191   \n",
       "2              content-based  092495283958e5d00f61cf2afe970d83   \n",
       "3              content-based  211cd7be59941679f5ea847879b3b19b   \n",
       "4              content-based  0e7ca1a7524aba84473540fa0d0ec75f   \n",
       "\n",
       "  track_genre_category  track_upload_date  track_duration  listen_duration  \\\n",
       "0   Dance & Electronic         1466640000          413933           413933   \n",
       "1         HipHop & R&B         1466726400          402640           110722   \n",
       "2                  Pop         1467072000          494811           193818   \n",
       "3         HipHop & R&B         1402272000          361585           361585   \n",
       "4                  Pop         1448668800          373700           160162   \n",
       "\n",
       "                        listener_id  listener_signup_date  \\\n",
       "0  0001d5755511cae40e5b32d83a3decce            1324771200   \n",
       "1  0001d5755511cae40e5b32d83a3decce            1324771200   \n",
       "2  0001d5755511cae40e5b32d83a3decce            1324771200   \n",
       "3  000264c5b34ad04999535c8b6a8b86e2            1379376000   \n",
       "4  000264c5b34ad04999535c8b6a8b86e2            1379376000   \n",
       "\n",
       "  listener_top_genre_category_listened  listener_prev_month_listening_time  \\\n",
       "0                         HipHop & R&B                         617270940.0   \n",
       "1                         HipHop & R&B                         617270940.0   \n",
       "2                         HipHop & R&B                         617270940.0   \n",
       "3                         HipHop & R&B                         112239180.0   \n",
       "4                         HipHop & R&B                         112239180.0   \n",
       "\n",
       "   listener_prev_month_avg_daily_tracks_listened  \n",
       "0                                           22.5  \n",
       "1                                           22.5  \n",
       "2                                           22.5  \n",
       "3                                           10.5  \n",
       "4                                           10.5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# to understand skips, the most important aspect is going to be looking at the listen to duration ratio\n",
    "\n",
    "df['listen_ratio'] = df.listen_duration/df.track_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 828169 entries, 0 to 828168\n",
      "Data columns (total 16 columns):\n",
      "ts                                               828169 non-null int64\n",
      "country_code                                     828169 non-null object\n",
      "client_version                                   828169 non-null object\n",
      "listening_context                                828169 non-null object\n",
      "recommender_algorithm_name                       828169 non-null object\n",
      "track_id                                         828169 non-null object\n",
      "track_genre_category                             828169 non-null object\n",
      "track_upload_date                                828169 non-null int64\n",
      "track_duration                                   828169 non-null int64\n",
      "listen_duration                                  828169 non-null int64\n",
      "listener_id                                      828169 non-null object\n",
      "listener_signup_date                             828169 non-null int64\n",
      "listener_top_genre_category_listened             824392 non-null object\n",
      "listener_prev_month_listening_time               766561 non-null float64\n",
      "listener_prev_month_avg_daily_tracks_listened    766561 non-null float64\n",
      "listen_ratio                                     828159 non-null float64\n",
      "dtypes: float64(3), int64(5), object(8)\n",
      "memory usage: 107.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "629\n"
     ]
    }
   ],
   "source": [
    "#we're getting some funky results--like infinity--in the listen ratio column for 629 variables \n",
    "#because the track duration is listed as 0 and the listen_duration is listed in positive seconds\n",
    "\n",
    "inf_df = df[df['listen_ratio'] == np.inf]\n",
    "print(len(inf_df))\n",
    "\n",
    "#we're going to get rid of these erroneous rows\n",
    "\n",
    "df = df[df['listen_ratio'] != np.inf]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "#we're also getting errors in the listen duration column because the track is listed as a zero duration\n",
    "\n",
    "null_df = df[df['listen_ratio'].isnull()]\n",
    "print(len(null_df))\n",
    "\n",
    "#we're going to get rid of these columns as well\n",
    "\n",
    "df = df[np.isfinite(df['listen_ratio'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#filling in nulls with zeros\n",
    "\n",
    "df = df.fillna(value=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 827530 entries, 0 to 828168\n",
      "Data columns (total 16 columns):\n",
      "ts                                               827530 non-null int64\n",
      "country_code                                     827530 non-null object\n",
      "client_version                                   827530 non-null object\n",
      "listening_context                                827530 non-null object\n",
      "recommender_algorithm_name                       827530 non-null object\n",
      "track_id                                         827530 non-null object\n",
      "track_genre_category                             827530 non-null object\n",
      "track_upload_date                                827530 non-null int64\n",
      "track_duration                                   827530 non-null int64\n",
      "listen_duration                                  827530 non-null int64\n",
      "listener_id                                      827530 non-null object\n",
      "listener_signup_date                             827530 non-null int64\n",
      "listener_top_genre_category_listened             827530 non-null object\n",
      "listener_prev_month_listening_time               827530 non-null float64\n",
      "listener_prev_month_avg_daily_tracks_listened    827530 non-null float64\n",
      "listen_ratio                                     827530 non-null float64\n",
      "dtypes: float64(3), int64(5), object(8)\n",
      "memory usage: 107.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "351.2744816013424\n",
      "-0.09999892713647361\n"
     ]
    }
   ],
   "source": [
    "#the data still has inconsistencies and errors\n",
    "#the listen ratio should be between 0 and 1, but it's between 351 and -0.999\n",
    "\n",
    "print(df.listen_ratio.max())\n",
    "print(df.listen_ratio.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#getting rid of erroneous listen times\n",
    "#the listen ratio should be between 0 and 1\n",
    "\n",
    "df = df[(df['listen_ratio'] >= 0) & (df['listen_ratio'] <=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#converting timestamps to datetime\n",
    "#looking at timestamps, we only have the date, not minute or second information for\n",
    "#user signups and for track upload date\n",
    "\n",
    "df['ts_dt'] = pd.to_datetime(df['ts'],unit='s')\n",
    "df['upload_date_dt'] = pd.to_datetime(df['track_upload_date'],unit='s')\n",
    "df['signup_date_dt'] = pd.to_datetime(df['listener_signup_date'], unit='s')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#we're going to see if the year, month or day of week of \n",
    "#the listen date, the upload or signup date have an impact on skipping\n",
    "\n",
    "df['ts_year'] = df['ts_dt'].dt.strftime('%y').astype(int)\n",
    "df['upload_year'] = df['upload_date_dt'].dt.strftime('%y').astype(int)\n",
    "df['signup_year'] = df['signup_date_dt'].dt.strftime('%y').astype(int)\n",
    "df['ts_month'] = df['ts_dt'].dt.strftime('%-m').astype(int)\n",
    "df['upload_month'] = df['upload_date_dt'].dt.strftime('%-m').astype(int)\n",
    "df['signup_month'] = df['signup_date_dt'].dt.strftime('%-m').astype(int)\n",
    "df['ts_day'] = df['ts_dt'].dt.strftime('%w').astype(int)\n",
    "df['upload_day'] = df['upload_date_dt'].dt.strftime('%w').astype(int)\n",
    "df['signup_day'] = df['signup_date_dt'].dt.strftime('%w').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 810053 entries, 0 to 828168\n",
      "Data columns (total 28 columns):\n",
      "ts                                               810053 non-null int64\n",
      "country_code                                     810053 non-null object\n",
      "client_version                                   810053 non-null object\n",
      "listening_context                                810053 non-null object\n",
      "recommender_algorithm_name                       810053 non-null object\n",
      "track_id                                         810053 non-null object\n",
      "track_genre_category                             810053 non-null object\n",
      "track_upload_date                                810053 non-null int64\n",
      "track_duration                                   810053 non-null int64\n",
      "listen_duration                                  810053 non-null int64\n",
      "listener_id                                      810053 non-null object\n",
      "listener_signup_date                             810053 non-null int64\n",
      "listener_top_genre_category_listened             810053 non-null object\n",
      "listener_prev_month_listening_time               810053 non-null float64\n",
      "listener_prev_month_avg_daily_tracks_listened    810053 non-null float64\n",
      "listen_ratio                                     810053 non-null float64\n",
      "ts_dt                                            810053 non-null datetime64[ns]\n",
      "upload_date_dt                                   810053 non-null datetime64[ns]\n",
      "signup_date_dt                                   810053 non-null datetime64[ns]\n",
      "ts_year                                          810053 non-null int64\n",
      "upload_year                                      810053 non-null int64\n",
      "signup_year                                      810053 non-null int64\n",
      "ts_month                                         810053 non-null int64\n",
      "upload_month                                     810053 non-null int64\n",
      "signup_month                                     810053 non-null int64\n",
      "ts_day                                           810053 non-null int64\n",
      "upload_day                                       810053 non-null int64\n",
      "signup_day                                       810053 non-null int64\n",
      "dtypes: datetime64[ns](3), float64(3), int64(14), object(8)\n",
      "memory usage: 179.2+ MB\n"
     ]
    }
   ],
   "source": [
    "#we have a clean dataframe with no nulls\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "42004074\n",
      "89\n"
     ]
    }
   ],
   "source": [
    "#looking at track duration to better understand skips\n",
    "\n",
    "print(df.track_duration.max())\n",
    "print(df.track_duration.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#eliminating tracks that are less than one second long\n",
    "#these are likely in error\n",
    "\n",
    "df = df[df['track_duration'] >= 1000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I define a skip as any time the listener abandons a song before the song finishes. \n",
    "It could be because the listener explicitly presses the skip button, or it could be that they searched for\n",
    "and started another song before the current song finished, or they clicked on a different song in the playlist.\n",
    "\n",
    "I'm not eliminating the really long or really short tracks (as long as they're longer than 1 second) from this dataset. Eliminating really long or short tracks would probably lead to less bias towards skipping the longer the track is.  In future analyses, I'd probably limit track length to one or two median absolute deviations from the median and/or find an alternate means of differentiating songs from mixes. The longer the track the more likely it is skipped, but when we don't differentiate between songs and longer mixes, we're not comprehensively understanding skipping behaviour. People are less likely to listen to a mix all the way through than a single song, but I also understand that NoiseNimbus is top online platform for longer independent DJs to post longer mixes. So I'd like to differentiate between songs and mixes in future analyses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Data for Modeling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#setting target variable by letting all listen_ratio values less than 1 become zero values\n",
    "\n",
    "df['skipped'] = df['listen_ratio'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#making a copy of the dataframe before beginning to prepare for modeling\n",
    "\n",
    "df_orig = df.copy(deep = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#encoding labels\n",
    "\n",
    "le = LabelEncoder()\n",
    "df['country_code'] = le.fit_transform(df['country_code'])\n",
    "df['client_version'] = le.fit_transform(df['client_version'])\n",
    "df['listening_context'] = le.fit_transform(df['listening_context'])\n",
    "df['recommender_algorithm_name'] = le.fit_transform(df['recommender_algorithm_name'])\n",
    "df['track_id'] = le.fit_transform(df['track_id'])\n",
    "df['track_genre_category'] = le.fit_transform(df['track_genre_category'])\n",
    "df['listener_id'] = le.fit_transform(df['listener_id']) \n",
    "df['listener_top_genre_category_listened'] = le.fit_transform(df['listener_top_genre_category_listened'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>country_code</th>\n",
       "      <th>client_version</th>\n",
       "      <th>listening_context</th>\n",
       "      <th>recommender_algorithm_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>track_genre_category</th>\n",
       "      <th>track_upload_date</th>\n",
       "      <th>track_duration</th>\n",
       "      <th>listen_duration</th>\n",
       "      <th>...</th>\n",
       "      <th>ts_year</th>\n",
       "      <th>upload_year</th>\n",
       "      <th>signup_year</th>\n",
       "      <th>ts_month</th>\n",
       "      <th>upload_month</th>\n",
       "      <th>signup_month</th>\n",
       "      <th>ts_day</th>\n",
       "      <th>upload_day</th>\n",
       "      <th>signup_day</th>\n",
       "      <th>skipped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                ts  country_code  client_version  listening_context  \\\n",
       "828164  1469210707             0             144                  6   \n",
       "828165  1469210923             1             144                  6   \n",
       "828166  1469211113             1             144                  6   \n",
       "828167  1469211300             0             144                  6   \n",
       "828168  1469211451             0             144                  6   \n",
       "\n",
       "        recommender_algorithm_name  track_id  track_genre_category  \\\n",
       "828164                           1    372461                     7   \n",
       "828165                           1    115912                     0   \n",
       "828166                           1     56300                     0   \n",
       "828167                           1    118332                     7   \n",
       "828168                           1     76666                     7   \n",
       "\n",
       "        track_upload_date  track_duration  listen_duration   ...     ts_year  \\\n",
       "828164         1306540800          376736           376736   ...          16   \n",
       "828165         1293148800          332924           332924   ...          16   \n",
       "828166         1410220800          324144           324144   ...          16   \n",
       "828167         1357171200          269325           269325   ...          16   \n",
       "828168         1306713600          398321           379204   ...          16   \n",
       "\n",
       "        upload_year  signup_year  ts_month  upload_month  signup_month ts_day  \\\n",
       "828164           11           14         7             5            11      5   \n",
       "828165           10           14         7            12            11      5   \n",
       "828166           14           14         7             9            11      5   \n",
       "828167           13           14         7             1            11      5   \n",
       "828168           11           14         7             5            11      5   \n",
       "\n",
       "       upload_day signup_day  skipped  \n",
       "828164          6          1        1  \n",
       "828165          5          1        1  \n",
       "828166          2          1        1  \n",
       "828167          4          1        1  \n",
       "828168          1          1        0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WEbEgIu7Nn18AHgK2ACYCU/JkU4AD8+eJwGWRzAA2kLQZsC9wU0QsjoglwE3A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\ntKQcbN0bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49118d0890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking at distributions\n",
    "\n",
    "df.hist(figsize=(20,16), color='darkviolet', bins=100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### overall, the data distributions are skewed and unevenly binned; tree algorithms will probably do well with this sort of data \n",
    "#### I will not be including ts_year and ts_month in the model. There aren't enough variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bb1du3X3AfSVt43LLU4ApzexzTG75frK5emZmZmZmFbnQMzMzMzOz2uS7blbk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\n25I+7FsRQAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff29b16bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking at correlations to prevent colinearity and for feature engineering\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,10))  \n",
    "corr = df.corr()\n",
    "sns.heatmap(corr,\n",
    "            xticklabels=corr.columns.values,\n",
    "            yticklabels=corr.columns.values);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- I'm not including listen_ratio due to colinearity with listen_duration and track duration\n",
    "- I'm not including listener id and track id, because those variables seem to be random\n",
    "- I'm also not going to include listener_signup_date and track_upload_date to avoid colinearity with the day/month/year time elements\n",
    "- Listen duration and track duration are correlated and somewhat explains skipping - longer tracks are more likely to be skipped and shorter tracks are less likely to be skipped - so I can't include both of these variables.\n",
    "- I'm seeing corrlations in prev_month_listening_time and prev_month_avg_tracks, so eliminating prev_month_listening_time because prev_month_avg_daily_tracks_listened is representative of almost the same information, but binned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#converting float to int\n",
    "\n",
    "df['listener_prev_month_listening_time'] = df['listener_prev_month_listening_time'].astype(int)\n",
    "df['listener_prev_month_avg_daily_tracks_listened'] = df['listener_prev_month_avg_daily_tracks_listened'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#set up data for modeling\n",
    "\n",
    "y = df['skipped'].values\n",
    "X = df[['country_code', 'client_version', 'listening_context','recommender_algorithm_name',\n",
    "       'track_genre_category', 'track_duration','listener_top_genre_category_listened',\n",
    "        'listener_prev_month_avg_daily_tracks_listened','upload_year', 'signup_year','upload_month', \n",
    "        'signup_month', 'upload_day', 'signup_day', 'ts_day']]\n",
    "\n",
    "#min max scaler\n",
    "\n",
    "X = MinMaxScaler().fit_transform(X)\n",
    "\n",
    "#test-train split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "0.6000665913461178\n",
      "RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
      "        max_iter=None, normalize=False, random_state=None, solver='lsqr',\n",
      "        tol=0.001)\n",
      "0.5984878526909638\n",
      "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
      "          learning_rate=1.0, n_estimators=50, random_state=None)\n",
      "0.66556554010071\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best')\n",
      "0.7630096295575791\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "0.7914119610029106\n",
      "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
      "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
      "0.7882133317371363\n"
     ]
    }
   ],
   "source": [
    "#choosing some Classifcation Algorithms that I like for this problem \n",
    "#and looking at their accuracy scores\n",
    "#I'm choosing 2 linear models and 4 Ensemble/Tree algorithms\n",
    "#Logistic, Ridge, Adaptive Boosting, Decision Tree, Random Forest, \n",
    "#and Extremely Randomized Trees\n",
    "#I suspect the Ensemble/Tree algorithms will do better\n",
    "\n",
    "models = [LogisticRegression(penalty='l1'),  #L1 regularization\n",
    "          RidgeClassifier(solver=\"lsqr\"),    #L2 regularization\n",
    "          AdaBoostClassifier(),\n",
    "          DecisionTreeClassifier(),\n",
    "          RandomForestClassifier(n_jobs=-1),\n",
    "          ExtraTreesClassifier(n_jobs=-1)\n",
    "         ]\n",
    "\n",
    "res = []\n",
    "\n",
    "for model in models:\n",
    "    print model\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    score = accuracy_score(y_test, y_pred)\n",
    "    print score\n",
    "    res.append([model, score])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's look at better performing algorithms - As predicted, tree algorithms did better with this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7638476330143433\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X_train, y_train)\n",
    "y_pred = dt.predict(X_test)\n",
    "score = accuracy_score(y_test, y_pred)\n",
    "print score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7886996730290083\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "score = accuracy_score(y_test, y_pred)\n",
    "print score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.789324434534721\n"
     ]
    }
   ],
   "source": [
    "et = ExtraTreesClassifier()\n",
    "et.fit(X_train, y_train)\n",
    "y_pred = et.predict(X_test)\n",
    "score = accuracy_score(y_test, y_pred)\n",
    "print score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5761647873940338\n"
     ]
    }
   ],
   "source": [
    "# null accuracy - a dumb model that always predicts 0 would be right ~58% of the time, \n",
    "#so these models are all better than a dumb model\n",
    "\n",
    "print max(y_test.mean(), 1 - y_test.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The best models are Random Forest and Extra Trees. Either of these are better than a dumb model and run fairly quickly. With these models, you need to be careful about overfitting, but they are fine for our current purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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VAACAzhFWAQAA6JyN13cBADAW7LPvGQO1u+mGc9ZyJQAwNhhZBQAAoHOEVQAA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\n+j4wH6kxWmernseAs6G+m+L5fXZ90nXjjbYfLhyp9Vo9WG0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BNgS+ZfuB3FH0lMKZmvJv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49a0482890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Algorithmic Importance by Feature so we can understand track skipping\n",
    "\n",
    "cols = ['country_code', 'client_version', 'listening_context','recommender_algorithm_name',\n",
    "       'track_genre_category', 'track_duration','listener_top_genre_category_listened',\n",
    "        'listener_prev_month_avg_daily_tracks_listened','upload_year', 'signup_year',\n",
    "        'upload_month', 'signup_month', 'upload_day', 'signup_day', 'ts_day']\n",
    "\n",
    "features = pd.DataFrame(zip(cols, np.transpose(dt.feature_importances_),\n",
    "                 np.transpose(rf.feature_importances_), \n",
    "                 np.transpose(et.feature_importances_) ), \n",
    "             columns = ['feature', 'dt_importance', 'rf_importance', 'et_importance'])\n",
    "features = features.set_index('feature')\n",
    "features.plot(kind='bar', colormap='tab20b', title='Algorithmic Importance by Feature', figsize = (16,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "I ran the model several times with different features, and this was one of the better runs to understand the features. The Track Duration and previous month's Average Daily Tracks Listened have the biggest impact on track skips, as does Listening Context, Client Version and Signup Month.\n",
    "\n",
    "Sadly, the recommender algorithms seem to have little effect on a user skipping tracks or not. Also, the location, genre, top genre and timing of everything except the signup month and maybe track upload month seems to have little to no effect on a user skipping tracks. \n",
    "\n",
    "I want to dive further into the Previous Month's Average Daily Tracks, Listening Context and Signup Month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df_orig[['listening_context', 'listener_prev_month_avg_daily_tracks_listened', 'signup_month']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = pd.get_dummies(df2, drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>listener_prev_month_avg_daily_tracks_listened</th>\n",
       "      <th>signup_month</th>\n",
       "      <th>listening_context_collection</th>\n",
       "      <th>listening_context_personal-recommended</th>\n",
       "      <th>listening_context_playlists</th>\n",
       "      <th>listening_context_search</th>\n",
       "      <th>listening_context_stream</th>\n",
       "      <th>listening_context_tracks</th>\n",
       "      <th>listening_context_users</th>\n",
       "      <th>listening_context_you</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>22.5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22.5</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.5</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listener_prev_month_avg_daily_tracks_listened  signup_month  \\\n",
       "0                                           22.5            12   \n",
       "1                                           22.5            12   \n",
       "2                                           22.5            12   \n",
       "3                                           10.5             9   \n",
       "4                                           10.5             9   \n",
       "\n",
       "   listening_context_collection  listening_context_personal-recommended  \\\n",
       "0                             0                                       0   \n",
       "1                             0                                       0   \n",
       "2                             0                                       0   \n",
       "3                             1                                       0   \n",
       "4                             1                                       0   \n",
       "\n",
       "   listening_context_playlists  listening_context_search  \\\n",
       "0                            0                         0   \n",
       "1                            0                         0   \n",
       "2                            0                         0   \n",
       "3                            0                         0   \n",
       "4                            0                         0   \n",
       "\n",
       "   listening_context_stream  listening_context_tracks  \\\n",
       "0                         0                         1   \n",
       "1                         0                         1   \n",
       "2                         0                         0   \n",
       "3                         0                         0   \n",
       "4                         0                         0   \n",
       "\n",
       "   listening_context_users  listening_context_you  \n",
       "0                        0                      0  \n",
       "1                        0                      0  \n",
       "2                        1                      0  \n",
       "3                        0                      0  \n",
       "4                        0                      0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([u'tracks', u'users', u'collection', u'you', u'search',\n",
       "       u'playlists', u'stream', u'personal-recommended', u'charts'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#our base case is: charts\n",
    "\n",
    "df_orig['listening_context'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 810005 entries, 0 to 828168\n",
      "Data columns (total 10 columns):\n",
      "listener_prev_month_avg_daily_tracks_listened    810005 non-null float64\n",
      "signup_month                                     810005 non-null int64\n",
      "listening_context_collection                     810005 non-null uint8\n",
      "listening_context_personal-recommended           810005 non-null uint8\n",
      "listening_context_playlists                      810005 non-null uint8\n",
      "listening_context_search                         810005 non-null uint8\n",
      "listening_context_stream                         810005 non-null uint8\n",
      "listening_context_tracks                         810005 non-null uint8\n",
      "listening_context_users                          810005 non-null uint8\n",
      "listening_context_you                            810005 non-null uint8\n",
      "dtypes: float64(1), int64(1), uint8(8)\n",
      "memory usage: 24.7 MB\n"
     ]
    }
   ],
   "source": [
    "df3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = df3.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X2 = df3\n",
    "\n",
    "#min max scaler\n",
    "\n",
    "X2 = MinMaxScaler().fit_transform(X2)\n",
    "\n",
    "#test-train split\n",
    "\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X2, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
      "          penalty='l1', random_state=None, solver='liblinear', tol=0.0001,\n",
      "          verbose=0, warm_start=False)\n",
      "0.6357041847797622\n",
      "RidgeClassifier(alpha=1.0, class_weight=None, copy_X=True, fit_intercept=True,\n",
      "        max_iter=None, normalize=False, random_state=None, solver='lsqr',\n",
      "        tol=0.001)\n",
      "0.6353412993542884\n",
      "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,\n",
      "          learning_rate=1.0, n_estimators=50, random_state=None)\n",
      "0.637503647559689\n",
      "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
      "            max_features=None, max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "            splitter='best')\n",
      "0.7134888627844161\n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "0.7130736021428945\n",
      "ExtraTreesClassifier(bootstrap=False, class_weight=None, criterion='gini',\n",
      "           max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=-1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n",
      "0.7134888627844161\n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
      "           weights='uniform')\n",
      "0.6794374901796469\n"
     ]
    }
   ],
   "source": [
    "models = [LogisticRegression(penalty='l1'),  #L1 regularization\n",
    "          RidgeClassifier(solver=\"lsqr\"),    #L2 regularization\n",
    "          AdaBoostClassifier(),\n",
    "          DecisionTreeClassifier(),\n",
    "          RandomForestClassifier(n_jobs=-1),\n",
    "          ExtraTreesClassifier(n_jobs=-1),\n",
    "          KNeighborsClassifier()\n",
    "         ]\n",
    "\n",
    "res = []\n",
    "\n",
    "for model in models:\n",
    "    print model\n",
    "    model.fit(X2_train, y2_train)\n",
    "    y2_pred = model.predict(X2_test)\n",
    "    score = accuracy_score(y2_test, y2_pred)\n",
    "    print score\n",
    "    res.append([model, score])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7134664162632528\n"
     ]
    }
   ],
   "source": [
    "dt = DecisionTreeClassifier()\n",
    "dt.fit(X2_train, y2_train)\n",
    "y2_pred = dt.predict(X2_test)\n",
    "score = accuracy_score(y2_test, y2_pred)\n",
    "print score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7133317371362728\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(X2_train, y2_train)\n",
    "y2_pred = rf.predict(X2_test)\n",
    "score = accuracy_score(y2_test, y2_pred)\n",
    "print score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7134963449581372\n"
     ]
    }
   ],
   "source": [
    "et = ExtraTreesClassifier()\n",
    "et.fit(X2_train, y2_train)\n",
    "y2_pred = et.predict(X2_test)\n",
    "score = accuracy_score(y2_test, y2_pred)\n",
    "print score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CKQAAAJ0RSgEAAOiMUAoAAEBnhFIAAAA6I5QCAADQGaEUAACAzgilAAAAdEYo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\n/SqLpHbZPlDSbGB/YK4kU50b+5ztibL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123BIWh/4ENUNWVOVTx9p+5aiwRqURWkUJ2nu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\n7HF40oSnfSktbZ+k04FTbS8onaXLJJ1o+6hB18ZZyncHu8/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UiIiIiKhkp3Sw70jaov+D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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f490e9dc790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols=['listener_prev_month_avg_daily_tracks_listened', 'signup_month',\n",
    "       u'listening_context_collection',\n",
    "       u'listening_context_personal-recommended',\n",
    "       u'listening_context_playlists', u'listening_context_search',\n",
    "       u'listening_context_stream', u'listening_context_tracks',\n",
    "       u'listening_context_users', u'listening_context_you']\n",
    "\n",
    "features = pd.DataFrame(zip(cols, np.transpose(dt.feature_importances_),\n",
    "                 np.transpose(rf.feature_importances_), \n",
    "                 np.transpose(et.feature_importances_) ), \n",
    "             columns = ['feature', 'dt_importance', 'rf_importance', 'et_importance'])\n",
    "features = features.set_index('feature')\n",
    "features.plot(kind='bar', colormap='tab20b', title='Algorithmic Importance by Feature', figsize = (16,8));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion:\n",
    "Compared to our base case, listening_context_charts, the listening_context_of_tracks is important in terms of skipping tracks. Signup month is significant, but not that important so I'm not going to dive deeper into that variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts</th>\n",
       "      <th>country_code</th>\n",
       "      <th>client_version</th>\n",
       "      <th>listening_context</th>\n",
       "      <th>recommender_algorithm_name</th>\n",
       "      <th>track_id</th>\n",
       "      <th>track_genre_category</th>\n",
       "      <th>track_upload_date</th>\n",
       "      <th>track_duration</th>\n",
       "      <th>listen_duration</th>\n",
       "      <th>...</th>\n",
       "      <th>ts_year</th>\n",
       "      <th>upload_year</th>\n",
       "      <th>signup_year</th>\n",
       "      <th>ts_month</th>\n",
       "      <th>upload_month</th>\n",
       "      <th>signup_month</th>\n",
       "      <th>ts_day</th>\n",
       "      <th>upload_day</th>\n",
       "      <th>signup_day</th>\n",
       "      <th>skipped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1467398683</td>\n",
       "      <td>GB</td>\n",
       "      <td>204.0.20</td>\n",
       "      <td>tracks</td>\n",
       "      <td>content-based</td>\n",
       "      <td>b47911d0e80d1a8a959a2b726654bbfa</td>\n",
       "      <td>Dance &amp; Electronic</td>\n",
       "      <td>1466640000</td>\n",
       "      <td>413933</td>\n",
       "      <td>413933</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1467398924</td>\n",
       "      <td>GB</td>\n",
       "      <td>204.0.20</td>\n",
       "      <td>tracks</td>\n",
       "      <td>content-based</td>\n",
       "      <td>1f5e4bc82ba7fc802f780870f9b69191</td>\n",
       "      <td>HipHop &amp; R&amp;B</td>\n",
       "      <td>1466726400</td>\n",
       "      <td>402640</td>\n",
       "      <td>110722</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1467820636</td>\n",
       "      <td>GB</td>\n",
       "      <td>204.0.20</td>\n",
       "      <td>users</td>\n",
       "      <td>content-based</td>\n",
       "      <td>092495283958e5d00f61cf2afe970d83</td>\n",
       "      <td>Pop</td>\n",
       "      <td>1467072000</td>\n",
       "      <td>494811</td>\n",
       "      <td>193818</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1467401951</td>\n",
       "      <td>US</td>\n",
       "      <td>36.0.8</td>\n",
       "      <td>collection</td>\n",
       "      <td>content-based</td>\n",
       "      <td>211cd7be59941679f5ea847879b3b19b</td>\n",
       "      <td>HipHop &amp; R&amp;B</td>\n",
       "      <td>1402272000</td>\n",
       "      <td>361585</td>\n",
       "      <td>361585</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1467402159</td>\n",
       "      <td>US</td>\n",
       "      <td>36.0.8</td>\n",
       "      <td>collection</td>\n",
       "      <td>content-based</td>\n",
       "      <td>0e7ca1a7524aba84473540fa0d0ec75f</td>\n",
       "      <td>Pop</td>\n",
       "      <td>1448668800</td>\n",
       "      <td>373700</td>\n",
       "      <td>160162</td>\n",
       "      <td>...</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           ts country_code client_version listening_context  \\\n",
       "0  1467398683           GB       204.0.20            tracks   \n",
       "1  1467398924           GB       204.0.20            tracks   \n",
       "2  1467820636           GB       204.0.20             users   \n",
       "3  1467401951           US         36.0.8        collection   \n",
       "4  1467402159           US         36.0.8        collection   \n",
       "\n",
       "  recommender_algorithm_name                          track_id  \\\n",
       "0              content-based  b47911d0e80d1a8a959a2b726654bbfa   \n",
       "1              content-based  1f5e4bc82ba7fc802f780870f9b69191   \n",
       "2              content-based  092495283958e5d00f61cf2afe970d83   \n",
       "3              content-based  211cd7be59941679f5ea847879b3b19b   \n",
       "4              content-based  0e7ca1a7524aba84473540fa0d0ec75f   \n",
       "\n",
       "  track_genre_category  track_upload_date  track_duration  listen_duration  \\\n",
       "0   Dance & Electronic         1466640000          413933           413933   \n",
       "1         HipHop & R&B         1466726400          402640           110722   \n",
       "2                  Pop         1467072000          494811           193818   \n",
       "3         HipHop & R&B         1402272000          361585           361585   \n",
       "4                  Pop         1448668800          373700           160162   \n",
       "\n",
       "    ...    ts_year  upload_year signup_year  ts_month  upload_month  \\\n",
       "0   ...         16           16          11         7             6   \n",
       "1   ...         16           16          11         7             6   \n",
       "2   ...         16           16          11         7             6   \n",
       "3   ...         16           14          13         7             6   \n",
       "4   ...         16           15          13         7            11   \n",
       "\n",
       "   signup_month ts_day upload_day signup_day  skipped  \n",
       "0            12      5          4          0        1  \n",
       "1            12      5          5          0        0  \n",
       "2            12      3          2          0        0  \n",
       "3             9      5          1          2        1  \n",
       "4             9      5          6          2        0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at the original dataframe, before we started modeling the data\n",
    "\n",
    "df_orig.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Track Duration vs Skips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>listened</th>\n",
       "      <th>skipped</th>\n",
       "      <th>skipped%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>track_duration</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1050</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1188</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1278</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1507</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1554</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                total  listened  skipped  skipped%\n",
       "track_duration                                    \n",
       "1050                1         1        0       0.0\n",
       "1188                1         1        0       0.0\n",
       "1278                1         1        0       0.0\n",
       "1507                2         2        0       0.0\n",
       "1554                3         3        0       0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making a pivot table to understand track duration\n",
    "\n",
    "lc = pd.pivot_table(df_orig, values='skipped', columns='track_duration', aggfunc=('count', 'sum')).T\n",
    "lc['skipped'] = lc['count'] - lc['sum']\n",
    "lc.columns = ['total', 'listened', 'skipped']\n",
    "lc['skipped%'] = (lc.skipped/lc.total)*100\n",
    "lc['skipped%'] = lc['skipped%'].round(2)\n",
    "lc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1      0.00\n",
      "0.2     18.18\n",
      "0.3     33.33\n",
      "0.4     42.31\n",
      "0.5     50.00\n",
      "0.6     66.67\n",
      "0.7    100.00\n",
      "0.8    100.00\n",
      "0.9    100.00\n",
      "Name: skipped%, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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kn++2H5jk8PGXJkmSNH1GeZTGx4EvAvfptr8PvGpcBUmSJE2zUcLZdlV1JnAnQFXdDtwx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff26b68d090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking at deciles\n",
    "\n",
    "decile_skips = lc['skipped%'].quantile(q=[.1,.20,.30,.4,.5,.6,.7,.8,.9])\n",
    "print(decile_skips)\n",
    "sns.set_palette(\"husl\")\n",
    "decile_skips.plot(kind='bar', title='Skip% by Decile', figsize = (10,6))\n",
    "plt.ylabel('Skip Percent')\n",
    "plt.xlabel('Decile')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the track duration increases, the skipped % increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Listening Context vs Skips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>listened</th>\n",
       "      <th>skipped</th>\n",
       "      <th>skipped%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listening_context</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>charts</th>\n",
       "      <td>6294</td>\n",
       "      <td>5701</td>\n",
       "      <td>593</td>\n",
       "      <td>9.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection</th>\n",
       "      <td>54866</td>\n",
       "      <td>43550</td>\n",
       "      <td>11316</td>\n",
       "      <td>20.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>personal-recommended</th>\n",
       "      <td>5374</td>\n",
       "      <td>4984</td>\n",
       "      <td>390</td>\n",
       "      <td>7.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>playlists</th>\n",
       "      <td>90459</td>\n",
       "      <td>67174</td>\n",
       "      <td>23285</td>\n",
       "      <td>25.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>search</th>\n",
       "      <td>11131</td>\n",
       "      <td>8168</td>\n",
       "      <td>2963</td>\n",
       "      <td>26.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stream</th>\n",
       "      <td>31251</td>\n",
       "      <td>21449</td>\n",
       "      <td>9802</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tracks</th>\n",
       "      <td>469190</td>\n",
       "      <td>209955</td>\n",
       "      <td>259235</td>\n",
       "      <td>55.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>users</th>\n",
       "      <td>100100</td>\n",
       "      <td>76868</td>\n",
       "      <td>23232</td>\n",
       "      <td>23.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>you</th>\n",
       "      <td>41340</td>\n",
       "      <td>28703</td>\n",
       "      <td>12637</td>\n",
       "      <td>30.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       total  listened  skipped  skipped%\n",
       "listening_context                                        \n",
       "charts                  6294      5701      593      9.42\n",
       "collection             54866     43550    11316     20.62\n",
       "personal-recommended    5374      4984      390      7.26\n",
       "playlists              90459     67174    23285     25.74\n",
       "search                 11131      8168     2963     26.62\n",
       "stream                 31251     21449     9802     31.37\n",
       "tracks                469190    209955   259235     55.25\n",
       "users                 100100     76868    23232     23.21\n",
       "you                    41340     28703    12637     30.57"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making a pivot table to understand listening context\n",
    "\n",
    "lc = pd.pivot_table(df_orig, values='skipped', columns='listening_context', aggfunc=('count', 'sum')).T\n",
    "lc['skipped'] = lc['count'] - lc['sum']\n",
    "lc.columns = ['total', 'listened', 'skipped']\n",
    "lc['skipped%'] = (lc.skipped/lc.total)*100\n",
    "lc['skipped%'] = lc['skipped%'].round(2)\n",
    "lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff273939610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#looking at the count plot, skipped tracks = 0, listened tracks = 1\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "sns.set_palette(\"husl\")\n",
    "ax = sns.countplot(x=\"listening_context\", hue='skipped', data=df_orig).set_title(\"Distribution of Skipped-0 & Listened-1 Tracks by Listening Context\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Users are overwhelmingly listening to more than half of all tracks in all contexts except the \"tracks\" context\n",
    "\n",
    "Users are less likely to skip tracks in the \"charts\" and \"personal-recommended\" context, skipping less than 10% of all tracks. Users are mostly likely to skip tracks in the \"tracks\" listening context, skipping 55% of tracks.\n",
    "\n",
    "Proportionally, listeners listen to the most tracks in \"personal-recommendation\", \"charts\", \"users\" and \"collections\" contexts, but those contexts don't get a lot of plays overall.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Previous Month Average Daily Tracks vs Skips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>listened</th>\n",
       "      <th>skipped</th>\n",
       "      <th>skipped%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_month_avg_daily_tracks</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60407</td>\n",
       "      <td>37572</td>\n",
       "      <td>22835</td>\n",
       "      <td>37.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15611</td>\n",
       "      <td>8027</td>\n",
       "      <td>7584</td>\n",
       "      <td>48.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19391</td>\n",
       "      <td>11234</td>\n",
       "      <td>8157</td>\n",
       "      <td>42.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>21316</td>\n",
       "      <td>13336</td>\n",
       "      <td>7980</td>\n",
       "      <td>37.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>18161</td>\n",
       "      <td>10187</td>\n",
       "      <td>7974</td>\n",
       "      <td>43.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18166</td>\n",
       "      <td>10622</td>\n",
       "      <td>7544</td>\n",
       "      <td>41.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>17972</td>\n",
       "      <td>10135</td>\n",
       "      <td>7837</td>\n",
       "      <td>43.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>16903</td>\n",
       "      <td>9571</td>\n",
       "      <td>7332</td>\n",
       "      <td>43.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>17710</td>\n",
       "      <td>10052</td>\n",
       "      <td>7658</td>\n",
       "      <td>43.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18135</td>\n",
       "      <td>10507</td>\n",
       "      <td>7628</td>\n",
       "      <td>42.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18475</td>\n",
       "      <td>11248</td>\n",
       "      <td>7227</td>\n",
       "      <td>39.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17004</td>\n",
       "      <td>10121</td>\n",
       "      <td>6883</td>\n",
       "      <td>40.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19420</td>\n",
       "      <td>11837</td>\n",
       "      <td>7583</td>\n",
       "      <td>39.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>15488</td>\n",
       "      <td>8777</td>\n",
       "      <td>6711</td>\n",
       "      <td>43.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19792</td>\n",
       "      <td>11950</td>\n",
       "      <td>7842</td>\n",
       "      <td>39.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>17272</td>\n",
       "      <td>9252</td>\n",
       "      <td>8020</td>\n",
       "      <td>46.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>15365</td>\n",
       "      <td>8556</td>\n",
       "      <td>6809</td>\n",
       "      <td>44.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>16802</td>\n",
       "      <td>10329</td>\n",
       "      <td>6473</td>\n",
       "      <td>38.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16365</td>\n",
       "      <td>9413</td>\n",
       "      <td>6952</td>\n",
       "      <td>42.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14686</td>\n",
       "      <td>8655</td>\n",
       "      <td>6031</td>\n",
       "      <td>41.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>15602</td>\n",
       "      <td>9054</td>\n",
       "      <td>6548</td>\n",
       "      <td>41.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>13404</td>\n",
       "      <td>7988</td>\n",
       "      <td>5416</td>\n",
       "      <td>40.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>14660</td>\n",
       "      <td>8872</td>\n",
       "      <td>5788</td>\n",
       "      <td>39.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>12497</td>\n",
       "      <td>7285</td>\n",
       "      <td>5212</td>\n",
       "      <td>41.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>14466</td>\n",
       "      <td>9610</td>\n",
       "      <td>4856</td>\n",
       "      <td>33.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>14380</td>\n",
       "      <td>7927</td>\n",
       "      <td>6453</td>\n",
       "      <td>44.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>12521</td>\n",
       "      <td>7283</td>\n",
       "      <td>5238</td>\n",
       "      <td>41.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>12407</td>\n",
       "      <td>7144</td>\n",
       "      <td>5263</td>\n",
       "      <td>42.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>10204</td>\n",
       "      <td>5691</td>\n",
       "      <td>4513</td>\n",
       "      <td>44.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>11172</td>\n",
       "      <td>6391</td>\n",
       "      <td>4781</td>\n",
       "      <td>42.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>392</td>\n",
       "      <td>20</td>\n",
       "      <td>372</td>\n",
       "      <td>94.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>367</td>\n",
       "      <td>78</td>\n",
       "      <td>289</td>\n",
       "      <td>78.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>23</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>69.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>1103</td>\n",
       "      <td>254</td>\n",
       "      <td>849</td>\n",
       "      <td>76.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>155</td>\n",
       "      <td>131</td>\n",
       "      <td>24</td>\n",
       "      <td>15.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>44.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>128</td>\n",
       "      <td>0</td>\n",
       "      <td>128</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>80</td>\n",
       "      <td>68</td>\n",
       "      <td>12</td>\n",
       "      <td>15.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>690</td>\n",
       "      <td>52</td>\n",
       "      <td>638</td>\n",
       "      <td>92.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>47.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>66.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>27.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>87.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>95.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>90.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>74</td>\n",
       "      <td>33</td>\n",
       "      <td>41</td>\n",
       "      <td>55.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>525</td>\n",
       "      <td>371</td>\n",
       "      <td>154</td>\n",
       "      <td>29.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>637</th>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             total  listened  skipped  skipped%\n",
       "last_month_avg_daily_tracks                                    \n",
       "0                            60407     37572    22835     37.80\n",
       "1                            15611      8027     7584     48.58\n",
       "3                            19391     11234     8157     42.07\n",
       "4                            21316     13336     7980     37.44\n",
       "6                            18161     10187     7974     43.91\n",
       "7                            18166     10622     7544     41.53\n",
       "9                            17972     10135     7837     43.61\n",
       "10                           16903      9571     7332     43.38\n",
       "12                           17710     10052     7658     43.24\n",
       "13                           18135     10507     7628     42.06\n",
       "15                           18475     11248     7227     39.12\n",
       "16                           17004     10121     6883     40.48\n",
       "18                           19420     11837     7583     39.05\n",
       "19                           15488      8777     6711     43.33\n",
       "21                           19792     11950     7842     39.62\n",
       "22                           17272      9252     8020     46.43\n",
       "24                           15365      8556     6809     44.32\n",
       "25                           16802     10329     6473     38.53\n",
       "27                           16365      9413     6952     42.48\n",
       "28                           14686      8655     6031     41.07\n",
       "30                           15602      9054     6548     41.97\n",
       "31                           13404      7988     5416     40.41\n",
       "33                           14660      8872     5788     39.48\n",
       "34                           12497      7285     5212     41.71\n",
       "36                           14466      9610     4856     33.57\n",
       "37                           14380      7927     6453     44.87\n",
       "39                           12521      7283     5238     41.83\n",
       "40                           12407      7144     5263     42.42\n",
       "42                           10204      5691     4513     44.23\n",
       "43                           11172      6391     4781     42.79\n",
       "...                            ...       ...      ...       ...\n",
       "234                            392        20      372     94.90\n",
       "237                            367        78      289     78.75\n",
       "238                             23         7       16     69.57\n",
       "240                           1103       254      849     76.97\n",
       "241                             14         0       14    100.00\n",
       "247                            155       131       24     15.48\n",
       "249                              1         0        1    100.00\n",
       "250                              1         0        1    100.00\n",
       "259                              9         5        4     44.44\n",
       "264                            128         0      128    100.00\n",
       "268                             80        68       12     15.00\n",
       "277                            690        52      638     92.46\n",
       "285                              1         0        1    100.00\n",
       "292                              1         0        1    100.00\n",
       "294                             17         9        8     47.06\n",
       "297                              1         0        1    100.00\n",
       "300                              2         0        2    100.00\n",
       "306                              9         3        6     66.67\n",
       "309                             11         8        3     27.27\n",
       "313                              8         1        7     87.50\n",
       "319                             21         1       20     95.24\n",
       "325                             11         1       10     90.91\n",
       "336                              2         0        2    100.00\n",
       "337                              1         0        1    100.00\n",
       "345                             16         0       16    100.00\n",
       "369                              1         0        1    100.00\n",
       "370                              1         0        1    100.00\n",
       "402                             74        33       41     55.41\n",
       "417                            525       371      154     29.33\n",
       "637                             61         0       61    100.00\n",
       "\n",
       "[182 rows x 4 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#looking at previous month average daily tracks listened to skipped tracks in a pivot table\n",
    "\n",
    "lc = pd.pivot_table(df_orig, values='skipped', \n",
    "                    columns='listener_prev_month_avg_daily_tracks_listened', \n",
    "                    aggfunc=('count', 'sum')).T\n",
    "lc = lc.reset_index()\n",
    "lc['skipped'] = lc['count'] - lc['sum']\n",
    "lc.columns = ['last_month_avg_daily_tracks','total', 'listened', 'skipped']\n",
    "\n",
    "#simplifying averages to intergers\n",
    "\n",
    "lc.last_month_avg_daily_tracks = lc.last_month_avg_daily_tracks.astype(int)\n",
    "\n",
    "#grouping by integer\n",
    "\n",
    "lc = lc.groupby(by ='last_month_avg_daily_tracks').sum()\n",
    "lc['skipped%'] = (lc.skipped/lc.total)*100\n",
    "lc['skipped%'] = lc['skipped%'].round(2)\n",
    "lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>listened</th>\n",
       "      <th>skipped</th>\n",
       "      <th>skipped%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>182.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4450.576923</td>\n",
       "      <td>2563.472527</td>\n",
       "      <td>1887.104396</td>\n",
       "      <td>53.056923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7165.608985</td>\n",
       "      <td>4316.429043</td>\n",
       "      <td>2894.190045</td>\n",
       "      <td>25.740376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>157.000000</td>\n",
       "      <td>46.250000</td>\n",
       "      <td>58.750000</td>\n",
       "      <td>36.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1107.000000</td>\n",
       "      <td>561.000000</td>\n",
       "      <td>537.500000</td>\n",
       "      <td>45.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5753.750000</td>\n",
       "      <td>3391.750000</td>\n",
       "      <td>2551.500000</td>\n",
       "      <td>70.357500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60407.000000</td>\n",
       "      <td>37572.000000</td>\n",
       "      <td>22835.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              total      listened       skipped    skipped%\n",
       "count    182.000000    182.000000    182.000000  182.000000\n",
       "mean    4450.576923   2563.472527   1887.104396   53.056923\n",
       "std     7165.608985   4316.429043   2894.190045   25.740376\n",
       "min        1.000000      0.000000      1.000000    2.960000\n",
       "25%      157.000000     46.250000     58.750000   36.687500\n",
       "50%     1107.000000    561.000000    537.500000   45.100000\n",
       "75%     5753.750000   3391.750000   2551.500000   70.357500\n",
       "max    60407.000000  37572.000000  22835.000000  100.000000"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lc.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qqpbkXq21y6e576lJnt5ae8ScN7aAVdV/JnnmmvtHMf9V1cVJjm2tnTB0L9Opqo8neX9r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\nfS3wPmxcHOBx9nSZUvX6NPDnkrH4UCL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RkZGRkZGRkZGRkZHRNPKiUkZGRkZG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\nxv8BzLpVDpp55jgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff2971a90d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#barplot looking at the skipped % per last month average daily tracks\n",
    "\n",
    "y=lc['skipped%']\n",
    "x=lc.index\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "ax.set_xticklabels(x, rotation=90)\n",
    "sns.barplot(x,y, data=lc).set_title(\"Skipped Percent by Last Month Average Daily Tracks\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=0.10977262270961044, intercept=37.07897500988852, rvalue=0.4262021662674043, pvalue=1.9884911839329137e-09, stderr=0.017366493306871154)\n"
     ]
    },
    {
     "data": {
      "image/png": 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Yjh6OFUhhmLadGpcdGGztgkGoBAAAAADt5JWKVCxTjYS+4uWKfG5hfSe2ZKYg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff293a76d50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#simple linear regression visualizing the relationship \n",
    "#between total tracks listened and last month average daily tracks\n",
    "\n",
    "#resetting index\n",
    "\n",
    "lc2 = lc.reset_index()\n",
    "lc2.last_month_avg_daily_tracks = lc2.last_month_avg_daily_tracks.astype(int)\n",
    "\n",
    "#setting variables\n",
    "\n",
    "y=lc2['skipped%']\n",
    "x=lc2['last_month_avg_daily_tracks']\n",
    "\n",
    "#line regress in SciPy\n",
    "\n",
    "print(linregress(x, y))\n",
    "\n",
    "#visualizing the regression of these 2 variables\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "sns.regplot(x,y, data=lc2).set_title(\"Linear Regression of Skipped % by Last Month Average Daily Tracks\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is a moderate and positive correlation between these two variables (0.42) that is statistically significant due to the p-value (1.99^10-09).  We can see a moderate and slow increase in skipped % as the number of last month average listening increases due to the slope (0.11)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CN+UaUVV1WJLnJHloVZ0/smurJDfNdscAAAAAWFimW6z8c+kWJr9/kr8Zqd+R\n5MrZ7BQAAAAAC8+UQVRr7TtJvpPkSVX1gCRP6Hd9q7V21xCdAwAAAGDhWOsaUVV1SJIvJTkkybOS\nfLGqlsx2xwAAAABYWKabmrfaHyd5QmvthiSpqu2T/EOSpbPZMQAAAAAWlnW5a949VodQvZvW8XUA\nAAAA8DPrEihdVFWXVNULquoFSS5MsmJtL6qqX6iqL1XVv1bVN6rqjX39oVX1xaq6qqo+UlX37Os/\n32+v7PfvMnKs1/f1f6+qvUfqi/rayqp63fpdOgAAAABDWpcgqiV5b5LfSPLoJKes47F/nOTprbVH\nJ9ktyaKq2iPJ25K8o7W2a5JbkhzVtz8qyS2ttYcneUffLlX1iCSHJnlkkkVJTqqqLapqiyTvTrJP\nkkckOaxvCwAAAMA8tC5B1O+21s5trb26tfaq1trydOHPtFrnzn7z5/pHS/L03L2+1BlJDuyfH9Bv\np9+/Z1VVXz+7tfbj1tq3k6xMsnv/WNlau7q19pMkZ/dtAQAAAJiHpgyiqurYqvpakl+pqitHHt9O\ncuW6HLwfufTVJDckuTTJfyS5tbV2V99kVZId++c7JrkmSfr9tyXZbrQ+4TVT1QEAAACYh6a7a95Z\nSS5K8udJRtdfuqO1dvO6HLy19tMku1XV1kmWJ/m1yZr1P2uKfVPVJwvR2iS1VNUxSY5Jkp223W4t\nvQYAAABgNkwZRLXWbks3KumwDT1Ja+3Wqvp0kj2SbF1VW/ajnnZKcm3fbFWSnZOsqqotk9wvyc0j\n9dVGXzNVfeL5T0m/ttVuD3nYpGEVAAAAALNrXdaImpGq2r4fCZWquleS30nyrSSfSrKkb3ZEkvP6\n5+f32+n3f7K11vr6of1d9R6aZNckX0pyeZJd+7vw3TPdgubnz9b1AAAAALBhppuat6EelOSM/u52\n90hyTmvtgqr6ZpKzq+rNSb6S5H19+/cl+UBVrUw3EurQJGmtfaOqzknyzSR3JTmun/KXqnpZkkuS\nbJHktNbaN2bxegAAAADYALMWRLXWrkzymEnqV6e7493E+o+SHDLFsd6S5C2T1FckWbHBnQUAAABg\n1s3a1DwAAAAAGCWIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGI\nAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAA\nBiGIAgDUMCOLAAAgAElEQVQAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAA\nBiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigA\nAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQ\ngigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAA\nAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABiGI\nAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABrHlXHdgU3bje05dY3v7lxw9Rz0BAAAAmP+MiAIA\nAABgEIIoAAAAAAYhiAIAAABgEIIoAAAAAAYxa0FUVe1cVZ+qqm9V1Teq6pV9fduqurSqrup/btPX\nq6pOrKqVVXVlVT125FhH9O2vqqojRuqPq6qv9a85sapqtq4HAAAAgA0zmyOi7kry+621X0uyR5Lj\nquoRSV6X5LLW2q5JLuu3k2SfJLv2j2OSnJx0wVWS45M8McnuSY5fHV71bY4Zed2iWbweAAAAADbA\nrAVRrbXrWmv/0j+/I8m3kuyY5IAkZ/TNzkhyYP/8gCRnts4XkmxdVQ9KsneSS1trN7fWbklyaZJF\n/b6tWmufb621JGeOHAsAAACAeWaQNaKqapckj0nyxSQPaK1dl3RhVZId+mY7Jrlm5GWr+tp09VWT\n1AEAAACYh2Y9iKqq+yRZluT3Wmu3T9d0klqbQX2yPhxTVVdU1RU33TldFwAAAACYLVvO5sGr6ufS\nhVAfaq2d25evr6oHtdau66fX3dDXVyXZeeTlOyW5tq8/dUL90319p0naj2mtnZLklCTZ7SEPmzSs\nYsOsetcLx2o7vey0OegJAAAAMF/N5l3zKsn7knyrtfb2kV3nJ1l957sjkpw3Uj+8v3veHklu66fu\nXZJkr6rapl+kfK8kl/T77qiqPfpzHT5yLAAAAADmmdkcEfWUJM9P8rWq+mpfe0OSv0hyTlUdleS7\nSQ7p961Ism+SlUl+kOTIJGmt3VxVb0pyed/uhNbazf3zY5O8P8m9klzUPwAAAACYh2YtiGqtfTaT\nr+OUJHtO0r4lOW6KY52WZGyeV2vtiiSP2oBurtWNJ58+Vtv+2CNn85TzwrXv/v2x2oOP+5s56AkA\nAACwUAxy1zwAAAAAEEQBAAAAMAhBFAAAAACDEEQBAAAAMAhBFAAAAACDmLW75jG/fO+k49fYfuBL\n3zhHPQEAAAA2V0ZEAQAAADAII6JmwY3vee9YbfuXvDg3vuekSeov3Wjn/d7Jbx6rPfDYP95oxwcA\nAADYEEZEAQAAADAII6LmgRvec+JYbYeXvGIOegIAAAAwewRRm6DrT37bWO0Bx752DnoCAAAAsO5M\nzQMAAABgEIIoAAAAAAZhah6z6rsnPmus9kuvOGcOegIAAADMNSOiAAAAABiEEVGbuetOev1Y7UEv\n/fM56AkAAACw0Ami5rEb3vP2sdoOL3n1HPQEAAAAYMMJophXVr7rgLHaw1923hz0BAAAANjYrBEF\nAAAAwCAEUQAAAAAMwtQ8Nhn//u41p+39ynGm7AEAAMCmxIgoAAAAAAYhiAIAAABgEIIoAAAAAAYh\niAIAAABgEBYr79148pljte2PPXwOesLG8rWTnzlW+/Vjz5+DngAAAACJIIr19F/vPm6stuNx717v\n43z7xAPHag99xcdm1CcAAABg02BqHgAAAACDEEQBAAAAMAhBFAAAAACDEEQBAAAAMAiLlbPJ++ZJ\n43fHe8RL1//ueF9+zzPGao97ycdn1CcAAABgnBFRAAAAAAzCiCiYoS+8d/+x2h4vviCfO2W8/uRj\nLhiiSwAAADCvGREFAAAAwCAEUQAAAAAMQhAFAAAAwCAEUQAAAAAMQhAFAAAAwCAEUQAAAAAMYsu5\n7gAM7asnP2OsttuxH5+DngAAAMDmxYgoAAAAAAYhiAIAAABgEIIoAAAAAAYhiAIAAABgEIIoAAAA\nAAbhrnmwFpe/d/wue0948czusvdPf7//WO03X3TBjI4FAAAAmxpBFMwDn/n7/cZqv/2iC+egJwAA\nADB7TM0DAAAAYBCCKAAAAAAGIYgCAAAAYBDWiALmxEdPXzRWO+TIi+egJwAAAAzFiCgAAAAABiGI\nAgAAAGAQgigAAAAABmGNKNgE/cOp+43VfufoC+egJwAAALDujIgCAAAAYBCCKAAAAAAGIYgCAAAA\nYBDWiAI2iuWn7TNWO+iFF81BTwAAAJivZm1EVFWdVlU3VNXXR2rbVtWlVXVV/3Obvl5VdWJVrayq\nK6vqsSOvOaJvf1VVHTFSf1xVfa1/zYlVVbN1LQAAAABsuNmcmvf+JIsm1F6X5LLW2q5JLuu3k2Sf\nJLv2j2OSnJx0wVWS45M8McnuSY5fHV71bY4Zed3EcwEAAAAwj8za1LzW2j9W1S4TygckeWr//Iwk\nn07y2r5+ZmutJflCVW1dVQ/q217aWrs5Sarq0iSLqurTSbZqrX2+r5+Z5MAk5gGxoHzq1P3Gak87\n+sI56AkAAABsuKEXK39Aa+26JOl/7tDXd0xyzUi7VX1tuvqqSeqTqqpjquqKqrripjtv3+CLAAAA\nAGD9zZfFyidb36nNoD6p1topSU5Jkt0e8rAp2wFrd/4ki5I/06LkAAAArIOhR0Rd30+5S//zhr6+\nKsnOI+12SnLtWuo7TVIHAAAAYJ4aekTU+UmOSPIX/c/zRuovq6qz0y1Mfltr7bqquiTJW0cWKN8r\nyetbazdX1R1VtUeSLyY5PMnfDXkhwLpZdvr4fQQWH3nxHPQEAACAuTZrQVRVfTjdYuP3r6pV6e5+\n9xdJzqmqo5J8N8khffMVSfZNsjLJD5IcmSR94PSmJJf37U5YvXB5kmPT3ZnvXukWKTc3CAAAAGAe\nm8275h02xa49J2nbkhw3xXFOS3LaJPUrkjxqQ/oIC80n3rfvWG2vo1bMQU8AAABg3NBrRAEAAACw\nmRJEAQAAADAIQRQAAAAAgxBEAQAAADAIQRQAAAAAg5i1u+YB89+KSe6yt6+77AEAADBLBFHAJuHD\n7997rHbYCy6Zg54AAAAwU6bmAQAAADAII6JgM3DxJFPwFpmCBwAAwMAEUcC88pHTF43Vnn3kxTM6\n1pmTTOc73HQ+AACAOSOIAjZpH5wkbHqesAkAAGBeskYUAAAAAIMwIgpghk75wPhorGOebzQWAADA\nVIyIAgAAAGAQgigAAAAABiGIAgAAAGAQgigAAAAABmGxcmBSF5y2zxrb+7/wojnqCQAAAAuFEVEA\nAAAADMKIKGCz8/4z9hqrveCIT8xBTwAAADYvRkQBAAAAMAhBFAAAAACDEEQBAAAAMAhrRAH0Tptk\n7agXLvC1o976kb3Ham949iVz0BMAAGBzIIgCYJ0df86isdobn3XxHPQEAADYFAmiANbi1DPHRw0d\nffj6jxo66YPjx3np84w+AgAANh+CKIBN0NvPGg+1Xv0coRYAADC/CaIANrL3fGA8JHrJ84VEAAAA\n7poHAAAAwCAEUQAAAAAMwtQ8gHng7z605nS+lz/XVL6Jjlk+fse+Uw5yxz4AANiUGBEFAAAAwCAE\nUQAAAAAMQhAFAAAAwCAEUQAAAAAMQhAFAAAAwCDcNQ9gM/C2s/ceq7320IVxZ77DPzZ+N70zD3Q3\nPQAAmI8EUQCMOeEj48HVnz57YQRXAADA3BFEAcxjf3vWeCD0yucIhJh/9jv37WO1Cw9+9Rz0BACA\n+UwQBQC9fc571ljtogPOmYOeAADAwiSIAgA2K/st+/ux2oWLXzQHPQEA2Py4ax4AAAAAgzAiCoBZ\n9dql43e1e9uSqe9q97Jzx9u/62B3wRvavsvfNlZbcdBr56AnAAAsJIIoAJihfc578VjtogPeO2X7\nfT82vnj3igPfnn0/9ppJ6n+5YZ0DAIB5SBAFsID89YfH77L3B4e5y97mZN/lbxyrrTjo+DnoCTAX\nliz7ylht6eLHzEFPAGBygigAADYrBy69dKz2sSW/Owc9AYDNj8XKAQAAABiEEVEAbBR/9NE1Fxl/\nyyEWGN9Q+37sDWO1FQe+dQ56AgAAG4cgCoDNyqLz9x2rXfzMFXPQk2Htu/zNY7UVB/3xHPQEAIDN\nmSAKgAXpkPMWjdU+esDMRmntc94RY7WLDjhjRsdi5vY796Sx2oUHv3QOegJM5tBzvz1WO/vgh85B\nTwCYz6wRBQAAAMAgjIgCANbZfuf+9VjtwoP/YJr275yk/e9t1D4tBPsv/eBY7YIlz5vBcc6e5DiH\nzqhPAACzQRAFwJx49bLxqXNvX2yBcwAAWMgEUQDAnNjv3BPHahce/Io56AkAAEMRRAEAMLhnLv34\nWO38Jc/YqOc4YOklY7Xzluy9Uc+xkD1r2b+N1c5Z/Ktz0BPY9Fx94vfGag97xQPnoCcw/wiiAGAj\n2+djLx+rXXTg381BT9Zu3+V/PlZbcdDr56AnM7ffsveM1S5c/JI56AkL1UHLPjVWW774aXPQE4C7\nXfeX/zVWe9BrdpyDnsD6EUQBACTZb9mpY7ULFx+dJNl/2Wlr1C9Y/MJB+rT/0g+N1S5Y8txBzg2b\no79bfv1Y7eUHPWAOegKwcAmiAIAFab9l7x2rXbj4xRv1HPsvO32sdsHiIzfqOTaW/Zd+ZI3tC5Y8\nu69/dKztBUsOGaRPAMDmRxAFAJugfZf/6RrbKw46YY56wvrYf+mZY7ULlhw+g+N8eJLjHDajPm0s\nz1i6fKz28SUHzUFPANhUXP+Or4zVHvCqx8xBTxjSJh9EVdWiJH+bZIskp7bW/mKOuwQAwAZ45tIL\n1tg+f8n+SZIDlq4Ya3vekn1zwNKLJ6kvmp3OMS+dsPzaNbb/9KAHz1FPNr5ly/57rLZ48f2nbP/x\nc8bbP+NZU7cHmMoNJ50zVtvhpc/a4ONu0kFUVW2R5N1JfjfJqiSXV9X5rbVvzm3PAIDN2f7Lzhir\nXbD4iDnoycztv3TpWO2CJUvmoCfDOXDpZWO1jy3Zc0bHOmjZP62xvXzxbyZJDl72z2Ntz138lCmP\ns3jZl8ZqyxbvPqM+bSr+YPmqsdpfH7TTHPSEqXzyrBvX2H76c7aftv0/feDGsdpvPn/7fO7M8fqT\nD5/6WJeffsNY7QlH7jDtueeb77xj/G56D3nVA7Pqb8brO/2+u+yxMG3SQVSS3ZOsbK1dnSRVdXaS\nA5IIogBgE7Xfue8aq1148MvmoCcwvYOWfWastnzxb89BTzYPf7R8/A5hbzloZncIe/vy8S/9rz5o\n/b/0v+/c8WDkqIN3yBnnjgcsRxw8fVgDsLnY1IOoHZNcM7K9KskT56gvAAAL3jOWnjtW+/iSg6dp\nf94k7Q/YqH1aCA5e9vmx2rmLn/T/2zvzMLuKau3/FgGBMIQpwgcIKIjKLENQAQEHBP1AJkVBBQe4\nVxQR5X6gXEVRroizMtwHkCAICBEQROZRBcGEzBMEEkKAQEIgkDCEENb3x1qbU11n79PndE53n27W\n+zz19Ol3165aVauq9qratWtVxj/kqjF13FWH7Nxyvp+6akodN+qQrVtOpxGOunp2HXfRwZvytWvm\n1PFnH/S2tubdabj0qvoFqiMOsQWqK0o+wTuswSd4f/lzffwDD62Of8MV9fE/flh1/Fsvr5f1o5/t\nm8W0+y6qX+B731Gt73waf359Ojsc3bMdVNPPqfeo+O5jB5ZHxad+PquO2+DEt7cv/V9Ork//W9sA\n8PSvJnbh1z9hu7blG1h+zDvnqjrurcceUhl//jn1Z1UOP7b5sypFVZuO3GkQkU8BH1PVr/j/nwdG\nqOpxWbxjgGP833cBD/rv9YD6Ebnz+E6UaTCXoRNlGsxl6ESZogydzXeiTIO5DJ0o02AuQyfKFGXo\nbL4TZRrMZehEmaIMnc13okyDuQydKNNgLkN+bVNV7X7FWlUHbADeD9yc/P8d4Dst3D9mIPCdKNNg\nLkMnyjSYy9CJMkUZOpvvRJkGcxk6UabBXIZOlCnK0Nl8J8o0mMvQiTJFGTqb70SZBnMZOlGmwVyG\n7q5VhRUY2BgNvFNE3i4ibwE+A1zXzzIFAoFAIBAIBAKBQCAQCARKMKDPiFLV10Tk68DNwBDgQlWt\n/9A9EAgEAoFAIBAIBAKBQCDQ7xjQC1EAqnoDcEMPbz9vgPD9mfebsQz9mfebsQz9mXeUoTPyHsyy\nDoYy9Gfeb8Yy9GfeUYbOyHswyzoYytCfeUcZOiPvwSzrYChDf+b9ZixDd9dKMaAPKw8EAoFAIBAI\nBAKBQCAQCAwcDPQzogKBQCAQCAQCgUAgEAgEAgMEsRAVCAQCgUAgEAgEAoFAIBDoEwz4M6KahYi8\nG/gksBGgwJPAdao6rcE9IwBV1dEishWwLzDdz6Uq4lysql8QkV2Baar6goisCpwM7AisCnxDVaeW\npL85cBDwNuA1YAZwuao+355SBwKBQCAQCAQCgUAgEAh0Dt4UZ0SJyEnAZ4E/AY87vTHwGeBPqnpG\nyT2nAl8AngNuBXYFNgSGA/OwRSMB9gbuAPYC1nFPfucBLwF/Bm7DFr7GAJcDo1R1voh8A9gfuBv4\nODDe8zoIOFZV72prJXQwROStqjqvv+UI9C4Gkp6rZBWRdVV1QX/I1CoGkqz9icGg60AgEAi8uSAi\nq6vq4ibiHaCq15Xw6wCvq+rCBveuqKqvFfkB7wZmquqzFfHfAzyoqq+LyFuAbYBH8/gicixwAbBU\nfSIqIntjL/BfUtVzG8i0M8kLfFWd7rwAI+i64eDfqqoispKqLs3SGQ4saCSrl3lLYA7wTImsU1X1\nxiqZmkGhHxFZq0oXjfQgIuunZVbVpyvSWAt4rarNeH1s7GWYpaqLu6nTpsosIseq6jkisgnwgqou\nFJHNgJ2x9nd197Vk7bXQTYU+11PVZ9J8gX+q6sRm0sY2njxXcm1N4J1YfT/n3NpYXS5qQta6em0U\nv1G+raIq71baTFmbbNRWW4aqDvoAPASsVMK/Bes8ZfdMAh4DhgIvAGsCY7HFpEeAPbHFp7n+e3Zy\n79jk9zhskWkf4PfAfOAmbFAb5nGGAnf5702Acf1QR+u2Ma01vY4uAQ7Prv0eWCcJ6wKPAodiC3kA\nwzzeROBZ4CfA5lk6OwN3An/EBsJbgeeB0cCPvY4nAhOAG4H/rGgDQ4H7gf8CVgGOAq4DzgRWz9uR\n//06sJ7/3gL4O7DQZT255L4VgC8Bf3N5HsAWRT8M/EeJrF/18CNgt0zWm0pkHVnkCawE/LfzNwNv\nK5FzPHCN19PqwPnAZGCUp53LuVerunbdprrukZ670fVY4EJgOrDAwzTgDGCtknQ2wPr02S7PD7B+\n/hfgPSWy/grYIpFhJvAwsAQzoPI2uTpwGjDF5ZsP3Od6GuZy5bL+0kNefxu4TnJZ/w78n5L6mwK8\np0TWuV5Ped09gHm3qJO1ga7Px9prl3bp1/67ZMzdLvk/bZfTsf6Q95N3uD7zdnkNcG4b9Hxlpude\n0XUSZ33MUH0vsH6LY+g6zfJY3z4E2ApYMZNxZ2r9bbjLsm1e9xV5vZvyMXO97P9jS+Ks7mXfrUH6\nmxT6AzbDxoZtsBc8uwIHYy9mdsVfmjWQcwVgBf//LZ53WV0d69cl4fYGvg18tZv62Nnl2R94dzv0\n3Am6bqYteLwDGpWBkr6YXC+VtQe6fk8b9LwfydjUrK4byVrRT4Z3J2vST4ZXydqorzRqlw36Sp/3\n6Z70lbxdNugnde0y6Se7dpNHaT/pga7XS+XuTqft0KfLvASbS5wHrO38wdiL8oM9HAI8BVwGHOxx\ntsKe0bOwiei/gS+T9WHMdlngcffDnou3e56fLZHpQGAZZnd8ErOt78DszguAb3n4NvAM8ARwit/7\nX8C9mJ2gmE34I2CrJP09sZf6t2Ev7q8H7gHuAo7Antk3el4XYPbyE9jzeT5wC7BZIuvSElkXA/t7\nnN0xW+JO4FXg0yWyjvY0cplGY7ZXs/o52OvutlwXDfTwlKc1ze+7DbOT7gN2LNHPa8ArJelv5fc+\n7OW839vGLZ5XXqePuyxlejgt0XOq6+u9DNOBr/jf37uunynR9X9n8hXt9SlsM0iuz2+5XHm+r5el\nn4xXf/K0Znj552E24Xs9zsdch7d5+ndjNt8yrG085uWYhtmDu2J29kxvF6NL6nUstfl/WrbFwD4l\n+S5zfX+Y7PkIfCn5vbHHW4itPdxbkvd1LlPeZsZic8Z/AN/Fxzmszcyjvs28Rklb7Uno8Y0DKXgl\nb1rCb4p1yokl4WVgiccb539XAE4AFgE7ODfT/44Cvui/RwI7++8pwOgkz5WAA7yhzHdubeCBJM5U\nBsak9VTsgZuH27EOdaA3+quAlT0/9c6QhqXYA7WoywuwyeimXvYZWGf/t9f/hv57P2yn2xzgUL/3\nDmygeh/WKTf23xdiE9p1svAX1+c5LvdZwAexjvsqtgi5yMOy4m9S938DDvLf87EB71ls0nsQZoCO\ndJ3sDvwaG6g/6nL+o0TWqcBs4Jte97/09K8Eni6R9WngEo/zC+Ai7IG9ALi4RM4J2OLRyVi7+bbr\n+5+uj1zO24CftqJrbPBfsrx69nhVuh7t9bRB1hd+gz+Is3Av1n5PxvrBSdiD6HXgxRJZX01kvRPY\nxX/Pcd3lbfJazGDYGHsQfg97m/EHrI+dVCLrJM8vr79isTqXdQ5wbUn9PQn8pUTWidhDLq+7e7A+\nn8t6OTbG5P1kXa+jy6hvl0XfyPvKMuztF3Rtl4tdb3k/+Tu2AJu3y8ku6/Lq+Tiqx5926voClyt/\n0E9weXLDdDfseZMbMc8D7/c4qbHyMjVj5fPOX+Dpvki9sToXa2e5QXIRbgyVPBv3xgyNZo29e4Bv\neZzUeFesbeZG5skuQ26UPurp5Ybvw0WZS2Sdj42BfT35GYM9L5vSc/JsLzNY+0XXtD6ZPRi4LLm/\n2wkt1ZOoeZ5mU7qmfZPcW7Ex/+EWdD0Ba5t9Pcm91a+X9ZVZ1CYq+WTwbR3Up6cAP6d8TKyaMO/m\n9Zf3k2c8n7xdTgKOLOkn6vk0O/G+yHXXyoLGOfjL5yZ1OgNrs83q82HqJ/ff8vpYBKwFnOh1tbnr\n+HnM5h3pYRHWBy9M7MFikfNh18ulHuda7GuRVb1e1wPejj3fN/d7zndd/DYL8z2vIv67PP5ibGz9\nPjZvONXL+DRwqscZA6xazLmAB4HTXb4JrvcpwHCP83bgGv/9Uc9js5K2NAF4xH8f6vX/Ps9jUoms\nk4ExiS2wo/9+MOFzWaeUyDQZs5Oa1c9Ir6P/W6KLyRV6mEzJZgrMNnqC+jYz1/PK078/Kf8I4A/+\ney5wQ0n6U6i9mM/18BpwRYmu52Fjxrouw/BEPzNKdD05m2cV7XUqtTl5qs9Fnk+e71xsbpynvxnw\nL+AwYEiS1xCsD9/n/99Lra//A3g4eUb+CljN63oU8H6sX+ye2N8PlNTrbODPFX3x3pJ8H8bG6Hs8\nr98A7/Nr6caXK7GXxStg7fVfJXk/BtxRotN/ex47AL/z/NfF+sgjlLfJsra6apmd1ij0+yJRXwTs\nbKfiwXKeh8Lgec4rftMsjAPm+v0rJGkNc8WMwhYCHkv4i1xh92MP0eJBsX2JTMd7OudhD6JiEWs4\nNhgNhEnr65hxdmcWFgEvJ7Kf4mms6w39JmDb5Posunam8cnvscX/wB7YQ/8pz+OYomMl8R+kZEcZ\nZsC+StcJ6EzMCH8VewP2FLW3Xr/ztrF+KmeRR8Kli4zjvA7XwIyhG7AH87MkhjW1Ae4h7FyxXNaJ\n1Ab5Fb2NXI0NoONKZB0HTCzqjtpK9oMJXydnSd1NTOsukXNlzKC7owVdfx972C6vnu/MZGxW1y82\nkjVL50Ts4Z7LOh1/k1/URSLrpBJZF+NtMq1z7KGwpGJsGp+1p6L+JlIzbh+rqKe0/qYnfCrruETW\nNJ0Jma4LWav6ySxsC3URP22XZ2NtvEtfydJP22VVP1lAbdLddj37/09QPv60U9evUPIm3ut8JvWG\n6b+xiUBuxEwD7vHfqbHyCDVjZTS+mxUzDKZQb6w+gLcxuhokR2NGXD6Z+C02QVjs8Zox9p6kNqG4\nk5rxPsXlyo3Ah7CJTm6UTifpD0ndXYS1sVzO33k72IC+n/w8DNzfrJ792ovY4k9f6/pKl2t5J7Mj\nsc9TKJG1akJbNYmqev5V6bpdk9wVsQW+bVrQ9WPA3yt03ZuT3BWx8aSsr0wo6i+T9RpsotMpfXo1\nr++yMbFqwvwYvjhL137ymus6b5fPUVtkyfvJLJqfeB/t+tqsBV2PpTYxbkanU7C20aw+X8XGgVOz\nMBdYmMi3t8v0RS/DV6nZiLPIvtTInnGF/KsCn8ae6wuA55J4Tya/F2G2/JFZmI2PD3RdSNjEdfRT\nYKhzM7HJbrET7CZqC+HjsvtHYC/IXqU2Hg7JyrSEZOdlwk/EFw/8/61dNzOp2ViTs/oo+HSDwL2J\nflJZJxX3pzJhNs+UZvVT5J3ET3XxGv4CINPDDNyWz8r8CtZP8jbzZNFmqtJP5fD0p5akP4nagkyu\nh4ewY2lyXU9M4s+jtlt0bBO6TttrbrsW+jya8jY2NpO9SH8OydwlizOFWl//ZyLrhEynxSLTOOz8\naEieaR4/rZuxSZmnlpRtCjChJN+x1BY7NwH+n3MzgaeS+8c3qKfudDqerv3kc9TG+uLeqjaZjxuX\n5ek3Ck1HHOgBmyC8D3uDcqj/HoK9idi9JP7KZZWJGVTb+u9PAP+TXV8D2B7YCduGvGUDmbZ2Wd6d\n8XWGeNJQOmnSOhnvrJmc04A5GXekN+rZ2ILWKGwwWMM7U/FG7tv+vyRlmJilNcTzuBn4lKd5oF+b\ngsoM6oYAAB41SURBVD2008XDFbCHdtlkdnwhK27IJNcewhZfvuFpFDsmTseM5XdgWxi/iQ0OjwLX\nZ2ms4/IVK9M74gYt9jZwTomsT5BNcLCHyGL87UcqK7WFgkPoOgiejj2MyuS8E9gFM/KK3XuTqQ2m\nb8jp/78CvLNFXT/eBj3viz209inR9Wivv3QBZH1soeCeElknJLr+cUk5clmPw958fgjb0fZrbAfa\nXHwHWibrVOBv/v/+wM3J9Rexh0cu6zzgtpL6ewX/3DeV1evvyZL6Ow4zDnNZizesed2lun5DVuwh\nVden/drSEu5UbAx6jKyveCjeWqftciz+sC3rJ5ihkLbLf3qdt0PPE6mNP79qk64PyHT9akX95Q/6\nwjB9kNr4nddTwafj7xtGOtaPV0nSL4yVJ7P6mJimm/xeBhxD/YTiMbouODRt7NHVeH9jkuP/dzEy\nqTdKZ5AYpcl9izxeLueR2DkNRbw+m/y4rFNKZK3S8/uws0/eGHP6StfYeDKf5ZzMlrSfZia0rU6i\nqnTdrknuKmQTkOXQdW9PclcBXknkSfvKRMonwIuwZ0RH9GmPU/UipmrC/Ea7pGs/2YXyRZZxwEYl\n/SSdwHU78S5kpbUFjUda1Okk7PnbrD7vpdzOngA8nnHbYW11Afay+07Xz0xsF9Z1wF+xsaDQdZdx\nL0lrmNffT7CX7ndgO5t38/TKFuHHURsnRiT8EC/zJzF74VBPYzsvx8UeHsEWGV8iO9bD07kQW/w+\nHNt1U+zKHup1OQ57EX+4h5M8rdOzdDZ2flGJrC9h7XIS1pcKvW2PLajmsj7j9ZzL9MYL5Wb0k4+n\n2X03uN5yPdznujwM+ICHw7D+fEWFfuaU8MXGhg9guxeLRd1TvC7yOl2A2d9leijsylzXF2E76q/F\ndt5fgn1OuQC4skSmhX5/3l7HFHlk+hzv+srzrapTwXbmnkPtDOgN/ffN2Lj0JWxcvAo7M/pJ18WG\nmM14VdIXi80DByZ5XI29AMjrdSE2n8vL9mmsjeX5LgB+UVKGd3k6xUu5J6i98K3K+yy/J28zi4Bz\ns/Q/go2HcyvGnyMrxo06vlFoOmKEvgvYhKhdk9a59N6k9VB8AM1kOhP4fgm/L8k2Uk/rPmxCmRsi\nxVuiv+Cfl2VpbY8NFjdi5x78xjt38d3yPP/9kP9+AH9zm6VzAfDtEn5zfEUam1z/g65G9FHYW7Vn\nvC6nYoZe2ecPH/JrxScMuzq/I7YAMt+vzaD2ffKRJen8g/LFgKuwAWekh/Wd38DlyuW8lNp35bv7\n/TOwh1fxrfQsals/h7se3tUTXS+Pnv3aDiW6fs7l/yO2sPoc9uCY5mmNKEnnNOAzJfwW1LbJviGr\n/78X9qAt3nDfgD2Ay86J2A7b3bLQ286WSf2dhD1Yclnvxz+ZzNK6lMToTfjfYIt8ef1t4HWUy3oa\nNp7k/eRhbwvPZ7KeBJxRoYfRwL4l/FewHaBd+grWFtM310W7/Bdwe0k6H8YM+7xdzvff07zellvP\n3eh6bBO63r6Bru/GjOX8Qf8CcF5Jm1mCHZgKXY2YhX5PbqzshY35p2FGxb3YboZ51D7ZTY3VqV6P\nuUGyEmZ0f6CkfGOo303WyNh7CTO8c+O9apJzETZO50bpaKx/5IZvnZGUpLWE2mSuLyc/51Bu0FXp\neQZdF836Utf/IjnHMsm3pclsImsrE9qqSdStXuamdE37JrljsAOJy9pSla6/T/2E7GR6f5I7BhtP\nyiZwD3k5c1nvqtB1f/Xp33t7KhsTqybM6cuEA7Nrk6lfZNkLey7m/eQF4MSS9Ksm3ithbbqVBY3X\nsQX9VhYu7mlBn9tT/vnV4ZTbtJsA5/vvjbDdkDOxz07TsIbH+QHwtYr+sCbwHfzsU28b17tO31ES\nfxd8ETDjNwM+57+HAj+j9kJ2CPbJ7vHYXOUw4CsV8qyEnf92FraAOsT5VbGvPbZyWX/ncU7GJvVl\nX6Xshe/4y/jdXJZNPRQT+/W8/Lms61XIdCRZ2y3Rz4aFfvz/urZaoYdDXA/neDv4X2yMvd5/H012\n7luRPiVns2E7Ys/0+09P2sYwbAdhXqfbNdJDku4busZ2d37W01vR6/ksbGxbrUSmvL0W5+AeSrYB\nJClD8Sn2akm+dc/05J63YIvaN1FbIL7Jy7YVZrNf43V7LrZL6EqP90dqR98cQfmZaTtg43Ferx/3\nfPOyrY/1xzzfqxqU4cgsFOPPlthzv0ynJ5S0mfOAPUvS/yVwa1lbqpKp1fCm8Jo30OCn8Z+MGQZv\ndfpp7KH6E1X9Sxb/UmxxY4uM/w22onmlU+eoeezbADvLZCHWWFfEFqHGYG9tl2EN9atYw34Ge9u1\nIdYBv6SqD/lp/IVBcL8mngBE5CvYYkbO7+f8Rtg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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff295b75610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#barplot looking at last month average daily tracks by track plays\n",
    "#there are fewer listeners listening to more tracks\n",
    "\n",
    "y=lc['total']\n",
    "x=lc.index\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "ax.set_xticklabels(x, rotation=90)\n",
    "sns.barplot(x,y, data=lc).set_title(\"Number of Track Plays by Last Month Average Daily Tracks\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinregressResult(slope=-46.3504861206202, intercept=11197.119383633788, rvalue=-0.6464540875081802, pvalue=6.448961493269051e-23, stderr=4.077350794525077)\n"
     ]
    },
    {
     "data": {
      "image/png": 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t6OEjUkdbGDOFJYRdnVKhoI4//O11fRkAAAAAAJwMCKpOMlYohP5U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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff277ce03d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#simple linear regression visualizing the relationship \n",
    "#between total tracks listened by last month average daily tracks\n",
    "\n",
    "y=lc2['total']\n",
    "x=lc2['last_month_avg_daily_tracks']\n",
    "\n",
    "#line regress in SciPy\n",
    "\n",
    "print(linregress(x, y))\n",
    "\n",
    "#visualizing the regression of these 2 variables\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "sns.regplot(x,y, data=lc2).set_title(\"Number of Tracks by Last Month Average Daily Tracks\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is a moderate to strong negative correlation between listened tracks and last month average daily tracks (-0.65) that is also statistically significant due to the p-value (6.44^10-23).  The slope is dramatic (-46.35) suggesting a fast drop off where fewer tracks are played in the context of more listening last month and more tracks are played in the context of having not listened to many tracks last month. Conversely, as last month's average daily tracks decreases, listening increases. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Previous Month Average Daily Tracks vs Skips in Conclusion:\n",
    "Fewer tracks listened to every day last month indicates a skipped rate of around 40%. But as users listen to more recommended tracks, there is more variance in skip percent and skip percent increases. Why do users that listen more recommended tracks skip more and exhibit more diverse behaviours therein?\n",
    "\n",
    "Also, why do a lot of users that listen heavily one month drop off the next? In the second chart, we're seeing that most recommended tracks are played in the context of fewer tracks listened to last month. But by pivoting the \"last month average tracks\" column, we're pivoting an average which can be problematic, but it works for illustrative purposes here. I'd probably do a deeper investigation of this metric with the raw data. We can note that the majority of users are skipping 40% of the tracks and have listened to between zero and less than 100 tracks last month. Both of these effects are statistically significant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding Client Version vs Skips"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>listened</th>\n",
       "      <th>skipped</th>\n",
       "      <th>skipped%</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>client_version</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
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       "      <td>1356</td>\n",
       "      <td>52.27</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
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       "      <td>1058</td>\n",
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       "      <td>87.10</td>\n",
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       "    <tr>\n",
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       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>20.00</td>\n",
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       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "      <td>30.00</td>\n",
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       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>83</td>\n",
       "      <td>68</td>\n",
       "      <td>15</td>\n",
       "      <td>18.07</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>111</td>\n",
       "      <td>66</td>\n",
       "      <td>45</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
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       "      <td>117</td>\n",
       "      <td>90</td>\n",
       "      <td>43.48</td>\n",
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       "    <tr>\n",
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       "      <td>106</td>\n",
       "      <td>44</td>\n",
       "      <td>62</td>\n",
       "      <td>58.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>124</td>\n",
       "      <td>81</td>\n",
       "      <td>43</td>\n",
       "      <td>34.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>575</td>\n",
       "      <td>434</td>\n",
       "      <td>141</td>\n",
       "      <td>24.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>85</td>\n",
       "      <td>48</td>\n",
       "      <td>37</td>\n",
       "      <td>43.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>519</td>\n",
       "      <td>408</td>\n",
       "      <td>111</td>\n",
       "      <td>21.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>598</td>\n",
       "      <td>398</td>\n",
       "      <td>200</td>\n",
       "      <td>33.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>5686</td>\n",
       "      <td>3998</td>\n",
       "      <td>1688</td>\n",
       "      <td>29.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>705</td>\n",
       "      <td>380</td>\n",
       "      <td>325</td>\n",
       "      <td>46.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>996</td>\n",
       "      <td>782</td>\n",
       "      <td>214</td>\n",
       "      <td>21.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>292</td>\n",
       "      <td>155</td>\n",
       "      <td>137</td>\n",
       "      <td>46.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2975</td>\n",
       "      <td>2058</td>\n",
       "      <td>917</td>\n",
       "      <td>30.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>2231</td>\n",
       "      <td>963</td>\n",
       "      <td>1268</td>\n",
       "      <td>56.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>2488</td>\n",
       "      <td>1758</td>\n",
       "      <td>730</td>\n",
       "      <td>29.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2842</td>\n",
       "      <td>1653</td>\n",
       "      <td>1189</td>\n",
       "      <td>41.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>28147</td>\n",
       "      <td>16682</td>\n",
       "      <td>11465</td>\n",
       "      <td>40.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>76897</td>\n",
       "      <td>48790</td>\n",
       "      <td>28107</td>\n",
       "      <td>36.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>2136</td>\n",
       "      <td>1382</td>\n",
       "      <td>754</td>\n",
       "      <td>35.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>20838</td>\n",
       "      <td>11733</td>\n",
       "      <td>9105</td>\n",
       "      <td>43.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>13906</td>\n",
       "      <td>9510</td>\n",
       "      <td>4396</td>\n",
       "      <td>31.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>536990</td>\n",
       "      <td>309331</td>\n",
       "      <td>227659</td>\n",
       "      <td>42.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>12982</td>\n",
       "      <td>6952</td>\n",
       "      <td>6030</td>\n",
       "      <td>46.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>726</td>\n",
       "      <td>346</td>\n",
       "      <td>380</td>\n",
       "      <td>52.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>364</td>\n",
       "      <td>181</td>\n",
       "      <td>183</td>\n",
       "      <td>50.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2400</th>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>25.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2404</th>\n",
       "      <td>173</td>\n",
       "      <td>78</td>\n",
       "      <td>95</td>\n",
       "      <td>54.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 total  listened  skipped  skipped%\n",
       "client_version                                     \n",
       "0                  197        90      107     54.31\n",
       "16                 249       200       49     19.68\n",
       "20                 227       108      119     52.42\n",
       "24                2594      1238     1356     52.27\n",
       "28                2044       986     1058     51.76\n",
       "32               10177      5530     4647     45.66\n",
       "36               54237     22634    31603     58.27\n",
       "40                1053       698      355     33.71\n",
       "44               24475     16103     8372     34.21\n",
       "64                  21         0       21    100.00\n",
       "84                  93        12       81     87.10\n",
       "88                  20        16        4     20.00\n",
       "92                  20        14        6     30.00\n",
       "96                  83        68       15     18.07\n",
       "100                111        66       45     40.54\n",
       "104                207       117       90     43.48\n",
       "108                  4         1        3     75.00\n",
       "112                 16         4       12     75.00\n",
       "116                 50        14       36     72.00\n",
       "120                 66         4       62     93.94\n",
       "124                671       498      173     25.78\n",
       "128                106        44       62     58.49\n",
       "132                124        81       43     34.68\n",
       "136                575       434      141     24.52\n",
       "140                 85        48       37     43.53\n",
       "144                519       408      111     21.39\n",
       "148                598       398      200     33.44\n",
       "152               5686      3998     1688     29.69\n",
       "156                705       380      325     46.10\n",
       "160                996       782      214     21.49\n",
       "164                292       155      137     46.92\n",
       "168               2975      2058      917     30.82\n",
       "172               2231       963     1268     56.84\n",
       "176               2488      1758      730     29.34\n",
       "180               2842      1653     1189     41.84\n",
       "184              28147     16682    11465     40.73\n",
       "188              76897     48790    28107     36.55\n",
       "192               2136      1382      754     35.30\n",
       "196              20838     11733     9105     43.69\n",
       "200              13906      9510     4396     31.61\n",
       "204             536990    309331   227659     42.40\n",
       "208              12982      6952     6030     46.45\n",
       "212                726       346      380     52.34\n",
       "216                364       181      183     50.27\n",
       "300                  1         0        1    100.00\n",
       "2400                 8         6        2     25.00\n",
       "2404               173        78       95     54.91"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#making pivot table of binned client version to skipped tracks\n",
    "\n",
    "lc = pd.pivot_table(df_orig, values='skipped', columns='client_version', aggfunc=('count', 'sum')).T\n",
    "lc['skipped'] = lc['count'] - lc['sum']\n",
    "lc.columns = ['total', 'listened', 'skipped']\n",
    "\n",
    "#seeing a lot of random client versions, so trying to eliminate ones that aren't used much\n",
    "\n",
    "lc = lc.reset_index()\n",
    "\n",
    "#stripping the value at the decimal point\n",
    "\n",
    "sep = '.'\n",
    "lc.client_version = [s.split(sep, 1)[0] for s in lc.client_version]\n",
    "\n",
    "#converting to integer\n",
    "\n",
    "lc.client_version = lc.client_version.astype(int)\n",
    "\n",
    "#grouping by integer\n",
    "\n",
    "lc = lc.groupby(by ='client_version').sum()\n",
    "\n",
    "#looking at skip to total songs ratio\n",
    "\n",
    "lc['skipped%'] = (lc.skipped/lc.total)*100\n",
    "lc['skipped%'] = lc['skipped%'].round(2)\n",
    "lc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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NqAQAAADAaEIlAAAAAEYTKgEAAAAw2qb1bgAAAAA2urP/6muT1brBE641WS1Y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\nVfWy/PR1+akV1k6Sq3T3KVs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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff27a4cad10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#barplot of binned client versions to % tracks skipped\n",
    "\n",
    "y=lc['skipped%']\n",
    "x=lc.index\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(20,8))\n",
    "ax.set_xticklabels(x, rotation=90)\n",
    "sns.barplot(x,y, data=lc).set_title(\"Skipped Percent by Client Version\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm not seeing a clear relationship of client version to skip ratio. I'd need a better understanding of how best to bin the many client versions, if I were to investigate this further."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "Overall, the biggest predictor of skipping a track is the duration of the track--Noise Nimbus users are also more likely to skip longer tracks than shorter tracks. This should be broken down further into skip rates for longer mixes as opposed to shorter songs. I'd expect mixes have a higher skip rate than tracks, because they are longer and often have more diverse content on them.\n",
    "\n",
    "Other predictors of skipping are the average number of tracks listened to last month. With fewer average tracks listened to last month, the skip rates hover around 40%. Skip rates demonstrate more variance and increase when the average number of tracks listened to last month increases. This should be investigated. Why does behaviour diverge in heavy users? Why do a lot of users drop off in engagement after listening heavily one month? What could make NoiseNimbus more sticky, and user engagement more consistent?\n",
    "\n",
    "Another predictor of skipping is the listening context. Tracks has the highest skip rate and also the highest number of plays. The other listening contexts have much lower skip rates and overall lower numbers of plays.\n",
    "\n",
    "There is probably a relationship between client version and skipping tracks but I don't have the context to understand it without a more comprehensive understanding of how I could best bin client versions to see that pattern.\n",
    "\n",
    "To decrease skip rate, the best things NoiseNimbus can do is find ways to increase user engagement in listening contexts that aren't tracks. They have a much lower skip rate as well as lower user engagement. I believe that in finding ways of increasing user engagement on those listening contexts, we could lower overall skip rates.\n",
    "\n",
    "I'm also not understanding why some users that listen to more recommended tracks per day have more divergent behaviour regarding track skipping and skip more tracks in general. Then I'd want to model that behaviour--with user churn and/or decreased activity as the target variable--to see what leads to churn and/or decreased behaviour. I'd also study power users to better understand how they use the site and what can lead to more user enagagement. \n",
    "\n",
    "The users that listen a lot one month and listen to a lot of recommended tracks the next month should also be researched to understand how they are using NoiseNimbus differently and if we can use that understanding to influence other user behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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