spotify_multivariate_label_creation_tf_ubf_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dat = pd.read_csv(r'F:\\AppliedDataScience\\data\\dat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "   Unnamed: 0                              session_id  session_position  \\\n",
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       "\n",
       "   session_length                                track_id  skip_1  skip_2  \\\n",
       "0              20  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   \n",
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       "2              20  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   \n",
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       "4              12  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   \n",
       "\n",
       "   skip_3  not_skipped  context_switch       ...          valence  \\\n",
       "0   False         True               0       ...         0.152255   \n",
       "1    True        False               0       ...         0.152255   \n",
       "2   False         True               0       ...         0.152255   \n",
       "3   False         True               0       ...         0.152255   \n",
       "4    True        False               0       ...         0.152255   \n",
       "\n",
       "   acoustic_vector_0  acoustic_vector_1  acoustic_vector_2  acoustic_vector_3  \\\n",
       "0          -0.815775           0.386409            0.23016           0.028028   \n",
       "1          -0.815775           0.386409            0.23016           0.028028   \n",
       "2          -0.815775           0.386409            0.23016           0.028028   \n",
       "3          -0.815775           0.386409            0.23016           0.028028   \n",
       "4          -0.815775           0.386409            0.23016           0.028028   \n",
       "\n",
       "   acoustic_vector_4  acoustic_vector_5 acoustic_vector_6  acoustic_vector_7  \\\n",
       "0          -0.333373           0.015452          -0.35359           0.205826   \n",
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       "4          -0.333373           0.015452          -0.35359           0.205826   \n",
       "\n",
       "  predicted_output  \n",
       "0                1  \n",
       "1                0  \n",
       "2                1  \n",
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       "4                0  \n",
       "\n",
       "[5 rows x 53 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'session_id', 'session_position', 'session_length',\n",
       "       'track_id', 'skip_1', 'skip_2', 'skip_3', 'not_skipped',\n",
       "       'context_switch', 'no_pause_before_play', 'short_pause_before_play',\n",
       "       'long_pause_before_play', 'hist_user_behavior_n_seekfwd',\n",
       "       'hist_user_behavior_n_seekback', 'hist_user_behavior_is_shuffle',\n",
       "       'hour_of_day', 'date', 'premium', 'context_type',\n",
       "       'hist_user_behavior_reason_start', 'hist_user_behavior_reason_end',\n",
       "       'label', 'duration', 'release_year', 'us_popularity_estimate',\n",
       "       'acousticness', 'beat_strength', 'bounciness', 'danceability',\n",
       "       'dyn_range_mean', 'energy', 'flatness', 'instrumentalness', 'key',\n",
       "       'liveness', 'loudness', 'mechanism', 'mode', 'organism', 'speechiness',\n",
       "       'tempo', 'time_signature', 'valence', 'acoustic_vector_0',\n",
       "       'acoustic_vector_1', 'acoustic_vector_2', 'acoustic_vector_3',\n",
       "       'acoustic_vector_4', 'acoustic_vector_5', 'acoustic_vector_6',\n",
       "       'acoustic_vector_7', 'predicted_output'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "dat.columns"
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  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = dat[['session_id','track_id', 'skip_1', 'skip_2', 'skip_3', 'not_skipped','context_switch']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "                               session_id  \\\n",
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       "9  0_03373b7c-3c30-4bac-a909-1a4ecc310797   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   False   \n",
       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False    True   \n",
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       "9  t_0479f24c-27d2-46d6-a00c-7ec928f2b539   False   False   False   \n",
       "\n",
       "   not_skipped  context_switch  \n",
       "0         True               0  \n",
       "1        False               0  \n",
       "2         True               0  \n",
       "3         True               0  \n",
       "4        False               0  \n",
       "5        False               0  \n",
       "6        False               0  \n",
       "7        False               0  \n",
       "8        False               0  \n",
       "9        False               1  "
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  if sys.path[0] == '':\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  del sys.path[0]\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "skip_1 = df['skip_1'].replace({False: 0,True: -1})\n",
    "skip_1 = pd.DataFrame(skip_1)\n",
    "skip_2 = df['skip_2'].replace({False: 0,True: -0.5})\n",
    "skip_2 = pd.DataFrame(skip_2)\n",
    "skip_3 = df['skip_3'].replace({False: 0,True: 0.2})\n",
    "skip_3 = pd.DataFrame(skip_3)\n",
    "not_skipped = df['not_skipped'].replace({False: 0,True: 1})\n",
    "not_skipped = pd.DataFrame(not_skipped)\n",
    "\n",
    "### replace columns###\n",
    "\n",
    "df['skip_1']=skip_1\n",
    "df['skip_2']=skip_2\n",
    "df['skip_3']=skip_3\n",
    "df['not_skipped']=not_skipped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "                                session_id  \\\n",
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       "\n",
       "                                  track_id  skip_1  skip_2  skip_3  \\\n",
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       "14  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "\n",
       "    not_skipped  context_switch  \n",
       "0             1               0  \n",
       "1             0               0  \n",
       "2             1               0  \n",
       "3             1               0  \n",
       "4             0               0  \n",
       "5             0               0  \n",
       "6             0               0  \n",
       "7             0               0  \n",
       "8             0               0  \n",
       "9             0               1  \n",
       "10            0               0  \n",
       "11            0               1  \n",
       "12            1               0  \n",
       "13            0               0  \n",
       "14            0               0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167880"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_score2 = (df['skip_1']/len(df) + df['skip_2']/len(df)+ df['skip_3']/len(df)+ df['not_skipped']/len(df))\n",
    "skip_score2 = pd.DataFrame(skip_score2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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     "metadata": {},
     "output_type": "execute_result"
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    "skip_score2.head(12)"
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  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_score = df['skip_1'] + df['skip_2']+ df['skip_3']+ df['not_skipped']\n",
    "skip_score = pd.DataFrame(skip_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0  1.0\n",
       "1  0.2\n",
       "2  1.0\n",
       "3  1.0\n",
       "4  0.2\n",
       "5 -1.3\n",
       "6 -1.3\n",
       "7  0.0\n",
       "8 -1.3\n",
       "9  0.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skip_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "behavior = dat[['hist_user_behavior_reason_start','hist_user_behavior_reason_end','hist_user_behavior_is_shuffle']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>False</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hist_user_behavior_reason_start hist_user_behavior_reason_end  \\\n",
       "0                        trackdone                     trackdone   \n",
       "1                           fwdbtn                       endplay   \n",
       "2                        trackdone                     trackdone   \n",
       "3                           fwdbtn                     trackdone   \n",
       "4                        trackdone                        fwdbtn   \n",
       "5                           fwdbtn                        fwdbtn   \n",
       "6                           fwdbtn                        fwdbtn   \n",
       "7                        trackdone                        fwdbtn   \n",
       "8                           fwdbtn                        fwdbtn   \n",
       "9                          appload                        fwdbtn   \n",
       "10                          fwdbtn                        fwdbtn   \n",
       "11                         playbtn                        fwdbtn   \n",
       "12                       trackdone                     trackdone   \n",
       "13                       trackdone                       endplay   \n",
       "14                       trackdone                        fwdbtn   \n",
       "\n",
       "    hist_user_behavior_is_shuffle  \n",
       "0                            True  \n",
       "1                           False  \n",
       "2                           False  \n",
       "3                           False  \n",
       "4                            True  \n",
       "5                           False  \n",
       "6                           False  \n",
       "7                           False  \n",
       "8                           False  \n",
       "9                            True  \n",
       "10                          False  \n",
       "11                          False  \n",
       "12                          False  \n",
       "13                          False  \n",
       "14                          False  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['trackdone', 'fwdbtn', 'appload', 'playbtn', 'clickrow', 'backbtn',\n",
       "       'remote', 'endplay', 'trackerror'], dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.hist_user_behavior_reason_start.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['trackdone', 'endplay', 'fwdbtn', 'backbtn', 'remote', 'logout',\n",
       "       'clickrow'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.hist_user_behavior_reason_end.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:16: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  app.launch_new_instance()\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:18: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n"
     ]
    }
   ],
   "source": [
    "start = behavior['hist_user_behavior_reason_start'].replace({'trackdone': 1,'remote': 0,'backbtn':0.9,'clickrow':0.2,\n",
    "                                                                'appload':0.2,'endplay':0.4,'playbtn':0.5,'trackerror':0,\n",
    "                                                                'fwdbtn':0.3})\n",
    "start = pd.DataFrame(start)\n",
    "\n",
    "\n",
    "\n",
    "end = behavior['hist_user_behavior_reason_end'].replace({'trackdone': 1,'endplay':0.4,'fwdbtn':0.3,'backbtn':0.9,'remote':0,\n",
    "                                                            'logout':0,'clickrow':0.2})\n",
    "end = pd.DataFrame(end)\n",
    "\n",
    "\n",
    "shuffle = behavior['hist_user_behavior_is_shuffle'].replace({True:1,False:0})\n",
    "shuffle = pd.DataFrame(shuffle)\n",
    "\n",
    "behavior['hist_user_behavior_reason_start'] = start\n",
    "behavior['hist_user_behavior_reason_end'] = end\n",
    "behavior['hist_user_behavior_is_shuffle'] = shuffle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "    hist_user_behavior_reason_start  hist_user_behavior_reason_end  \\\n",
       "0                               1.0                            1.0   \n",
       "1                               0.3                            0.4   \n",
       "2                               1.0                            1.0   \n",
       "3                               0.3                            1.0   \n",
       "4                               1.0                            0.3   \n",
       "5                               0.3                            0.3   \n",
       "6                               0.3                            0.3   \n",
       "7                               1.0                            0.3   \n",
       "8                               0.3                            0.3   \n",
       "9                               0.2                            0.3   \n",
       "10                              0.3                            0.3   \n",
       "11                              0.5                            0.3   \n",
       "12                              1.0                            1.0   \n",
       "13                              1.0                            0.4   \n",
       "14                              1.0                            0.3   \n",
       "\n",
       "    hist_user_behavior_is_shuffle  \n",
       "0                               1  \n",
       "1                               0  \n",
       "2                               0  \n",
       "3                               0  \n",
       "4                               1  \n",
       "5                               0  \n",
       "6                               0  \n",
       "7                               0  \n",
       "8                               0  \n",
       "9                               1  \n",
       "10                              0  \n",
       "11                              0  \n",
       "12                              0  \n",
       "13                              0  \n",
       "14                              0  "
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     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "behavior.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167880"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(behavior)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "           0\n",
       "0   0.000018\n",
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     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking3 = ((behavior['hist_user_behavior_reason_start'] + behavior['hist_user_behavior_reason_end']) + \n",
    "          behavior['hist_user_behavior_is_shuffle'])/len(behavior)\n",
    "liking3 = pd.DataFrame(liking3)\n",
    "liking3.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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jTyqljptuvFazbAdwIavOsFmWr7wdmNNaJ1eUL2vL/HzerH8ZSqn7lFJ9Sqm+ycnJEv6k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\nthewC2sNjJN/L8Cu9nq8bsV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9ZT5Xy8aVdfx5YAg4CRwHPpB1bs8BiaxzdUMZz5XO+h8+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\n1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(liking3,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "liking1 = ((behavior['hist_user_behavior_reason_start'] * behavior['hist_user_behavior_reason_end']) + \n",
    "          behavior['hist_user_behavior_is_shuffle'])\n",
    "liking1 = pd.DataFrame(liking1)\n",
    "\n",
    "liking2 = ((behavior['hist_user_behavior_reason_start'] + behavior['hist_user_behavior_reason_end']) + \n",
    "          behavior['hist_user_behavior_is_shuffle'])\n",
    "liking2 = pd.DataFrame(liking2)"
   ]
  },
  {
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0  3.0\n",
       "1  0.7\n",
       "2  2.0\n",
       "3  1.3\n",
       "4  2.3"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0\n",
       "0   2.00\n",
       "1   0.12\n",
       "2   1.00\n",
       "3   0.30\n",
       "4   1.30\n",
       "5   0.09\n",
       "6   0.09\n",
       "7   0.30\n",
       "8   0.09\n",
       "9   1.06\n",
       "10  0.09\n",
       "11  0.15\n",
       "12  1.00\n",
       "13  0.40\n",
       "14  0.30"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking1.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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1Lcb2d3E7rNz7sT2EY0k+9Q8vG9byQxWyqquUOgD8P8AK/J3W+n8s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\nfxEmXEZntkMXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(liking1,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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cy9VR4q7RcCxF1pS/YczjdtES8DAXKT+gjuR70fd3ll8GaalGG2LlfEUFdxE5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\n1BakwV0ppbag/x+XgExBEbipEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(liking2,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_score['liking'] = liking2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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j4sAXSQXsY8AjxpgjInKfiNyaPu0/icgREXkV+E/AZ9P3HQS+TuqPxz7gvvQx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\nDfxKKbXCaOBXSqkV5v8HpZUSUymlLOIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(skip_score[0],bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "liking_array = liking2.values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167880, 1)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Found array with dim 3. the normalize function expected <= 2.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-0040b6b601e3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnormalized_X\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpreprocessing\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mliking_array\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py\u001b[0m in \u001b[0;36mnormalize\u001b[1;34m(X, norm, axis, copy, return_norm)\u001b[0m\n\u001b[0;32m   1410\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1411\u001b[0m     X = check_array(X, sparse_format, copy=copy,\n\u001b[1;32m-> 1412\u001b[1;33m                     estimator='the normalize function', dtype=FLOAT_DTYPES)\n\u001b[0m\u001b[0;32m   1413\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1414\u001b[0m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    449\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_nd\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m             raise ValueError(\"Found array with dim %d. %s expected <= 2.\"\n\u001b[1;32m--> 451\u001b[1;33m                              % (array.ndim, estimator_name))\n\u001b[0m\u001b[0;32m    452\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    453\u001b[0m             \u001b[0m_assert_all_finite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Found array with dim 3. the normalize function expected <= 2."
     ]
    }
   ],
   "source": [
    "normalized_X = preprocessing.normalize([liking_array])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(167880, 1)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3. ],\n",
       "       [0.7],\n",
       "       [2. ],\n",
       "       ...,\n",
       "       [3. ],\n",
       "       [2.3],\n",
       "       [1.6]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "liking_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zoopy = np.reshape(liking_array, (2, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3. , 0.7, 2. , ..., 2. , 2. , 1.9],\n",
       "       [2. , 3. , 0.6, ..., 3. , 2.3, 1.6]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 83940)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zoopy = zoopy.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3. , 0.7, 2. , ..., 2. , 2. , 1.9],\n",
       "       [2. , 3. , 0.6, ..., 3. , 2.3, 1.6]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3. , 2. ],\n",
       "       [0.7, 3. ],\n",
       "       [2. , 0.6],\n",
       "       ...,\n",
       "       [2. , 3. ],\n",
       "       [2. , 2.3],\n",
       "       [1.9, 1.6]])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zoopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "normalized_zoopy = preprocessing.normalize(zoopy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.83205029, 0.5547002 ],\n",
       "       [0.22722962, 0.97384121],\n",
       "       [0.95782629, 0.28734789],\n",
       "       ...,\n",
       "       [0.5547002 , 0.83205029],\n",
       "       [0.65617871, 0.75460552],\n",
       "       [0.7649112 , 0.64413575]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized_zoopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "like = normalized_zoopy.reshape(-1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "like = pd.DataFrame(like)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
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  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
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GMv7lN+2XXVg1xvBfnz5MYDzKF393B26HHY/LwWffs4Uj3cP8qLVzhme1tqfe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\nC9LkrpRSFqTJXSmlLOj/B+UjnX3/0aH5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(like,bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0  1.0\n",
       "1  0.2\n",
       "2  1.0\n",
       "3  1.0\n",
       "4  0.2\n",
       "5 -1.3\n",
       "6 -1.3\n",
       "7  0.0\n",
       "8 -1.3\n",
       "9  0.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "skip_score.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.832050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.554700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.227230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.973841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.957826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.832050\n",
       "1  0.554700\n",
       "2  0.227230\n",
       "3  0.973841\n",
       "4  0.957826"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "like.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_score['liking'] = liking2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "skip_score['normalized_liking'] = like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mul_label = skip_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>0</th>\n",
       "      <th>normalized_liking</th>\n",
       "      <th>liking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.832050</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.554700</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.227230</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.973841</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.957826</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0  normalized_liking  liking\n",
       "0  1.0           0.832050     3.0\n",
       "1  0.2           0.554700     0.7\n",
       "2  1.0           0.227230     2.0\n",
       "3  1.0           0.973841     1.3\n",
       "4  0.2           0.957826     2.3"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_label.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mul_label = mul_label.rename(columns = {0: \"skip_score\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>skip_score</th>\n",
       "      <th>normalized_liking</th>\n",
       "      <th>liking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>0.832050</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.554700</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.227230</td>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.973841</td>\n",
       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.957826</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.287348</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-1.3</td>\n",
       "      <td>0.585491</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.810679</td>\n",
       "      <td>1.3</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
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       "      <td>0.854199</td>\n",
       "      <td>0.6</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.519947</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   skip_score  normalized_liking  liking\n",
       "0         1.0           0.832050     3.0\n",
       "1         0.2           0.554700     0.7\n",
       "2         1.0           0.227230     2.0\n",
       "3         1.0           0.973841     1.3\n",
       "4         0.2           0.957826     2.3\n",
       "5        -1.3           0.287348     0.6\n",
       "6        -1.3           0.585491     0.6\n",
       "7         0.0           0.810679     1.3\n",
       "8        -1.3           0.854199     0.6\n",
       "9         0.0           0.519947     1.5"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mul_label.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'session_id', 'session_position', 'session_length',\n",
       "       'track_id', 'skip_1', 'skip_2', 'skip_3', 'not_skipped',\n",
       "       'context_switch', 'no_pause_before_play', 'short_pause_before_play',\n",
       "       'long_pause_before_play', 'hist_user_behavior_n_seekfwd',\n",
       "       'hist_user_behavior_n_seekback', 'hist_user_behavior_is_shuffle',\n",
       "       'hour_of_day', 'date', 'premium', 'context_type',\n",
       "       'hist_user_behavior_reason_start', 'hist_user_behavior_reason_end',\n",
       "       'label', 'duration', 'release_year', 'us_popularity_estimate',\n",
       "       'acousticness', 'beat_strength', 'bounciness', 'danceability',\n",
       "       'dyn_range_mean', 'energy', 'flatness', 'instrumentalness', 'key',\n",
       "       'liveness', 'loudness', 'mechanism', 'mode', 'organism', 'speechiness',\n",
       "       'tempo', 'time_signature', 'valence', 'acoustic_vector_0',\n",
       "       'acoustic_vector_1', 'acoustic_vector_2', 'acoustic_vector_3',\n",
       "       'acoustic_vector_4', 'acoustic_vector_5', 'acoustic_vector_6',\n",
       "       'acoustic_vector_7', 'predicted_output'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "track_features = dat[['acousticness', 'beat_strength', 'bounciness', 'danceability','energy', 'flatness']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frames = [df, behavior,track_features,mul_label]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = pd.concat(frames,axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>session_id</th>\n",
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       "      <th>hist_user_behavior_reason_end</th>\n",
       "      <th>hist_user_behavior_is_shuffle</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>beat_strength</th>\n",
       "      <th>bounciness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>flatness</th>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
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      "text/plain": [
       "                               session_id  \\\n",
       "0  0_00006f66-33e5-4de7-a324-2d18e439fc1e   \n",
       "1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
       "2  0_012b0fb4-0cc3-429f-9a78-cc6e622153fb   \n",
       "3  0_013cc010-c476-4ad2-8972-73449e0b2ef4   \n",
       "4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "2  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "3  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "\n",
       "   not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0            1               0                              1.0   \n",
       "1            0               0                              0.3   \n",
       "2            1               0                              1.0   \n",
       "3            1               0                              0.3   \n",
       "4            0               0                              1.0   \n",
       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  acousticness  \\\n",
       "0                            1.0                              1      0.015848   \n",
       "1                            0.4                              0      0.015848   \n",
       "2                            1.0                              0      0.015848   \n",
       "3                            1.0                              0      0.015848   \n",
       "4                            0.3                              1      0.015848   \n",
       "\n",
       "   beat_strength  bounciness  danceability    energy  flatness  skip_score  \\\n",
       "0       0.438551    0.473455      0.653119  0.553465  1.035007         1.0   \n",
       "1       0.438551    0.473455      0.653119  0.553465  1.035007         0.2   \n",
       "2       0.438551    0.473455      0.653119  0.553465  1.035007         1.0   \n",
       "3       0.438551    0.473455      0.653119  0.553465  1.035007         1.0   \n",
       "4       0.438551    0.473455      0.653119  0.553465  1.035007         0.2   \n",
       "\n",
       "   normalized_liking  liking  \n",
       "0           0.832050     3.0  \n",
       "1           0.554700     0.7  \n",
       "2           0.227230     2.0  \n",
       "3           0.973841     1.3  \n",
       "4           0.957826     2.3  "
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "X1 = result[['skip_1','skip_2','skip_3','context_switch','hist_user_behavior_reason_start','hist_user_behavior_reason_end',\n",
    "            \n",
    "            'hist_user_behavior_is_shuffle']]\n",
    "\n",
    "X2 = result[['acousticness', 'beat_strength', 'bounciness', 'danceability','energy', 'flatness']]\n",
    "y1 = result[['skip_score']]\n",
    "y2 = result[['liking']]\n",
    "y3 = result [['normalized_liking']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictind based on the track features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### skip_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "          0\n",
       "0 -0.246588\n",
       "1 -0.246588\n",
       "2 -0.246588\n",
       "3 -0.246588\n",
       "4 -0.246588"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_skip_score2 = sm.OLS(y1, X2).fit()\n",
    "predictions_skip_score_tf = model_skip_score2.predict(X2)\n",
    "predictions_skip_score_tf = pd.DataFrame(predictions_skip_score_tf)\n",
    "predictions_skip_score_tf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### liking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.474869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.474869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  1.474869\n",
       "1  1.474869\n",
       "2  1.474869\n",
       "3  1.474869\n",
       "4  1.474869"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_liking2 = sm.OLS(y2, X2).fit()\n",
    "predictions_liking_tf = model_liking2.predict(X2)\n",
    "predictions_liking_tf = pd.DataFrame(predictions_liking_tf)\n",
    "predictions_liking_tf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### normalized liking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.675468</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.675468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.675468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.675468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.675468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.675468\n",
       "1  0.675468\n",
       "2  0.675468\n",
       "3  0.675468\n",
       "4  0.675468"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_normalized_liking2 = sm.OLS(y3, X2).fit()\n",
    "predictions_normalized_liking_tf = model_normalized_liking2.predict(X2)\n",
    "predictions_normalized_liking_tf = pd.DataFrame(predictions_normalized_liking_tf)\n",
    "predictions_normalized_liking_tf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pout2 = predictions_skip_score_tf\n",
    "pout2['p_like_tf'] = predictions_liking_tf\n",
    "pout2['p_normalized_like_tf'] = predictions_normalized_liking_tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "pout2.rename(columns = {0:'p_skip_tf'}, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_skip_tf</th>\n",
       "      <th>p_like_tf</th>\n",
       "      <th>p_normalized_like_tf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_skip_tf  p_like_tf  p_normalized_like_tf\n",
       "0  -0.246588   1.474869              0.675468\n",
       "1  -0.246588   1.474869              0.675468\n",
       "2  -0.246588   1.474869              0.675468\n",
       "3  -0.246588   1.474869              0.675468\n",
       "4  -0.246588   1.474869              0.675468"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pout2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## predicting  based on the behavior features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model_skip_score = sm.OLS(y1, X1).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<statsmodels.regression.linear_model.RegressionResultsWrapper at 0x21c009275c0>"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_skip_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_skip_score_ubf = model_skip_score.predict(X1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions_skip_score_ubf = pd.DataFrame(predictions_skip_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.973240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.165435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.908970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.845195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.211694</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.973240\n",
       "1  0.165435\n",
       "2  0.908970\n",
       "3  0.845195\n",
       "4  0.211694"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictions_skip_score_ubf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### predict the liking based on user behavior features###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "0  3.0\n",
       "1  0.7\n",
       "2  2.0\n",
       "3  1.3\n",
       "4  2.3"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_liking = sm.OLS(y2, X1).fit()\n",
    "predictions_liking_ubf = model_liking.predict(X1)\n",
    "predictions_liking_ubf = pd.DataFrame(predictions_liking)\n",
    "predictions_liking_ubf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### predict the normalized liking based on user behavior features###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.685047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.634429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.634997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.573398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.691377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0\n",
       "0  0.685047\n",
       "1  0.634429\n",
       "2  0.634997\n",
       "3  0.573398\n",
       "4  0.691377"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_normalized_liking = sm.OLS(y3, X1).fit()\n",
    "predictions_normalized_liking_ubf = model_normalized_liking.predict(X1)\n",
    "predictions_normalized_liking_ubf = pd.DataFrame(predictions_normalized_liking_ubf)\n",
    "predictions_normalized_liking_ubf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pout = predictions_skip_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "pout['p_like_ubf'] = predictions_liking_ubf\n",
    "pout['p_normalized_like_ubf'] = predictions_normalized_liking_ubf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>p_like_ubf</th>\n",
       "      <th>p_normalized_like_ubf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.973240</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.685047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.165435</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.634429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.908970</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.634997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.845195</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.573398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.211694</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.691377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0  p_like_ubf  p_normalized_like_ubf\n",
       "0  0.973240         3.0               0.685047\n",
       "1  0.165435         0.7               0.634429\n",
       "2  0.908970         2.0               0.634997\n",
       "3  0.845195         1.3               0.573398\n",
       "4  0.211694         2.3               0.691377"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pout.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pout.rename(columns = {0:'p_skip_ubf'}, inplace = True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p_skip_ubf</th>\n",
       "      <th>p_like_ubf</th>\n",
       "      <th>p_normalized_like_ubf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.973240</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.685047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.165435</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.634429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.908970</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.634997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.845195</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.573398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.211694</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.691377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p_skip_ubf  p_like_ubf  p_normalized_like_ubf\n",
       "0    0.973240         3.0               0.685047\n",
       "1    0.165435         0.7               0.634429\n",
       "2    0.908970         2.0               0.634997\n",
       "3    0.845195         1.3               0.573398\n",
       "4    0.211694         2.3               0.691377"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pout.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "result_tf = result\n",
    "result_tf['p_skip_tf'] = pout2['p_skip_tf']\n",
    "result_tf['p_like_tf'] = pout2['p_like_tf']\n",
    "result_tf['p_normalized_tf'] = pout2['p_normalized_like_tf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
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       "<p>5 rows × 22 columns</p>\n",
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       "                               session_id  \\\n",
       "0  0_00006f66-33e5-4de7-a324-2d18e439fc1e   \n",
       "1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
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       "4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
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       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "\n",
       "   not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0            1               0                              1.0   \n",
       "1            0               0                              0.3   \n",
       "2            1               0                              1.0   \n",
       "3            1               0                              0.3   \n",
       "4            0               0                              1.0   \n",
       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  \\\n",
       "0                            1.0                              1   \n",
       "1                            0.4                              0   \n",
       "2                            1.0                              0   \n",
       "3                            1.0                              0   \n",
       "4                            0.3                              1   \n",
       "\n",
       "        ...         bounciness  danceability    energy  flatness  skip_score  \\\n",
       "0       ...           0.473455      0.653119  0.553465  1.035007         1.0   \n",
       "1       ...           0.473455      0.653119  0.553465  1.035007         0.2   \n",
       "2       ...           0.473455      0.653119  0.553465  1.035007         1.0   \n",
       "3       ...           0.473455      0.653119  0.553465  1.035007         1.0   \n",
       "4       ...           0.473455      0.653119  0.553465  1.035007         0.2   \n",
       "\n",
       "   normalized_liking  liking  p_skip_tf  p_like_tf  p_normalized_tf  \n",
       "0           0.832050     3.0  -0.246588   1.474869         0.675468  \n",
       "1           0.554700     0.7  -0.246588   1.474869         0.675468  \n",
       "2           0.227230     2.0  -0.246588   1.474869         0.675468  \n",
       "3           0.973841     1.3  -0.246588   1.474869         0.675468  \n",
       "4           0.957826     2.3  -0.246588   1.474869         0.675468  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_tf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "result['p_skip_ubf'] = pout['p_skip_ubf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result['p_like_ubf'] = pout['p_like_ubf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result['p_normalized_like_ubf'] = pout['p_normalized_like_ubf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "                               session_id  \\\n",
       "0  0_00006f66-33e5-4de7-a324-2d18e439fc1e   \n",
       "1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
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       "4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
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       "\n",
       "   not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0            1               0                              1.0   \n",
       "1            0               0                              0.3   \n",
       "2            1               0                              1.0   \n",
       "3            1               0                              0.3   \n",
       "4            0               0                              1.0   \n",
       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  \\\n",
       "0                            1.0                              1   \n",
       "1                            0.4                              0   \n",
       "2                            1.0                              0   \n",
       "3                            1.0                              0   \n",
       "4                            0.3                              1   \n",
       "\n",
       "           ...            flatness  skip_score  normalized_liking  liking  \\\n",
       "0          ...            1.035007         1.0           0.832050     3.0   \n",
       "1          ...            1.035007         0.2           0.554700     0.7   \n",
       "2          ...            1.035007         1.0           0.227230     2.0   \n",
       "3          ...            1.035007         1.0           0.973841     1.3   \n",
       "4          ...            1.035007         0.2           0.957826     2.3   \n",
       "\n",
       "   p_skip_tf  p_like_tf  p_normalized_tf  p_skip_ubf  p_like_ubf  \\\n",
       "0  -0.246588   1.474869         0.675468    0.973240         3.0   \n",
       "1  -0.246588   1.474869         0.675468    0.165435         0.7   \n",
       "2  -0.246588   1.474869         0.675468    0.908970         2.0   \n",
       "3  -0.246588   1.474869         0.675468    0.845195         1.3   \n",
       "4  -0.246588   1.474869         0.675468    0.211694         2.3   \n",
       "\n",
       "   p_normalized_like_ubf  \n",
       "0               0.685047  \n",
       "1               0.634429  \n",
       "2               0.634997  \n",
       "3               0.573398  \n",
       "4               0.691377  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
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yvtJHLGGY0IuqjjMRjrHjyBA3nbv4hdS5bjqnlVNj0xzqD+VgZLkzPhWltgDl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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(result['p_skip'],bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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VmvPNcm5qieZVZZCr9bSFdOauAHsz99uAU8aYM8aYZeAJ4KE11xigJft1KzDi3BDVaovZ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\ncr+InBCRUyLy6Q2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M2Gor7DYvnpkibSgquN+y++JmplsL3CNQTnZm7geBfSLSLyIB4GHg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\nUjXo/wcpaZKMRdWr5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(result['p_like'],bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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inzWu2DLCu195JXsOnua//MteSuUK/+HFlxJWHGNmv+5jZws8cPA0p6ZKlELn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\nEPuzUHL3NfMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.distplot(result['p_normalized_like'],bins = 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result.to_csv(r'F:\\AppliedDataScience\\data\\multivariate_result_total.csv')\n",
    "result.to_csv(r'F:\\AppliedDataScience\\data\\multivariate_result_only_tf.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### EDA OF resultant Data Frame####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>session_id</th>\n",
       "      <th>track_id</th>\n",
       "      <th>skip_1</th>\n",
       "      <th>skip_2</th>\n",
       "      <th>skip_3</th>\n",
       "      <th>not_skipped</th>\n",
       "      <th>context_switch</th>\n",
       "      <th>hist_user_behavior_reason_start</th>\n",
       "      <th>hist_user_behavior_reason_end</th>\n",
       "      <th>hist_user_behavior_is_shuffle</th>\n",
       "      <th>...</th>\n",
       "      <th>flatness</th>\n",
       "      <th>skip_score</th>\n",
       "      <th>normalized_liking</th>\n",
       "      <th>liking</th>\n",
       "      <th>p_skip_tf</th>\n",
       "      <th>p_like_tf</th>\n",
       "      <th>p_normalized_tf</th>\n",
       "      <th>p_skip_ubf</th>\n",
       "      <th>p_like_ubf</th>\n",
       "      <th>p_normalized_like_ubf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_00006f66-33e5-4de7-a324-2d18e439fc1e</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.832050</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "      <td>0.973240</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.685047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0_00079a23-1600-486a-91bd-5208be0c745a</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.554700</td>\n",
       "      <td>0.7</td>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "      <td>0.165435</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.634429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_012b0fb4-0cc3-429f-9a78-cc6e622153fb</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.227230</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "      <td>0.908970</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.634997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0_013cc010-c476-4ad2-8972-73449e0b2ef4</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.973841</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "      <td>0.845195</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.573398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_01a5f0dc-9938-48c9-92f1-c7e51f34d290</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.957826</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-0.246588</td>\n",
       "      <td>1.474869</td>\n",
       "      <td>0.675468</td>\n",
       "      <td>0.211694</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.691377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               session_id  \\\n",
       "0  0_00006f66-33e5-4de7-a324-2d18e439fc1e   \n",
       "1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
       "2  0_012b0fb4-0cc3-429f-9a78-cc6e622153fb   \n",
       "3  0_013cc010-c476-4ad2-8972-73449e0b2ef4   \n",
       "4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "2  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "3  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.0   \n",
       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539       0     0.0     0.2   \n",
       "\n",
       "   not_skipped  context_switch  hist_user_behavior_reason_start  \\\n",
       "0            1               0                              1.0   \n",
       "1            0               0                              0.3   \n",
       "2            1               0                              1.0   \n",
       "3            1               0                              0.3   \n",
       "4            0               0                              1.0   \n",
       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  \\\n",
       "0                            1.0                              1   \n",
       "1                            0.4                              0   \n",
       "2                            1.0                              0   \n",
       "3                            1.0                              0   \n",
       "4                            0.3                              1   \n",
       "\n",
       "           ...            flatness  skip_score  normalized_liking  liking  \\\n",
       "0          ...            1.035007         1.0           0.832050     3.0   \n",
       "1          ...            1.035007         0.2           0.554700     0.7   \n",
       "2          ...            1.035007         1.0           0.227230     2.0   \n",
       "3          ...            1.035007         1.0           0.973841     1.3   \n",
       "4          ...            1.035007         0.2           0.957826     2.3   \n",
       "\n",
       "   p_skip_tf  p_like_tf  p_normalized_tf  p_skip_ubf  p_like_ubf  \\\n",
       "0  -0.246588   1.474869         0.675468    0.973240         3.0   \n",
       "1  -0.246588   1.474869         0.675468    0.165435         0.7   \n",
       "2  -0.246588   1.474869         0.675468    0.908970         2.0   \n",
       "3  -0.246588   1.474869         0.675468    0.845195         1.3   \n",
       "4  -0.246588   1.474869         0.675468    0.211694         2.3   \n",
       "\n",
       "   p_normalized_like_ubf  \n",
       "0               0.685047  \n",
       "1               0.634429  \n",
       "2               0.634997  \n",
       "3               0.573398  \n",
       "4               0.691377  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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27R8gWQ+sB1i7di29Xm8/ShzOC7/6wrGd614XLt5llP7iYX8x3hOOyYs+/n123DXmk156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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "result.boxplot(column=['skip_1','skip_2','skip_3','not_skipped','context_switch'],patch_artist=True,vert=True,showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gh1 = dat.hist_user_behavior_reason_start.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dSY4Zmz86yW0LG1XVM5O8OslPtda+1ac8AIC1Y5J7rK5KclxVHVtVm5OcnuSi8QZV9YQk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\nANDJ/wdcsJDnuZzwawAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "result.boxplot(column=['hist_user_behavior_reason_start',\n",
    "                       'hist_user_behavior_reason_end',\n",
    "                        'hist_user_behavior_is_shuffle'],\n",
    "                        patch_artist='True',vert=True,showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KxPgtWrRITU1NKi8vV1NTkxYtWhR0SYBXq1evVmtr65Bx3traqtWrVwddGuAN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\n41YNBHwQ4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AB6IKAKADUQUA0IGoAgDoQFQBAHQgqgAAOhBVAAAdiCoAgA5EFQBAB6IKAKADUQUA0IGo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\nEFUAAB2IKgCADkQVAEAHogoAoIP/Dw5m1gIXzZg9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "result.boxplot(column=['skip_score',\n",
    "                       'liking',\n",
    "                        'normalized_liking'],\n",
    "               patch_artist='True',vert=True,showmeans=True)\n",
    "plt.show()\n",
    "plt.figure(figsize=(10, 7))\n",
    "result.boxplot(column=['p_skip_tf',\n",
    "                       'p_like_tf',\n",
    "                       'p_normalized_tf'],\n",
    "               patch_artist='True',vert=True,showmeans=True)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "result.boxplot(column=['p_skip_ubf',\n",
    "                       'p_like_ubf',\n",
    "                       'p_normalized_like_ubf'],\n",
    "               patch_artist='True',vert=True,showmeans=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "167880"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.DataFrame(dat['session_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.DataFrame(dat['session_id'].unique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}