predicted_user_behavior.ipynb

{
 "cells": [
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   "cell_type": "code",
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       "   Unnamed: 0                              session_id  \\\n",
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       "\n",
       "                                 track_id  skip_1  skip_2  skip_3  \\\n",
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       "\n",
       "   hist_user_behavior_reason_end  hist_user_behavior_is_shuffle  skip_score  \\\n",
       "0                            1.0                              1         1.0   \n",
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       "2                            1.0                              0         1.0   \n",
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       "4                            0.3                              1         0.2   \n",
       "\n",
       "   liking  rating  \n",
       "0     3.0     4.0  \n",
       "1     0.7     0.9  \n",
       "2     2.0     3.0  \n",
       "3     1.3     2.3  \n",
       "4     2.3     2.5  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy as sp\n",
    "\n",
    "df = pd.read_csv(r'F:\\AppliedDataScience\\data\\ratingdf_likescore.csv')\n",
    "print(len(df))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'session_id', 'track_id', 'skip_1', 'skip_2', 'skip_3',\n",
       "       'not_skipped', 'context_switch', 'hist_user_behavior_reason_start',\n",
       "       'hist_user_behavior_reason_end', 'hist_user_behavior_is_shuffle',\n",
       "       'skip_score', 'liking', 'rating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dat = pd.read_csv(r'F:\\AppliedDataScience\\data\\dat.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_df = df[['Unnamed: 0', 'session_id','track_id']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: 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",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: 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:3: 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",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: 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",
      "  after removing the cwd from sys.path.\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: 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:6: 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:7: 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",
      "  import sys\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: 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:9: 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 __name__ == '__main__':\n",
      "C:\\Users\\Ted\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: 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",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    }
   ],
   "source": [
    "new_df['acousticness'] = dat['acousticness']\n",
    "new_df['beat_strength'] = dat['beat_strength']\n",
    "new_df['bounciness'] = dat['bounciness']\n",
    "new_df['danceability'] = dat['danceability']\n",
    "new_df['dyn_range_mean'] = dat['dyn_range_mean']\n",
    "new_df['energy'] = dat['energy']\n",
    "new_df['flatness'] = dat['flatness']\n",
    "new_df['instrumentalness'] = dat['instrumentalness']\n",
    "new_df['key'] = dat['key']\n",
    "new_df['liveness'] = dat['liveness']\n",
    "new_df['loudness'] = dat['loudness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_df['hist_user_behavior_reason_start'] = df['hist_user_behavior_reason_start']\n",
    "new_df['hist_user_behavior_reason_end'] = df['hist_user_behavior_reason_end']\n",
    "new_df['hist_user_behavior_is_shuffle'] = df['hist_user_behavior_is_shuffle']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.678553</td>\n",
       "      <td>-6.577</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0_01a5f0dc-9938-48c9-92f1-c7e51f34d290</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</td>\n",
       "      <td>0.015848</td>\n",
       "      <td>0.438551</td>\n",
       "      <td>0.473455</td>\n",
       "      <td>0.653119</td>\n",
       "      <td>7.660024</td>\n",
       "      <td>0.553465</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>1</td>\n",
       "      <td>0.678553</td>\n",
       "      <td>-6.577</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                              session_id  \\\n",
       "0           0  0_00006f66-33e5-4de7-a324-2d18e439fc1e   \n",
       "1           1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
       "2           2  0_012b0fb4-0cc3-429f-9a78-cc6e622153fb   \n",
       "3           3  0_013cc010-c476-4ad2-8972-73449e0b2ef4   \n",
       "4           4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  acousticness  beat_strength  \\\n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "2  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "3  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "\n",
       "   bounciness  danceability  dyn_range_mean    energy  flatness  \\\n",
       "0    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "1    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "2    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "3    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "4    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "\n",
       "   instrumentalness  key  liveness  loudness  hist_user_behavior_reason_start  \\\n",
       "0          0.003484    1  0.678553    -6.577                              1.0   \n",
       "1          0.003484    1  0.678553    -6.577                              0.3   \n",
       "2          0.003484    1  0.678553    -6.577                              1.0   \n",
       "3          0.003484    1  0.678553    -6.577                              0.3   \n",
       "4          0.003484    1  0.678553    -6.577                              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  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_df[['acousticness','bounciness']]\n",
    "X = X.values\n",
    "\n",
    "y = new_df[['hist_user_behavior_reason_start','hist_user_behavior_reason_end','hist_user_behavior_is_shuffle']]\n",
    "y = y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01584839, 0.47345549],\n",
       "       [0.01584839, 0.47345549],\n",
       "       [0.01584839, 0.47345549],\n",
       "       ...,\n",
       "       [0.03320934, 0.743599  ],\n",
       "       [0.03320934, 0.743599  ],\n",
       "       [0.03320934, 0.743599  ]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1. , 1. , 1. ],\n",
       "       [0.3, 0.4, 0. ],\n",
       "       [1. , 1. , 0. ],\n",
       "       ...,\n",
       "       [0.2, 0.3, 0. ],\n",
       "       [1. , 0.3, 0. ],\n",
       "       [0.2, 0.4, 0. ]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "mlb = MultiLabelBinarizer()\n",
    "y=pd.DataFrame(mlb.fit_transform(df[['hist_user_behavior_reason_start','hist_user_behavior_reason_end','hist_user_behavior_is_shuffle']].values), \n",
    "               columns=mlb.classes_, index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0.0  0.2  0.3  0.4  0.5  0.9  1.0\n",
       "0    0    0    0    0    0    0    1\n",
       "1    1    0    1    1    0    0    0\n",
       "2    1    0    0    0    0    0    1\n",
       "3    1    0    1    0    0    0    1\n",
       "4    0    0    1    0    0    0    1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
    "from sklearn import metrics\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "knc = KNeighborsClassifier()\n",
    "knc.fit(X_train, y_train)\n",
    "y_pred = knc.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = pd.DataFrame(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
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       "   0  1  2  3  4  5  6\n",
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       "3  1  0  0  0  0  0  1\n",
       "4  1  0  0  0  0  0  0"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
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   ],
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    "y_pred.head()"
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  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>3886</th>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5437</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
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     "execution_count": 44,
     "metadata": {},
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    }
   ],
   "source": [
    "y_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred.rename(columns = {1:'0.2', 2:'0.3',3:'0.4',4:'0.5',5:'0.9',6:'1.0'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
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  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GXaGL3iOjRRU/7ROBEwGypv1x229vbCNpHunl46Lsk3h/fhG5BcnvcF5u9wXgecC/dPFv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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.heatmap(y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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H2443/37v2T3p1V18RmSJSg5AJZctDIZSKwZ1GOQufyGEKarkikHVOSLLD9k1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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###### Applying Decision Tree Regressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_df[['acousticness','bounciness']]\n",
    "X = X.values\n",
    "\n",
    "y = new_df[['hist_user_behavior_reason_start','hist_user_behavior_reason_end','hist_user_behavior_is_shuffle']]\n",
    "y = y.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.01584839, 0.47345549],\n",
       "       [0.01584839, 0.47345549],\n",
       "       [0.01584839, 0.47345549],\n",
       "       ...,\n",
       "       [0.03320934, 0.743599  ],\n",
       "       [0.03320934, 0.743599  ],\n",
       "       [0.03320934, 0.743599  ]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1. , 1. , 1. ],\n",
       "       [0.3, 0.4, 0. ],\n",
       "       [1. , 1. , 0. ],\n",
       "       ...,\n",
       "       [0.2, 0.3, 0. ],\n",
       "       [1. , 0.3, 0. ],\n",
       "       [0.2, 0.4, 0. ]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.536649</td>\n",
       "      <td>0.655676</td>\n",
       "      <td>0.255135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.532819</td>\n",
       "      <td>0.599853</td>\n",
       "      <td>0.328194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.543056</td>\n",
       "      <td>0.637037</td>\n",
       "      <td>0.336806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.532819</td>\n",
       "      <td>0.599853</td>\n",
       "      <td>0.328194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.543056</td>\n",
       "      <td>0.637037</td>\n",
       "      <td>0.336806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.536649  0.655676  0.255135\n",
       "1  0.532819  0.599853  0.328194\n",
       "2  0.543056  0.637037  0.336806\n",
       "3  0.532819  0.599853  0.328194\n",
       "4  0.543056  0.637037  0.336806"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "clf = DecisionTreeRegressor(max_depth=None, max_leaf_nodes=20)\n",
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "y_pred = pd.DataFrame(y_pred)\n",
    "y_pred.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Um9SatR4PqWV5PGqOxYON7c60LwZOI2yT8yZZCftRwJ+ADzX53EeB+4dy2LUVkdtvsD1/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\nRCKRcERy2olEIuGI5LQTiUTCEf8fJyuOoU0jsMEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(y_test)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eoDoLZMFErMXS3gG4LynPiqTfAn9vZhel5oziHd/+AcCjZvYIgJm9Vs1FCl7l\nLzrW5Zu9sdmh0PSeSIf8K+3HgfMTi/lt4GBCnDZJCvuxwJvAh5L52wEmaR4wCZjTS8GvIx1Kc+6U\nGRw1fosaxHQq0TphWKNFyD33t/lG5GBQq3skC+X7a4nTXiDpQuBOYCXwCCFeGzM7AzhD0ixCidaz\nk2t9kBBAvhq4S9KDZnZXibXXhdI8Pu0Q6/SQtKj4RmR8fCMyG+Td0sbMvg98H0DS/yPEJab5MaEv\n5NnJsd+a2bJk/lxgF2ADpZ1m2HDPYY9NcaW7R2KzzLPYM0EWqvfXGj3S08R3K+DjwF9Lmm5mzyZT\nDiX0ioQQ9nJqkvbeBexNqK1dFsvC80rG6fbfxeiMzYIJ5+S7CULCzakokC+Y2euSrpa0PeFJ4wWg\nJybxdUnfIgScGzDXzG6vdIG1XW6ixKZ9XAa+qRmn3R9mMkEWfltrdY/sVWLsE2XmX08I+6sad4/E\nx90j8XH3SDbIvdIeDN5c4fGtsel6tum/BpnnhHd5XfgskAXzpeK/VknXAIcAS81sp2RsAvATYGvg\neeBIM3s9OTYL+CzBp/8lM5uXjB8NfJ1wX5YAn+7ZlCzH+LGr+/1HOf1j8gey8FXNNv9y56RGizAk\n+F6N5+fFp/0D4HLgutTY6cBdZnaBpNOTz6clXRmOAnYkdLP5dVJTW4SWOzPMbFnSCOFkYHali3f7\nRmR0uhZ7TGVsptWhU7gTnyxEj1RMNzSze4DlvYYPA36YvP8h8LHU+BwzW2NmfwYWEtLclbxGJa3H\nxrBhiULHcZyG0o1V/WoUA3VmbmpmLwOY2cuSJifjmxOaU/bQAWxuZvdKOgl4jNCJ/VngC9VcyEP+\n4uMhf/HxkL9skIX/TfXegSrZqUFSO3AS8H5gEfCfwCzgvJKLpNLYz9rkPRwxZqs6i+mk8ZC/+HjI\nXzbIwv+mgSrtVyRNTazsqYQSrNB3p4b3AZjZcwCSbiT4wUuSTmNfMP1gy8bvX3bx0qzx8dKsg0Ot\ntUeyoGkGqrRvA44DLkj++7PU+I+TJJrNgOnA/cCmwAxJk5Lu7fsDC6q5ULHoVf5iY4Us2BfZ5oDi\n6EaL4FRBQc3/b6GakL8bgH2AiZI6CHVELgBulPRZQlf2IwCSrgw3ElrEFwhZkkVgiaRzgHskrSVk\nSh5f/z/HcRxn4DS/yq5CaZvZ0X0c2q+P+ecD55cYvxLod3ud5as9uSY2U1b7o3ts/jvs2zuRcfdI\nEzBh5NuNFiH3tI50n3ZsjtHURovgVEEjQ/mqpemV9shRXY0WIfdohO8bxOZ1rz2SCZpfZdc5jT0J\n7buaUCe7DbjOzL6ZnLMrISICor4AAA8dSURBVLtyI0Kjyy+bWcV71F3vTpvOBrSMbPrf7szzrLxz\nTRbIi3vkB1SZxk7YkBxuZu9J6mY/KekGM3se+A4h9vo+gtI+ELij0sWHj/LMj9gUlvk9js0hne2N\nFsGpgmIGbO1qNiLvkbR1r+HDCBElENLY7yYobSOkqrcRLOouYEUSyz3GzO4FkHQdIfW9otJe/Zb3\nL4zN8BWutGPzUru7oLJAXiztUvSVxn4TQaG/DIwEvmpmyyXtxvqtyDoIKe8l8YzIwcUzIuPjGZHZ\nwPJgafeTmYRCWZsB44HfSfo1faS397VIOiPynilH2HKvzhqVqV0e8hebOeuShp2YeMhf3/SVxv4p\n4JdmthZYKun3wG7A7wgp7T30pLdXZLMJKwYoolMt7VO8bGhsprWNbbQIThXkOeSvrzT2xcC+kq4n\nuEf2BL6dKPe3JO0J/BE4llA0qiKdvoETneLylY0WIffMLPgPYxZofpVd5zR24ArgWuBxgkvkWjN7\nNDl2Eu+E/N1BFZuQAK2tWXhgyTZeMCo+XjBqcKjVPVLIgNquaxq7ma3kHQXe+9gDwE79kg5obXOl\nHZvCG36PY7MLXjAqCwzFjUjHcZzMkgXzZaAZkUcQ+jvuAMxMrGiSeO4FwNPJ6feZ2YlJos1Pgb8i\nRJf83Mz6rKedpljw+NbYtI3zexybh3D3SBbIi6X9AzbMiHwc+Djw3RLznzOz95UYv8TMfiNpGHCX\npIPMrKJfu709C602s03LGN/sjc04eZJYFsiFpV0qI9LMFgCEHr2VMbPVwG+S912SHmL9EMA+KRS8\n0k5suld6JcXYjMa/x1mgWLkcUsOJ4dPeRtLDwArg38zsd+mDksYBHwUu62uBdEbkuVNmcNT4qvS7\nM0BaNnZLOzZv4U+MWSDPcdp98TKwlZm9llT1+x9JO5rZCoCkJskNwH+Y2aK+FklnRD4+7RDr7Kyz\nlM56eJx2fDxOOxtkwadd1x0oM1tjZq8l7x8EngO2S025CnjWzL5dz+s6juPUg+5+vBpFXZW2pEmS\nWpP30wiNfRcln88DxgJfqec1Hcdx6kU3VvWrGiQdKOlpSQuTMta9j/+DpEeT1x8kvbfSmgPNiFxO\nSEOfBNwu6U9m9hHgb4FvSCoQQvtOTKr8bQGcATwFPJRsYF5uZldXur73iIyPF4yKjxeMGhxqzYis\np3skMWCvAPYnVDadL+k2M3syNe3PwN5JE5mDCN6IPcqtW0tG5K0l5t4M3FxivIPSlf4qMmn0qoGc\n5vSD1pEepx2bCa1ufGSBOkePzAQW9uzfSZpDKF29Tmmb2R9S8++jiqi6ps+I9DT2+Hgae3w8jT0b\n9Cd6JB3llnBVEkTRw+bAi6nPHZS3oj9LFTWZml5pdxfdCoxN2wS/x7F5Vd4dKAv0x3xJR7n1QdV9\nBCR9iKC0P1jpugNNY7+YEGvdRYgQOcHM3pC0P6EC4LDk2L+a2f/2Wu82YFrPWpXIQKx79nFDOzov\nmicwZYE6h/x1AFumPpfsIyBpZ0JD9IN6ou/KMdA09juBWWZWkHQhMIvQI3IZ8FEzWyJpJ2AeqbZi\nkj4O9Cso2N0j8XH3SHzcPZIN6pxcMx+YLmkb4CXgKEKjmHVI2gq4BTjGzJ6pZtGBprH/KvXxPuDw\nZPzh1PgTwAhJw81sjaSNga8RfEA3ViMcuHtkMFDTO8myzyKtabQIThVYHR/tE6P2ZILx2gpcY2ZP\nSDoxOX4lcBawCfBfSVRdwcx2K7duPf65fgb4SYnxTwAPm1nPt/Vc4N+Bih0f0w7+2ZN25Mix3tjX\nyTbPFl5vtAhOFRTrnBFpZnOBub3Grky9/0fgH/uzZk1KW9IZQAH4Ua/xHYELgQOSz+8DtjWzr/a2\n2kuRdvA/s8OB5k7XuJjvkUXnI61TGi2CUwW5rj0i6TjCBuV+lnqmSBJpbgWONbPnkuG/BnaV9Hxy\nzcmS7jazfSoK6D7t6LRN9Ap0sfE7nA3q6R6JxYCUtqQDCRuPeydlV3vGxwG3EzYpf98zbmbfAb6T\nzNka+EU1Chtg9SqvQxyTkaO6sE7/YRwMxnuhv6i8XodfxlxY2n2ksc8ChgN3Js7z+8zsROBkYFvg\nTElnJkscYGYDzuH1NPa4LF+9EWO39hTr2Mwr+j2OThFOrXGJLFT5G2ga+/f7mHsecF6F9Z5nAA1+\nHcdxYjNUmyDUFa89Mgi4dyQ6Rb/JmSAv7pH+ZEQOI/SN3I2gCr5sZncn5wwjJOnskxw7IykwVZZX\n3xrV/7/K6ReTW/yHMTat9a2C7EQiF0qb/mVEfg7AzN4jaTJwh6TdzaybUJp1qZltJ6kFmFCNgFPG\ne9nQ2FhXoyXIPx9undxoEZwqyEX0SH8yIoEZwF3JnKWS3iBY3fcTknDenRzrJqS8V6RrTdN7cDJP\n68YDqprr9IOJHjmSCfJiaVcinRH5CHBYUjd2S2BXYEtJPTn150rah+BSOdnMXim1YDoj8rypMzhq\ngjf2jUnrOG/sG5t6hKM58clF9Eg5SmREXgPsADwAvAD8ITneRqhw9Xsz+5qkrwGXAMeUWjedEblw\nxkeCXe7Eo8Ut7dg867VHMkExA8qmrhmRZlYAvpqa8wfgWeA1Qs2Rnm43PyXUjq3IkuVjBiqiUyXj\nOjyGODZPr13eaBGcKsiFT7sUZTIiRwIys1VJbe1CTz80ST8nRI78L7AfqZY75fCNyLh0F1s85G8Q\n2K19EpPM92diUo9GE7nwafczI3IyME9SN6F+bNr9cRrw35K+DbwKnFDHv8MZIC2trrEHA1fY8anH\nPc6FT7ufGZHPA9v3cewFQrf2flEseHxrbNrG+T2OzUP4E2MW6M6re2QwKXoThOhYofm/qFnngKJ3\nrskCubC0G83YTby3Xmysu/m/qFlncsHdUFkgF9EjfaSxnwscRtjCWgocb2ZLkmM7E1LZxyTHdzez\nTklHA18ndCNeAnzazCom2Kx6c/hA/i6nH4zetvm/qFnntyP8Hg8Gh9V4fl7cIz9gwzT2i83sTABJ\nXyL0OTtRUhtwPaFJ5SOSNgHWJuOXATPMbJmkiwhlXGdXuviw4d5WJToepx2dKb4RmQly4R7pI419\nRerjKFj3lx4APGpmjyTzXgOQ1A4IGCXpNYIVvrAaAQu+ERmdwjLPsY6N3+FskBdLuySSzgeOBd4E\nPpQMbweYpHnAJGCOmV1kZmslnQQ8BqwiJNx8ocza69LYz50yg6PGexp7TIZt5S6o2Cx62jMis0Au\nLO2+MLMzgDMkzSK4Os5O1vsgsDshA/IuSQ8C9wAnAe8HFgH/SYj1LtkwIZ3G/vi0Q6yzc6BSOtVQ\nXL6y0SLknpkF/2HMAkVr/meiejjafkzoC3k20AH8tmeDUdJcYBdgBUBPo19JNwKnV7O4+7TjU1zZ\n/NZF1lnmBaMyQZ7T2Keb2bPJx0OBp5L384BTk3T2LmBv4FJCduQMSZPM7FVgf2BBNdfq9jjt6Mj3\nyKKzyAtGZYI8p7EfLGl7QkjfC8CJAGb2uqRvAfMJm5Nzzez2ZJ1zgHskrU3OOb4aAd3Sjk/rODcD\nYzPd3D2SBXJhafcnjT2Zfz0h7K/3+JXAlf2SDuguejhabFpGuqkdGy/Nmg1yHT0yWBS73T0Sm+Jy\nzzqNzc5Ft7SzQC6iR0plRKaOnQJcDExKZzdK2opQenW2mV2SjO1KSNTZCJhLaPpb8Q5JzX8Ts45v\nRMZnpdsemSAXaeyUzohE0paEDcXFJc65FLij19h3CLHX9xGU9oEl5mwoYFvz38Ss0zbRfdqx8Tuc\nDfLi094gIzLhUuBU4GfpQUkfI8Rir0qNTQXGmNm9yefrgI9RhdJevWpYpSlOjYzv9HbssRnf/OG/\nDjn2aUs6FHgpqS+SHh9FaHawP3BK6pTNCTHcPXQkY32tvy4j8pxNd+ST47YciJhOlax9o/m/qFln\nre+nZ4JcWNq9SWKwzyDUGenNOcClZrYyrcwJdUd60+fdSWdELnrPAeaVG+IybIo/vMdmZVWVdpxG\nk4s47RL8FbAN0GNlbwE8JGkmsAdweFLFbxzQLakTuDmZ18MWhPKsjuM4TUMuLW0ze4zQCxIASc8D\nuyXRI3ulxmcDK83s8uTzW5L2BP5IKDT1nzVJ7jiOU2dyET1SKiPSzPpMrinDSbwT8ncHVWxCAnSt\nafpQ8uzTvbbREuSev9ShU7gTn1xsRPaREZk+vnUf47N7fX4A2KnU3HJ4Gvsg4E0QouNNELJBLt0j\ng82azqYXMfsU3NKOzXzvxp4J6p0RKelAQteuVuBqM7ug13Elxw8mlLM+3sweKrema0SH4srm9+Nl\nnVeLqypPchpOPS1tSa3AFYQQ6A5gvqTbzOzJ1LSDgOnJaw9CEuIe5dZteqUtf3J3csD0tvGNFsGp\ngjr7tGcCC81sEYCkOYTew2mlfRhwXVLS4z5J4yRNNbOX+1q06ZX2u5+Zmym1LenzSZy5E5Gs3efv\nNVqAAZC1e1wPCl0vVa1v0kmACVf1ul+bAy+mPnewoRVdas7mQJ9K28vY1J/PV57i1AG/z/Hxe1wG\nM7vKzHZLvXr/wFWTVNivxENwpe04jhOLDiBdg6NUUmE1c9bDlbbjOE4c5gPTJW0jaRhwFHBbrzm3\nAccqsCfwZjl/NmTAp51BhpQPsIH4fY6P3+MaMLOCpJMJvXNbgWvM7AlJPe0ZrySUqT4YWEgI+Tuh\n0rrKQjC54ziOE3D3iOM4ToZwpe04jpMhXGnXEUkHSnpa0kJJpzdanjwi6RpJSyU93mhZ8oikLSX9\nRtICSU9I+nKjZXLWx33adSJJWX2GVMoqcHSvlFWnRiT9LbCSkEXW7wJkTnmS1oBTzewhSaOBB4GP\n+fe4eXBLu36sS1k1sy6gJ2XVqSNmdg+wvNFy5BUze7mnYJGZvQUsoExrQGfwcaVdP/pKR3WcTJI0\n9H4/oXGJ0yS40q4f/U5HdZxmRdLGhDaBXzGzFY2Wx3kHV9r1o9/pqI7TjEhqJyjsH5nZLY2Wx1kf\nV9r1o5qUVcdpapKi/N8HFpjZtxotj7MhrrTrhJkVgJ6U1QXAjWb2RGOlyh9Jz9J7ge0ldUj6bKNl\nyhkfAI4B9pX0p+R1cKOFct7BQ/4cx3EyhFvajuM4GcKVtuM4ToZwpe04jpMhXGk7juNkCFfajuM4\nGcKVtuM4ToZwpe04jpMh/j9JukX8u1kf2gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/DryE3s2dR4B3VNV3x1Peiho528b9GIalMMijHAZ63MMqNPC8kvwcvdD/mWWtaDwGmfd7\ngHdW1bne4u+iMMi81wKvBLYBzwI+m+RzVfVvy13cMhpk3jcADwM/D/w4cH+Sf66qby53cSts5Gxb\njaE/yKMcLtbHPQw0ryQ/CdwFvL6qvj6m2pbTIPOeBA50gX8FcGOSs1X11+MpcVkM+m/9a1X1beDb\nST4NvAxYzaE/yLzfCtxRvQvd00lOAC8GHhxPiStm5GxbjZd3BnmUwyHgLd073dcDT1bV6XEXugwW\nnHuSFwD3Am9e5au9fgvOu6q2VNXmqtoM3AP89ioPfBjs3/p9wM8mWZvk2cCrgGNjrnOpDTLvk/T+\nd0OSq+g9jfcrY61yZYycbatupV/neZRDkt/sjr+f3qc3bgSmgf+ltypY9Qac+x8CPwLc2a16z9Yq\nfyrhgPO+6Awy76o6luQTwJeB7wJ3VdW8H/dbLQb8ef8R8KEkR+hd8nhnVa36Ry4nuRt4HXBFkhng\nduAZsHTZ5mMYJKkhq/HyjiRpSIa+JDXE0Jekhhj6ktQQQ1+SGmLoS1JDDH1Jasj/A3tJFh8lZwJ4\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_test)\n",
    "plt.show()\n",
    "\n",
    "plt.hist(y_pred)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "##### Try to Make some EDA on predicted user performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "X = new_df[['acousticness','bounciness']]\n",
    "X = X.values\n",
    "\n",
    "y = new_df[['hist_user_behavior_reason_start','hist_user_behavior_reason_end','hist_user_behavior_is_shuffle']]\n",
    "y = y.values"
   ]
  },
  {
   "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",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.606757</td>\n",
       "      <td>0.571622</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.606757</td>\n",
       "      <td>0.571622</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.606757</td>\n",
       "      <td>0.571622</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.606757</td>\n",
       "      <td>0.571622</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.606757</td>\n",
       "      <td>0.571622</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2\n",
       "0  0.606757  0.571622  0.108108\n",
       "1  0.606757  0.571622  0.108108\n",
       "2  0.606757  0.571622  0.108108\n",
       "3  0.606757  0.571622  0.108108\n",
       "4  0.606757  0.571622  0.108108"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "clf2 = DecisionTreeRegressor(max_depth=None, max_leaf_nodes=20)\n",
    "clf2.fit(X, y)\n",
    "y_pred2 = clf2.predict(X)\n",
    "y_pred2 = pd.DataFrame(y_pred2)\n",
    "y_pred2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_df['pred_hist_user_behavior_reason_start'] = y_pred2[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_df['pred_hist_user_behavior_reason_end'] = y_pred2[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new_df['pred_hist_user_behavior_is_shuffle'] = y_pred2[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "        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>Unnamed: 0</th>\n",
       "      <th>session_id</th>\n",
       "      <th>track_id</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>beat_strength</th>\n",
       "      <th>bounciness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>dyn_range_mean</th>\n",
       "      <th>energy</th>\n",
       "      <th>flatness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</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>pred_hist_user_behavior_reason_start</th>\n",
       "      <th>pred_hist_user_behavior_reason_end</th>\n",
       "      <th>pred_hist_user_behavior_is_shuffle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0_00006f66-33e5-4de7-a324-2d18e439fc1e</td>\n",
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       "      <td>1.035007</td>\n",
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       "      <td>1</td>\n",
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       "      <td>-6.577</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0_00079a23-1600-486a-91bd-5208be0c745a</td>\n",
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       "      <td>0.473455</td>\n",
       "      <td>0.653119</td>\n",
       "      <td>7.660024</td>\n",
       "      <td>0.553465</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>1</td>\n",
       "      <td>0.678553</td>\n",
       "      <td>-6.577</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.606757</td>\n",
       "      <td>0.571622</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0_012b0fb4-0cc3-429f-9a78-cc6e622153fb</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</td>\n",
       "      <td>0.015848</td>\n",
       "      <td>0.438551</td>\n",
       "      <td>0.473455</td>\n",
       "      <td>0.653119</td>\n",
       "      <td>7.660024</td>\n",
       "      <td>0.553465</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>1</td>\n",
       "      <td>0.678553</td>\n",
       "      <td>-6.577</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.606757</td>\n",
       "      <td>0.571622</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0_013cc010-c476-4ad2-8972-73449e0b2ef4</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</td>\n",
       "      <td>0.015848</td>\n",
       "      <td>0.438551</td>\n",
       "      <td>0.473455</td>\n",
       "      <td>0.653119</td>\n",
       "      <td>7.660024</td>\n",
       "      <td>0.553465</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>1</td>\n",
       "      <td>0.678553</td>\n",
       "      <td>-6.577</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.606757</td>\n",
       "      <td>0.571622</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0_01a5f0dc-9938-48c9-92f1-c7e51f34d290</td>\n",
       "      <td>t_0479f24c-27d2-46d6-a00c-7ec928f2b539</td>\n",
       "      <td>0.015848</td>\n",
       "      <td>0.438551</td>\n",
       "      <td>0.473455</td>\n",
       "      <td>0.653119</td>\n",
       "      <td>7.660024</td>\n",
       "      <td>0.553465</td>\n",
       "      <td>1.035007</td>\n",
       "      <td>0.003484</td>\n",
       "      <td>1</td>\n",
       "      <td>0.678553</td>\n",
       "      <td>-6.577</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.606757</td>\n",
       "      <td>0.571622</td>\n",
       "      <td>0.108108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                              session_id  \\\n",
       "0           0  0_00006f66-33e5-4de7-a324-2d18e439fc1e   \n",
       "1           1  0_00079a23-1600-486a-91bd-5208be0c745a   \n",
       "2           2  0_012b0fb4-0cc3-429f-9a78-cc6e622153fb   \n",
       "3           3  0_013cc010-c476-4ad2-8972-73449e0b2ef4   \n",
       "4           4  0_01a5f0dc-9938-48c9-92f1-c7e51f34d290   \n",
       "\n",
       "                                 track_id  acousticness  beat_strength  \\\n",
       "0  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "1  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "2  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "3  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "4  t_0479f24c-27d2-46d6-a00c-7ec928f2b539      0.015848       0.438551   \n",
       "\n",
       "   bounciness  danceability  dyn_range_mean    energy  flatness  \\\n",
       "0    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "1    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "2    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "3    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "4    0.473455      0.653119        7.660024  0.553465  1.035007   \n",
       "\n",
       "   instrumentalness  key  liveness  loudness  hist_user_behavior_reason_start  \\\n",
       "0          0.003484    1  0.678553    -6.577                              1.0   \n",
       "1          0.003484    1  0.678553    -6.577                              0.3   \n",
       "2          0.003484    1  0.678553    -6.577                              1.0   \n",
       "3          0.003484    1  0.678553    -6.577                              0.3   \n",
       "4          0.003484    1  0.678553    -6.577                              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",
       "   pred_hist_user_behavior_reason_start  pred_hist_user_behavior_reason_end  \\\n",
       "0                              0.606757                            0.571622   \n",
       "1                              0.606757                            0.571622   \n",
       "2                              0.606757                            0.571622   \n",
       "3                              0.606757                            0.571622   \n",
       "4                              0.606757                            0.571622   \n",
       "\n",
       "   pred_hist_user_behavior_is_shuffle  \n",
       "0                            0.108108  \n",
       "1                            0.108108  \n",
       "2                            0.108108  \n",
       "3                            0.108108  \n",
       "4                            0.108108  "
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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