Spotify_FinalProject_SVM_CatBoost.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af603bfc-3399-414d-97d1-8e597050cb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final Project Scratch\n",
    "\n",
    "# Packages\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "from itertools import product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b2f28c8-bafe-4bd6-9825-0a24c3965a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "music = pd.read_csv('music.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c0ac2e9b-1f77-498d-91ad-6c36b783176e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 174389 entries, 0 to 174388\n",
      "Data columns (total 19 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   acousticness      174389 non-null  float64\n",
      " 1   artists           174389 non-null  object \n",
      " 2   danceability      174389 non-null  float64\n",
      " 3   duration_ms       174389 non-null  int64  \n",
      " 4   energy            174389 non-null  float64\n",
      " 5   explicit          174389 non-null  int64  \n",
      " 6   id                174389 non-null  object \n",
      " 7   instrumentalness  174389 non-null  float64\n",
      " 8   key               174389 non-null  int64  \n",
      " 9   liveness          174389 non-null  float64\n",
      " 10  loudness          174389 non-null  float64\n",
      " 11  mode              174389 non-null  int64  \n",
      " 12  name              174389 non-null  object \n",
      " 13  popularity        174389 non-null  int64  \n",
      " 14  release_date      174389 non-null  object \n",
      " 15  speechiness       174389 non-null  float64\n",
      " 16  tempo             174389 non-null  float64\n",
      " 17  valence           174389 non-null  float64\n",
      " 18  year              174389 non-null  int64  \n",
      "dtypes: float64(9), int64(6), object(4)\n",
      "memory usage: 25.3+ MB\n"
     ]
    }
   ],
   "source": [
    "music.head()\n",
    "music.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79bcc7f6-1266-4175-b0e1-7e736ff231af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove columns ID and Name\n",
    "\n",
    "music = music.drop('id', 1)\n",
    "music = music.drop('name', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "91931585-dfdc-4c70-93e9-450ce5ccf7ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 174389 entries, 0 to 174388\n",
      "Data columns (total 17 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   acousticness      174389 non-null  float64\n",
      " 1   artists           174389 non-null  object \n",
      " 2   danceability      174389 non-null  float64\n",
      " 3   duration_ms       174389 non-null  int64  \n",
      " 4   energy            174389 non-null  float64\n",
      " 5   explicit          174389 non-null  int64  \n",
      " 6   instrumentalness  174389 non-null  float64\n",
      " 7   key               174389 non-null  int64  \n",
      " 8   liveness          174389 non-null  float64\n",
      " 9   loudness          174389 non-null  float64\n",
      " 10  mode              174389 non-null  int64  \n",
      " 11  popularity        174389 non-null  int64  \n",
      " 12  release_date      174389 non-null  object \n",
      " 13  speechiness       174389 non-null  float64\n",
      " 14  tempo             174389 non-null  float64\n",
      " 15  valence           174389 non-null  float64\n",
      " 16  year              174389 non-null  int64  \n",
      "dtypes: float64(9), int64(6), object(2)\n",
      "memory usage: 22.6+ MB\n"
     ]
    }
   ],
   "source": [
    "music.head()\n",
    "music.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25d35231-63c5-43ed-8ea7-f2a8c15cbdff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Decades Variable\n",
    "\n",
    "#music.loc[1,1]\n",
    "#len(music)\n",
    "music['year'][1201] >= 1930\n",
    "music.year[1201] >= 1930\n",
    "\n",
    "music['decade'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c843eb49-ad77-4b91-9004-aea90cf703a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 174389 entries, 0 to 174388\n",
      "Data columns (total 18 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   acousticness      174389 non-null  float64\n",
      " 1   artists           174389 non-null  object \n",
      " 2   danceability      174389 non-null  float64\n",
      " 3   duration_ms       174389 non-null  int64  \n",
      " 4   energy            174389 non-null  float64\n",
      " 5   explicit          174389 non-null  int64  \n",
      " 6   instrumentalness  174389 non-null  float64\n",
      " 7   key               174389 non-null  int64  \n",
      " 8   liveness          174389 non-null  float64\n",
      " 9   loudness          174389 non-null  float64\n",
      " 10  mode              174389 non-null  int64  \n",
      " 11  popularity        174389 non-null  int64  \n",
      " 12  release_date      174389 non-null  object \n",
      " 13  speechiness       174389 non-null  float64\n",
      " 14  tempo             174389 non-null  float64\n",
      " 15  valence           174389 non-null  float64\n",
      " 16  year              174389 non-null  int64  \n",
      " 17  decade            174389 non-null  int64  \n",
      "dtypes: float64(9), int64(7), object(2)\n",
      "memory usage: 23.9+ MB\n"
     ]
    }
   ],
   "source": [
    "music.head()\n",
    "music.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "599536d0-610a-49ff-b6b5-0ef1a6a43bbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 1920\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:5: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 1930\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 1940\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 1950\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 1960\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:19: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 2000\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:21: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 2010\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:23: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 2020\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:17: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 1990\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 1970\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/2075037868.py:15: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music['decade'][i] = 1980\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(music)):\n",
    "    if (music.year[i] >= 1920) & (music.year[i] < 1930):\n",
    "        music['decade'][i] = 1920\n",
    "    elif (music.year[i] >= 1930) & (music.year[i] < 1940):\n",
    "        music['decade'][i] = 1930\n",
    "    elif (music.year[i] >= 1940) & (music.year[i] < 1950):\n",
    "        music['decade'][i] = 1940\n",
    "    elif (music.year[i] >= 1950) & (music.year[i] < 1960):\n",
    "        music['decade'][i] = 1950\n",
    "    elif (music.year[i] >= 1960) & (music.year[i] < 1970):\n",
    "        music['decade'][i] = 1960\n",
    "    elif (music.year[i] >= 1970) & (music.year[i] < 1980):\n",
    "        music['decade'][i] = 1970\n",
    "    elif (music.year[i] >= 1980) & (music.year[i] < 1990):\n",
    "        music['decade'][i] = 1980\n",
    "    elif (music.year[i] >= 1990) & (music.year[i] < 2000):\n",
    "        music['decade'][i] = 1990\n",
    "    elif (music.year[i] >= 2000) & (music.year[i] < 2010):\n",
    "        music['decade'][i] = 2000\n",
    "    elif (music.year[i] >= 2010) & (music.year[i] < 2020):\n",
    "        music['decade'][i] = 2010\n",
    "    else:\n",
    "        music['decade'][i] = 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d957ccce-91b7-46a9-9b44-6b8b0a8d7689",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>decade</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1920</td>\n",
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       "<p>174389 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "        year  decade\n",
       "0       1920    1920\n",
       "1       1920    1920\n",
       "2       1920    1920\n",
       "3       1920    1920\n",
       "4       1920    1920\n",
       "...      ...     ...\n",
       "174384  2020    2020\n",
       "174385  2021    2020\n",
       "174386  2020    2020\n",
       "174387  2021    2020\n",
       "174388  2020    2020\n",
       "\n",
       "[174389 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "music[['year', 'decade']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c636f9f1-490b-49dc-a370-861c8fb5cf19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010    22455\n",
       "1990    20328\n",
       "1970    20118\n",
       "1980    20074\n",
       "1950    20000\n",
       "1960    19610\n",
       "1940    15493\n",
       "2000    13782\n",
       "1930    10764\n",
       "2020     6134\n",
       "1920     5631\n",
       "Name: decade, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(music.decade)\n",
    "music.decade.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e5cde144-cd4f-4ad4-b485-e1d223a25ac4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    43768\n",
       "4    43669\n",
       "3    43134\n",
       "1    42737\n",
       "0     1081\n",
       "Name: tempo_disc, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Tempos\n",
    "# hist(music$tempo)\n",
    "# music$tempo_disc <- discretize(music$tempo, method = \"cluster\", breaks = 5, labels = FALSE)\n",
    "# music$tempo_disc <- as.factor(music$tempo_disc)\n",
    "# table(music$temp_disc)\n",
    "\n",
    "plt.hist(music.tempo)\n",
    "music[\"tempo_disc\"] = np.digitize(music.tempo, [60, 94, 116, 135])\n",
    "music[\"tempo_disc\"] = music[\"tempo_disc\"].astype(str)\n",
    "music.tempo_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "116facb5-78c1-42ef-af4b-89cf3f405b3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/3859284878.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music.multi_artist[i] = 0\n",
      "/var/folders/hr/q7jvsqzd6n9fz7ld_5zx4df00000gn/T/ipykernel_71270/3859284878.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  music.multi_artist[i] = 1\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0    136808\n",
       "1     37581\n",
       "Name: multi_artist, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Multi Artists\n",
    "# for (i in 1:nrow(music)) {\n",
    "#   if (grepl(\"', '\", music$artists[i])) { music$multi_artist[i] <- 1 }\n",
    "#   else { music$multi_artist[i] <- 0 }\n",
    "# }\n",
    "# table(music$multi_artist)\n",
    "\n",
    "music[\"multi_artist\"] = np.where((\",\" in music.artists), 1, 0) #I don't know why but this line has to be run before the for loop\n",
    "for i in range(len(music)):\n",
    "    if \",\" in music.artists[i]:\n",
    "        music.multi_artist[i] = 1\n",
    "    else:\n",
    "        music.multi_artist[i] = 0\n",
    "\n",
    "music.multi_artist = music.multi_artist.astype(str) \n",
    "music.multi_artist.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e4139d0c-e647-4f97-837b-b64934d04798",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Explicit - make factor\n",
    "# music$explicit <- as.factor(music$explicit)\n",
    "\n",
    "music.explicit = music.explicit.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f199fdff-fd7c-415e-a7aa-fe897c083d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Duration MS\n",
    "# hist(music$duration_ms)\n",
    "# hist(log(music$duration_ms))\n",
    "# music$log_duration_ms <- log(music$duration_ms)\n",
    "\n",
    "plt.hist(music.duration_ms)\n",
    "plt.hist(np.log(music.duration_ms))\n",
    "music[\"log_duration_ms\"] = np.log(music.duration_ms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "44ca3bdc-a3bb-4dcb-b93c-c333541847a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    44561\n",
       "3    44086\n",
       "0    42221\n",
       "2    27187\n",
       "1    16334\n",
       "Name: acousticness_disc, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7KjNHTTnmJO8E/o5eEMz188gwxZir6nBVLamqkaoaoXed5NKqGh1Od2dEl5/tf6N3VECSJfROGz0zwD7OtC5j/g5wAUCSX6YXBuMD7eXg7QCuancVrQEOV9Xz09nQvD1NVJM84iLJp4HRqtoB3ELvUHKM3kWa9cPr8YnrOOa/Al4P/HO7Vv6dqrp0aJ0+QR3HPK90HPMu4MIkTwCvAH9cVXP2qLfjmD8J/H2SP6R3MfnDc/zNHUm+TC/Ul7RrIdcCpwBU1RfpXRu5BBgDXgQ+Mu19zfF/K0nSDJjPp4kkSR0ZBpIkw0CSZBhIkjAMJEkYBpIkDANJEvD/U+1uUDPnd+MAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Acousticness - discretize into 5 bins\n",
    "# hist(music$acousticness)\n",
    "# hist(music$acousticness, breaks = 20, main = \"K-Means\")\n",
    "# abline(v = discretize(music$acousticness, method = \"cluster\", breaks = 5, \n",
    "#                       onlycuts = TRUE), col = \"red\")\n",
    "# music$acousticness_disc <- discretize(music$acousticness, method = \"cluster\", breaks = 5, labels = FALSE)\n",
    "# table(music$acousticness_disc)\n",
    "# music$acousticness_disc <- as.factor(music$acousticness_disc)\n",
    "\n",
    "plt.hist(music.acousticness)\n",
    "music.acousticness.describe()\n",
    "music[\"acousticness_disc\"] = np.digitize(music.acousticness, [0.08, 0.2, 0.5, 0.89])\n",
    "music.acousticness_disc = music.acousticness_disc.astype(str)\n",
    "music.acousticness_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6a711171-0fdb-4656-844f-36ca64e38859",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    46678\n",
       "3    39932\n",
       "0    39740\n",
       "2    27905\n",
       "1    20134\n",
       "Name: danceability_disc, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Danceability - discretize\n",
    "# hist(music$danceability)\n",
    "# summary(music$danceability)\n",
    "# hist(music$danceability, breaks = 20, main = \"K-Means\")\n",
    "# abline(v = discretize(music$danceability, method = \"cluster\", breaks = 5, \n",
    "#                       onlycuts = TRUE), col = \"red\")\n",
    "# music$danceability_disc <- discretize(music$danceability, method = \"cluster\", breaks = 5, labels = FALSE)\n",
    "# music$danceability_disc <- as.factor(music$danceability_disc)\n",
    "\n",
    "plt.hist(music.danceability)\n",
    "music.danceability.describe()\n",
    "music[\"danceability_disc\"] = np.digitize(music.danceability, [0.4, 0.47, 0.55, 0.66])\n",
    "music.danceability_disc = music.danceability_disc.astype(str)\n",
    "music.danceability_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d538e89d-e9b5-4763-8e1a-8cbb30809c90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    42115\n",
       "2    37296\n",
       "3    33079\n",
       "0    32477\n",
       "4    29422\n",
       "Name: energy_disc, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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u2ctENcsjLpLcDExW1X7g03ROJ6foTNZcM7oe96fH8f4R8DrgL9o8+der6sqRdbpPPY55SelxzAeBLUmeAL4H/G5VjesZb69j/gDwqSS/TWcy+T1j/A87knyOTqCvavMgu4FXA1TVJ+nMi2wDpoCXgev7PuYY/3pJkgZkKV8mkiT1yDCQJBkGkiTDQJKEYSBJwjCQJGEYSJKA/wPzZzPMEVLo1wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Energy - transform or discretize\n",
    "# hist(music$energy)\n",
    "# library(bestNormalize)\n",
    "# BNobject <- bestNormalize(music$energy)\n",
    "# music$energy_trans <- BNobject$x.t\n",
    "# hist(music$energy_trans)\n",
    "# hist(music$energy, breaks = 20, main = \"K-Means\")\n",
    "# abline(v = discretize(music$energy, method = \"cluster\", breaks = 5, \n",
    "#                       onlycuts = TRUE), col = \"red\")\n",
    "# music$energy_disc <- discretize(music$energy, method = \"cluster\", breaks = 5, labels = FALSE)\n",
    "# music$energy_disc <- as.factor(music$energy_disc)\n",
    "\n",
    "plt.hist(music.energy)\n",
    "music.energy.describe()\n",
    "music[\"energy_disc\"] = np.digitize(music.energy, [0.2, 0.4, 0.6, 0.8])\n",
    "music.energy_disc = music.energy_disc.astype(str)\n",
    "music.energy_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d6bc9c5e-e4c5-4d7a-9832-6f0a49f4fbcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    91786\n",
       "1    31978\n",
       "4    24535\n",
       "2    14290\n",
       "3    11800\n",
       "Name: instrumentalness_disc, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instrumentalness - discretize into 5 bins\n",
    "# hist(music$instrumentalness, breaks = 20, main = \"K-Means\")\n",
    "# abline(v = discretize(music$instrumentalness, method = \"cluster\", breaks = 5, \n",
    "#                       onlycuts = TRUE), col = \"red\")\n",
    "# music$instrumentalness_disc <- discretize(music$instrumentalness, method = \"cluster\", breaks = 5, labels = FALSE)\n",
    "# music$instrumentalness_disc <- as.factor(music$instrumentalness_disc)\n",
    "\n",
    "plt.hist(music.instrumentalness)\n",
    "music.instrumentalness.describe()\n",
    "music[\"instrumentalness_disc\"] = np.digitize(music.instrumentalness, [0.001, 0.1, 0.5, 0.8])\n",
    "music.instrumentalness_disc = music.instrumentalness_disc.astype(str)\n",
    "music.instrumentalness_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5e980296-075f-4faa-ae05-540c9851dde7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    71246\n",
       "2    42656\n",
       "3    24118\n",
       "0    22946\n",
       "4    13423\n",
       "Name: liveness_disc, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Key - make factor\n",
    "# music$key <- as.factor(music$key)\n",
    "\n",
    "music.key = music.key.astype(str)\n",
    "\n",
    "# # Liveness - discretize into 5 bins\n",
    "# hist(music$liveness, breaks = 5)\n",
    "# table(discretize(music$liveness, method = \"cluster\", breaks = 5))\n",
    "# hist(music$liveness, breaks = 20, main = \"K-Means\")\n",
    "# abline(v = discretize(music$liveness, method = \"cluster\", breaks = 5, \n",
    "#                       onlycuts = TRUE), col = \"red\")\n",
    "# music$liveness_disc <- discretize(music$liveness, method = \"cluster\", breaks = 5, labels = FALSE)\n",
    "# music$liveness_disc <- as.factor(music$liveness_disc)\n",
    "\n",
    "plt.hist(music.liveness)\n",
    "music.liveness.describe()\n",
    "music[\"liveness_disc\"] = np.digitize(music.liveness, [0.08, 0.15, 0.3, 0.5])\n",
    "music.liveness_disc = music.liveness_disc.astype(str)\n",
    "music.liveness_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4381f99f-5130-4fe4-891e-7a374bcd71bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    62359\n",
       "2    54982\n",
       "1    27174\n",
       "0    15712\n",
       "4    14162\n",
       "Name: loudness_disc, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loudness - discretize into 5 bins\n",
    "# hist(music$loudness)\n",
    "# table(discretize(music$loudness, method = \"cluster\", breaks = 5))\n",
    "# hist(music$loudness, breaks = 20, main = \"K-Means\")\n",
    "# abline(v = discretize(music$loudness, method = \"cluster\", breaks = 5, \n",
    "#                       onlycuts = TRUE), col = \"red\")\n",
    "# music$loudness_disc <- discretize(music$loudness, method = \"cluster\", breaks = 5, labels = FALSE)\n",
    "# music$loudness_disc <- as.factor(music$loudness_disc)\n",
    "\n",
    "plt.hist(music.loudness)\n",
    "music.loudness.describe()\n",
    "music[\"loudness_disc\"] = np.digitize(music.loudness, [-20, -15, -10, -5])\n",
    "music.loudness_disc = music.loudness_disc.astype(str)\n",
    "music.loudness_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "dfd494c9-7522-43d7-aac9-1b399e0d6bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    44458\n",
       "3    44443\n",
       "2    43094\n",
       "1    26266\n",
       "0    16128\n",
       "Name: speechiness_disc, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Speechiness - discretize into 5 bins\n",
    "# hist(music$speechiness)\n",
    "# table(discretize(music$speechiness, method = \"cluster\", breaks = 5))\n",
    "# hist(music$speechiness, breaks = 20, main = \"K-Means\")\n",
    "# abline(v = discretize(music$speechiness, method = \"cluster\", breaks = 5, \n",
    "#                       onlycuts = TRUE), col = \"red\")\n",
    "# music$speechiness_disc <- discretize(music$speechiness, method = \"cluster\", breaks = 5, labels = FALSE)\n",
    "# music$speechiness_disc <- as.factor(music$speechiness_disc)\n",
    "\n",
    "plt.hist(music.speechiness)\n",
    "music.speechiness.describe()\n",
    "music[\"speechiness_disc\"] = np.digitize(music.speechiness, [0.03, 0.035, 0.045, 0.075])\n",
    "music.speechiness_disc = music.speechiness_disc.astype(str)\n",
    "music.speechiness_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "701f7824-eeac-4bdb-ae60-5585ba780e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    44447\n",
       "3    44191\n",
       "2    42316\n",
       "0    25567\n",
       "1    17868\n",
       "Name: valence_disc, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Valence - discretize into 5 bins\n",
    "# table(discretize(music$valence, method = \"cluster\", breaks = 5))\n",
    "# hist(music$valence, breaks = 20, main = \"K-Means\")\n",
    "# abline(v = discretize(music$valence, method = \"cluster\", breaks = 5, \n",
    "#                       onlycuts = TRUE), col = \"red\")\n",
    "# music$valence_disc <- discretize(music$valence, method = \"cluster\", breaks = 5, labels = FALSE)\n",
    "# music$valence_disc <- as.factor(music$valence_disc)\n",
    "\n",
    "plt.hist(music.valence)\n",
    "music.valence.describe()\n",
    "music[\"valence_disc\"] = np.digitize(music.valence, [0.2, 0.31, 0.53, 0.74])\n",
    "music.valence_disc = music.valence_disc.astype(str)\n",
    "music.valence_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2780f456-75fe-437c-8117-a35240d6c09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mode - factor\n",
    "# music$mode <- as.factor(music$mode)\n",
    "\n",
    "music.mode = music[\"mode\"].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a386e600-6d15-4ae4-94ad-e7ac520c0ac8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    82401\n",
       "1    61384\n",
       "2    28398\n",
       "3     2206\n",
       "Name: popularity_disc, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Popularity - factor w/ 5 levels\n",
    "# music$popularity_disc <- discretize(music$popularity, method = \"interval\", breaks = 3, labels = FALSE)\n",
    "# music$popularity_disc <- as.factor(music$popularity_disc)\n",
    "# summary(music$popularity)\n",
    "# table(music$popularity_disc)\n",
    "\n",
    "plt.hist(music.popularity)\n",
    "music.popularity.describe()\n",
    "music[\"popularity_disc\"] = np.digitize(music.popularity, [24, 49, 74])\n",
    "music.popularity_disc = music.popularity_disc.astype(str)\n",
    "music.popularity_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "73dec986-81de-45de-8d9e-4982f82f245f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Split into 2 datasets: All Numeric, Discretized\n",
    "# music_raw <- music[which(colnames(music) %in% c(\"acousticness\", \"danceability\", \"duration_ms\", \"energy\", \"explicit\", \"instrumentalness\", \n",
    "#\"key\", \"liveness\", \"loudness\", \"mode\", \"popularity_disc\", \"release_date\", \"speechiness\", \"tempo\", \"valence\", \"year\", \"multi_artist\"))]\n",
    "\n",
    "# music_disc <- music[which(colnames(music) %in% c(\"popularity_disc\", \"decade\", \"temp_disc\", \"multi_artist\", \"log_duration_ms\", \"danceability_disc\", \n",
    "#\"explicit\", \"instrumentalness_disc\", \"key\", \"liveness_disc\", \"loudness_disc\", \"speechiness_disc\", \"valence_disc\", \"acousticness_disc\", \"energy_disc\",\n",
    "#\"mode\"))]\n",
    "\n",
    "music_raw = music[[\"acousticness\", \"danceability\", \"duration_ms\", \"energy\", \"explicit\", \"instrumentalness\",\"key\", \"liveness\", \"loudness\", \"mode\", \n",
    "                   \"popularity_disc\", \"release_date\", \"speechiness\", \"tempo\", \"valence\", \"year\", \"multi_artist\"]]\n",
    "\n",
    "music_disc = music[[\"popularity_disc\", \"decade\", \"tempo_disc\", \"multi_artist\", \"log_duration_ms\", \"danceability_disc\", \"explicit\", \n",
    "                    \"instrumentalness_disc\", \"key\", \"liveness_disc\", \"loudness_disc\", \"speechiness_disc\", \"valence_disc\", \"acousticness_disc\", \n",
    "                    \"energy_disc\", \"mode\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bb407410",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training and holdout\n",
    "X = music_disc.loc[:, music_disc.columns != 'popularity_disc']\n",
    "y = music_disc.loc[:, music_disc.columns == 'popularity_disc']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "60edde78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.07456467305082"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Naive Model\n",
    "y_test['popularity_disc'].value_counts()\n",
    "24628/(len(y_test))*100  #47.07456467305082 accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "d844c6cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create tuning parameter grid function\n",
    "def expand_grid(dictionary):\n",
    "   return pd.DataFrame([i for i in product(*dictionary.values())], \n",
    "                       columns=dictionary.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ef186d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "accuracy     0.761416\n",
       "criterion        gini\n",
       "splitter         best\n",
       "max_depth          10\n",
       "Name: 10, dtype: object"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DECISION TREE\n",
    "# Create tuning grid for Decision Tree\n",
    "dictionary = {'criterion': ['entropy','gini'],\n",
    "             'splitter': ['random','best'],\n",
    "             'max_depth': [8,10,25]}\n",
    "\n",
    "grid = expand_grid(dictionary)\n",
    "\n",
    "\n",
    "# CREATE MODEL\n",
    "accuracies = []\n",
    "for i in grid.index:\n",
    "    dt = DecisionTreeClassifier(criterion = grid.iloc[i,0]  , splitter = grid.iloc[i,1], max_depth = grid.iloc[i,2])\n",
    "\n",
    "    dt = dt.fit(X_train,y_train)\n",
    "\n",
    "    accuracies.append(dt.score(X_test,y_test))\n",
    "\n",
    "    \n",
    "# CHECK ACCURACIES AND WHICH MODEL IS BEST\n",
    "dt_accuracies = pd.DataFrame(accuracies)\n",
    "dt_accuracies = dt_accuracies.rename(columns={0: \"accuracy\"})\n",
    "dt_accuracies['criterion'] = grid.iloc[:,0]\n",
    "dt_accuracies['splitter'] = grid.iloc[:,1]\n",
    "dt_accuracies['max_depth'] = grid.iloc[:,2]\n",
    "dt_accuracies\n",
    "\n",
    "best_dt_model = dt_accuracies.iloc[dt_accuracies['accuracy'].idxmax()]\n",
    "best_dt_model\n",
    "# 0.761664 accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e3fa1bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5487814522196701"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RANDOM FOREST\n",
    "# CREATE MODEL\n",
    "\n",
    "rf = RandomForestRegressor(n_estimators = 2000, random_state = 42, max_features = 'sqrt', bootstrap = True)\n",
    "\n",
    "rf = rf.fit(X_train,y_train.values.ravel())\n",
    "    \n",
    "accuracy = rf.score(X_test,y_test)\n",
    "\n",
    "best_rf_model = accuracy\n",
    "best_rf_model\n",
    "# 0.5487814522196701 accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "26483535",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>importance</th>\n",
       "      <th>cum_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>decade</td>\n",
       "      <td>0.291001</td>\n",
       "      <td>0.291001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>log_duration_ms</td>\n",
       "      <td>0.148391</td>\n",
       "      <td>0.439392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>acousticness_disc</td>\n",
       "      <td>0.076291</td>\n",
       "      <td>0.515684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>key</td>\n",
       "      <td>0.068363</td>\n",
       "      <td>0.584046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>instrumentalness_disc</td>\n",
       "      <td>0.061948</td>\n",
       "      <td>0.645994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>energy_disc</td>\n",
       "      <td>0.055455</td>\n",
       "      <td>0.701449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>loudness_disc</td>\n",
       "      <td>0.046903</td>\n",
       "      <td>0.748352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speechiness_disc</td>\n",
       "      <td>0.043637</td>\n",
       "      <td>0.791989</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  feature  importance  cum_importance\n",
       "0                  decade    0.291001        0.291001\n",
       "3         log_duration_ms    0.148391        0.439392\n",
       "12      acousticness_disc    0.076291        0.515684\n",
       "7                     key    0.068363        0.584046\n",
       "6   instrumentalness_disc    0.061948        0.645994\n",
       "13            energy_disc    0.055455        0.701449\n",
       "9           loudness_disc    0.046903        0.748352\n",
       "10       speechiness_disc    0.043637        0.791989"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Feature Importance values and plots for RF model\n",
    "importances_rf = pd.DataFrame({'feature': X.columns,\n",
    "                            'importance': rf.feature_importances_})\n",
    "importances_rf = importances_rf.sort_values('importance', ascending=False)\n",
    "importances_rf['cum_importance'] = importances_rf.importance.cumsum()\n",
    "importances_rf.loc[importances_rf.cum_importance <= .80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "72b6b107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7AQOBeyUNj4h/1t9J0rHAsQCDBg0q6KnNzAw6d4T+HLBR3fbAbF+9ecCEiFgYEX8GniAl+FYi4vKIGBMRYwYMGNBom83MrB2dSeiTgE0lDZG0CvBpYEKb+9xIOjpHUn9SF8zTxTXTzMw60mGXS0QsknQCcCvQF/hpRMyUdDYwOSImZL/bS9JjwLvA1yPi5TIb3mu1V0ALXETLzDrXhx4RtwC3tNn3zbqfAzg5u5mZWTfwTFEzs4pwQjczqwgndDOzinBCNzOrCCd0M7OKcEI3M6sIJ3Qzs4pwQjczqwgndDOzinBCNzOrCCd0M7OKcEI3M6uIoha4sLbaq4roiohmViIfoZuZVYQTuplZRTihm5lVhBO6mVlFOKGbmVWEE7qZWUU4oZuZVYQTuplZRTihm5lVhBO6mVlFOKGbmVWEE7qZWUU4oZuZVYQTuplZRTihm5lVhOuhu265mVWEj9DNzCrCCd3MrCKc0M3MKsIJ3cysIjqV0CXtI2m2pDmSTl3O/T4lKSSNKa6JZmbWGR0mdEl9gYuBjwFbAodJ2rKd+60JnAT8qehGmplZxzpzhD4WmBMRT0fEO8C1wAHt3O9bwLnAggLbZ2ZmndSZhP5B4Nm67XnZvsUkjQY2ioiblxdI0rGSJkua/NJLL61wY83MbNlyXxSV1Af4H+DfO7pvRFweEWMiYsyAAQPyPrWZmdXpTEJ/Dtiobntgtq9mTWBr4G5Jc4EdgAm+MGpm1rU6k9AnAZtKGiJpFeDTwITaLyPi1YjoHxGDI2Iw8BCwf0RMLqXFZmbWrg4TekQsAk4AbgVmAb+MiJmSzpa0f9kNNDOzzulUca6IuAW4pc2+by7jvrvlb5aZma0ozxQ1M6sIJ3Qzs4pwQjczqwgndDOzinBCNzOrCCd0M7OKcEI3M6sIJ3Qzs4pwQjczqwgndDOzinBCNzOrCCd0M7OKcEI3M6sIJ3Qzs4pwQjczqwgndDOzinBCNzOriE6tWNSVBp9681L75rZ8pv07n/lqya0xM+s5fIRuZlYRTuhmZhXhhG5mVhFO6GZmFeGEbmZWEU7oZmYV4YRuZlYRTuhmZhXhhG5mVhFO6GZmFeGEbmZWEU7oZmYV4YRuZlYRTuhmZhXRqYQuaR9JsyXNkXRqO78/WdJjkqZLukPSxsU31czMlqfDhC6pL3Ax8DFgS+AwSVu2udsjwJiIGAHcAHy36IaamdnydeYIfSwwJyKejoh3gGuBA+rvEBF3RcSb2eZDwMBim2lmZh3pTEL/IPBs3fa8bN+yfAH4XZ5GmZnZiit0CTpJRwBjgF2X8ftjgWMBBg0aVORTm5n1ep05Qn8O2Khue2C2rxVJHwVOA/aPiLfbCxQRl0fEmIgYM2DAgEbaa2Zmy9CZhD4J2FTSEEmrAJ8GJtTfQdI2wGWkZP5i8c00M7OOdJjQI2IRcAJwKzAL+GVEzJR0tqT9s7t9D1gDuF7SVEkTlhHOzMxK0qk+9Ii4Bbilzb5v1v380YLbZWZmK8gzRc3MKsIJ3cysIpzQzcwqwgndzKwinNDNzCqi0JmizWzwqTe3u39uSxc3xMysJD5CNzOrCCd0M7OKcEI3M6sIJ3Qzs4pwQjczqwgndDOzinBCNzOrCCd0M7OKcEI3M6sIJ3Qzs4pwQjczqwgndDOzinBCNzOrCCd0M7OK6DXlc8visrxm1ix8hG5mVhE+Qm9i7R39+8jfzJbFCb0XcjeRWTU5oVth/EVh1r3ch25mVhE+QrcewdcTzDrmhG69WllfFO5+su7ghG7Ww3Tll5C/3HoW96GbmVWEE7qZWUU4oZuZVYT70M2sR3Lf/NI6ldAl7QNcCPQFfhwR/93m96sCVwLbAi8Dh0bE3GKbambWNXrqBeIOu1wk9QUuBj4GbAkcJmnLNnf7AvBKRHwIOB84t5jmmZlZZ3WmD30sMCcino6Id4BrgQPa3OcA4OfZzzcAH5Gk4pppZmYdUUQs/w7SQcA+EXF0tv1ZYPuIOKHuPjOy+8zLtp/K7vP3NrGOBY7NNocBszvZzv7A3zu8V2PKit3T4pYZ23HLj93T4pYZu6fFXdHYG0fEgPZ+0aUXRSPicuDyFX2cpMkRMaaEJpUWu6fFLTO245Yfu6fFLTN2T4tbZOzOdLk8B2xUtz0w29fufSStBKxNujhqZmZdpDMJfRKwqaQhklYBPg1MaHOfCcDns58PAu6MjvpyzMysUB12uUTEIkknALeShi3+NCJmSjobmBwRE4CfAFdJmgP8g5T0i7TC3TRNELunxS0ztuOWH7unxS0zdk+LW1jsDi+KmplZz+Cp/2ZmFeGEbmZWEU7oZmYV0fQJXdL7ursN3S0rv2A9lKTVJfWp2+7jz7VJOqkz+1ZE0yZ0STtJegx4PNseKemSgmIfLGnN7OfTJf1a0ugC4v5c0jp12+tK+mneuMCTkr7XTg2d3LLhqC1126tJGtyscbNYt7fzOt9aQNyy3r87gPoE/j7gD3mDStqh9jnOtteStH0Bcb/czutwfAFxy/xM3NGZfQ3ElaQjJH0z2x4kaWzeuJnPt7NvXJ6ATZvQSUW+9iaboBQR04B/KSj2f0bEfEm7AB8lDbv8UQFxR0TEP2sbEfEKsE0BcUcCTwA/lvSQpGMlrVVAXIDrgffqtt/N9jVrXID+7bzO6xcQt6z3ryUiXq+L+zqtE3yjfgS8Xrf9OsV8jo9p53U4poC4hX8mJLVI6gf0z754+mW3wcAH88TOXALsCByWbc8nFStsmKTDJN0EDJE0oe52N2nYd8Oauh56RDzbpsbXuwWFrsX5BHB5RNws6ZwC4vaRtG72B0D2Qcv9GkfEfOAK4ApJuwLXAOdLugH4VkTMyRF+pazoWu253skmkOVVVlyA9yQNiohnACRtDBQx/raU9w94Q9LoiHg4i7st8FYBcVU/gS8i3stmaufVV9Li2FmXX7N+Jr4I/BuwIfBw3f7XgB/mjA2pJtVoSY9A+nIroM0PAC+Q6rd8v27/fGB6nsDNnNCflbQTEJJWBk4CZhUU+zlJlwF7Aucq1XMv4mzl+8CDkq4HRJo1++28QbM/qE8ARwKDs+e5GvgwcAuwWY7wL0naP5sghqQDKKYAUVlxAU4D7pd0D+l1/jBLir7lUcr7R0o410t6Pov7AeDQAuI+LekrLDkqPx54uoC4vweuy/5GICXN3xcQt/DPRERcCFwo6cSIuKiANra1MPv7q325DaD1WcYKi4i/AH+R9FHgreyLeDNgc+DRPLGbdmKRpP6kRTU+SvojuA04KSJy14hRuiC1D/BoRDwpaQNgeETcVkDsLYE9ss07I+KxAmI+DdwF/CQiHmjzux9ExFdyxN6E9OWwIel1fhb4XM6j/tLi1sXvD+yQbT7UtrJnjriFv39Z3JVJFUYBZkfEwgJirg/8gNTeIPXV/1tEvJgzbh/SF+RHs123kxa2yXWGXMZnQtIeEXGnpAPb+31E/LrR2Fn8w0lfvqNJJcIPAk6PiNzdh5KmkA5G1gX+SCqz8k5EHN5wzGZN6GWTtC6poNjis5TaKXGOmJsA8yLibUm7ASOAK+v7IxuMu0Z9H2wZJK0Bi/t3mzqupJ2BqRHxhqQjSH9sF2ZHPnnilvX+HQz8Prtuc3rW3nPyft66QtbtNDAicnUFtIlZ2GdC0lkRcYakn7Xz64iIowp4js2Bj5C+hO6IiEJ6CiQ9nHXnnAisFhHflTQ1IkY1HDQimuoGXEQ66mj3VtBzfIt0dHA36cj3LtLRWN64U0lfEB8i1Xr/HnBLAXE3Ix19zci2R5COEop4LU4C1iJ9WH9M6ofcq1njZrGnZ3FHZnG/DNzTxO/f9OzfXbLP2ieAPxUQ97vZa7xy9vl4CTiigLh3Z3H7AX8G/gSc38yfiaJv2f99mbeCnuMR0gXXh4Ctsn2P5onZjKNcJgNTgBbSkcyT2W0UxVyYATgE2CQidouI3bPbHh0+qmPvRcQi4EDghxHxdWCDAuJeAXwDWAgQ6WipqAJoR0XEa8BewHrAZ4H/Xv5DujUuwKJIn/4DgIsj4mJgzQ4e0xllvX/1F+GviIibKeazvFf2Gu8LzCV9EX29gLhrZ3EPJJ2hbE86Qs2rtM+EpO+0M9Qyz0CHKSzJRS+RRpk9mf08JUfcev9G+rv+TaSCh0NJX/gNa7qEHhE/j4ifk45Cd4uIiyJd7PgIKakXYQawTkGx6i2UdBjwOeC32b6VC4j7voiY2GbfogLiQjpaAvg46Y93Zt2+ZowLMF/SN4AjgJuzPt8iXuey3r/aRfhDgVsKvAhf6y78BHB9RLxaQEyAlbLrSoew5HUoQpmfiY/F0kMtP95osIgYEhFDSfMF9ouI/hGxHunLM/e1tuw57omI/SPi3Gz76chxPQyaMKHXWZd0elazRravCP8FPCLp1vpxoAXEPZJ0CvXtiPizpCHAVQXE/XvWv1u70n4QadhTEaZIuo304b9VaaJKrqv4JceFlBjfBr4QEX8lLbryvQLilvX+HUIqP713lnT6UcyR9G8lPQ5sC9yRjcBYUEDcs0ntnRMRk7IjxycLiFvmZ6Jv9kUJpElLwKrLuX9n7RARt9Q2IuJ3wE55Akq6IPv3pjbj0HPnoaa9KCrpSOBM0imISJOKzsyO3vPGnglcRhoitPgDFRH35I1dBqWZad8hfZBeIfVrXhcFDNPKjm5HAU9HxD8lrQd8MHJeBCsrbk8iaa2IeC27sLiUiMg1iSR7jn7AqxHxbjZ6a63sS67plPmZkHQKsB9Quzh6JDAhIr6bM+6twH3A/2a7Dgf+JSL2zhFz24iYojSnZCl58lDTJnQASR8AalOZ/1TUB1XSpIjYrohYWbxfRsQhkh6lnQkuETEiZ/yHSdOEnyadVe1LGp7W8DRvSZtHxONaRsmDaHAERllxs9j3R8QukubT+nVWCh0NzZ4t6/2T9NuI2FfSn7O49d0LkZ3SNxK3lKF6kv4j0kiLi2j/dWioO6DMz0Sb5/kYS/r6b4+IIspB9APOYMks9XuBswr6Mj4p0jj65e5boZhNntDXBTYlXSAFICLuLSDu/5BO2Sdk/9ZiN5rENoiIF5RmLC4l8g+nG0qaIv0Z0rjVz5H69RruM5V0RUQcI6m9izDR6EXisuKWqez3r2hlDdWTtF9E3CSpvRojNHp23BM/E21l3UMRxQ6/fTgiRrfZ90hENFxuomkTuqSjScOcBpKGk+0APFjEm98TP1hKM8luBJ4BPhkRRUwd71GW1XVRU8RRU5GWdURaU9SRaW9W1llbXfzhwJWk6x6QZrZ+PiJm5Ih5GEsOzuoPUNckjbRqeERRMyf0R4HtSLMAR2WD+78TEe2eZhb83J9fkaORdj5MreToCmjbBbA+8CrZWUWerpxlna7X5DhtLyVuFru+62IQ6XqCSCOWnomIIQ3GLev9qx04tABjgGmk9o4grce7Y4NxT17e7yPifxqMexPLfx32bzBuaZ+Jskl6ADgtIu7Ktncj5aGGL4xmZ4JDSIMzTq371XzSnIWGR7A1cy2XBRGxQBKSVs364IZ1/LBCnESa5tspEVErxfst0uiTq0h/uIeTbxzzvjke25H9sn/XJ11svTPb3p1UPKjRP7Ky4lJL2JKuII3dvSXb/hjwrznilvL+RcTuWdxfA6Mj4tFse2vSBf9G1cbcDyMd9NRGRuwHtB3euiLOy/49kFRvpnYh8DDgbznilvaZ6IKzttVryTyLd7ek1fMEjIi/SJpHynHFDsSIJpiV1d4N+A3pyOtM0mnJ/1HArL1OPvcjDT5uWmf2NdONNKZ2g7rtDYBbmzVuFmup2XTt7WuW9w+Y2Zl9DcS9F1izbntN4N4C4k7uzL5m+EyQRnw9nf3b9vZ0AW3+DfCfpKJ4g4HTSQcTueJmse8gTeLKHat2a9oj9Ij4ZPbjmdmp69oUU/GtU0/f4OPeUCrmc20W4zDgjcJaVY6NIqJ+TPvfSN0ZzRoX4Hmlmij1Q8meLyBuWe/fdEk/pnV7ixi++X7gnbrtd7J9ea0uaWhEPA2QjcfPdVSaKfwzEQ12s62Ao4CzgF9l2/eRhkQW4XXgUUm3U/c5ixyTi5o2oUvagXQUMz8i7lFa0GEbUl2J0p++wcd9hlQh8kJSQvhjtq+Z3ZGNtf1Ftn0oBaymU2JcSIn2DNLRU5COVA9b7iM6p6z370jgS6SuPEjtLWIhiiuBiZJ+k23/KzC+gLhfBe5WqvIpYGOKKU9c5mei1le/C+m9uy8ibiwg7CakIn59SPnyI6TqlrmGImd+TY7upvY080XRR0j9jrXZkX1Ip325l4rrxHP/MCJOKCHuNyLiv4qOm5ekT1I3zjYifrO8+3d33E4870URcWIJcUt5/yT9KiI+1eBjR5NGS0B6jR+p+93ixToaiLsqqT43wOMR8Xbd7/aMiNsbjFvWZ+0SUi2b+i+LpyLiyznjzga+RioXUj8JsamGstY0c0KfGm3KSEqaHjkn6WRxVgU+ReoTqy+fe3be2B0871LjTpudpAejwdEY3RE3i13K61xi3Ecix9jj5cTtaa9Dw58JpRIIW7Q5AJwZEVvkbNP9EbFLnhjLib0paaTLlrSea9PQhDNo4i4XyluNBdIF1ldJVdPe7uC+RSqqEFFXaun4Lk0Vt0xlvX9lHVWV1d6y4ub5TMwh9cfXjpw3yvbldUZ2/eMOWk9CLKKr5GekrsPzSSN+jiRnfa1mTujHkWqgn86S1ViK6MeDVLB/n4JirYjmPB1avrLa7NeifD3tvcsTd01glqSJWZyxwGRlxa6iwTH0pCS7OanqZq3LJSim73u1iLhDkrIunDOVVjH6ZqMBmzahR1pGq6ia3209IGl4ZOOCu1BPPELviXrakak/F/k1nAQ7sF1ElDX/5e2sa+hJSScAz5GqyjasacvnStpM0h2SZmTbI7KhakXYhVTKc7ak6ZIeldQVVQBzr0PYDZo6iUnqk42AqtdwcaMOFLGO5LqS2l4HOiVv3GU9XUlx55YUN097X4pUX3zxjXSNsPZzox5QWme2DCcB7wO+QiqBfASpCF/jihzUXuQNuId02vRI3b4ZBcXeuL1bAXFLWRKs5Nd5daBP9vNmwP7AynW/37rBuCcC6y7n9w3FzR57TfY6rw48BswDvt6s7x/tL+n2PwW9f7sAR2Y/DwCG1P2uoaXSSNeWvry89y9He99PmgG9L7B+gZ+JGcB/kL4UViMtZflgAe2dRRrfP5s0d+BRsiUFy74BF63oY5r2CJ0SV+mJ1F+1DmlK8n7AOlHMMKSylgQr071Ai6QPkmbyfZa6sczReBGi9wOTJP1S0j6SWh195YgLsGX2Ov8r8DtSXYzP5ohX05VLun00b1BJZ5CO7r+R7VqZJZOXiManvR8KbEh6/66VtHfb968Rkg4hlSY4mLTox5+UFmuptTfPZ2J70kXRB4BJpIlmO+eIV7MPqeLrXqRcsS9LShmUbYXb38wJvbRVeiSdBFxNqi2xPvC/Sitv51XWkmBlUkS8SUo2l0TEwcBWeYNGxOmkP4SfAONI/YTfyd7TvFaWtDIpoU+IiIUUc6Gupy3p9knSGdUbABHxPAWsrRoRcyLiNNIZ2zXAT4G/SDqro9opHTiN1Cf9+Yj4HOkM/D/ztjezEHiLdHTeAvw5InKvhhQRf2nvljduWZo5oX+ZtKrQ5pKeIy2oelxBsb8AbB8R34yIb5JK8x5TQNyylgQrkyTtSJqOfnO2r28RgSOdN/41uy0iLSF4g6Rcq8iQPhdzSV0u92bV617LGRN63pJu72Svce2gp4jp+WSxRgDfJy3t9yvSUfVrLCms1Yg+kQY71LxMcTloEimhjyFNtDpMUk+8ZpVL000s0tKlQVcjvem1o5CGSoO2eY5HSUcKC7LtFmBSRAwvIHaPWRIMQGkZrH8H/hgR52bJ5t8i52K12VnQ50j1o38M3BgRC2tX9SOiiCP1+udbKXKUHa2L02PeP0lfI50F7UmaoHIUcE3kXJowGzr3T9LZ1a+i9SzRX0eDJawlfY80Zb5+Nuf0iMh9UVhpmcZhpGsIZ0saBHwuIs7JG7u7NDLhrBkT+hnZj7XSoP9HutCxHzAxIo4o4DlOJl1NblUDIyIuyBn3YOD3ETE/G5EzGjgneshCBlmyXSPr780b6yzgp+2dnkraIiJm5Yh9EmlSxnzSl8U2wKkRkWs19rLev+yM5BzSEeTvSUntqxHxv8t9YOdi70nq3xWpcmFDU/LbxFxcmKtu35CI+HMBsWv1ViDVWylq6v+PSOPE94iILZRWO7stClxqsqtJGhcR41foQV1xtbaRGyWVBq2LN5o0XOgrwDYFxZye/bsLaWTDJ0hroXb767mcNpc1YqRfO7eV88bNYk/L/t2bNMFjK+DhZn3/gKnZv58kHfWuTTFleYcALXXbqwGDC4i71GsJTCnovfsAcADpAO0DRcSsbzOtR8VNLSp+kTfgguzfm0i17Fvd8sRu2olFlFAaVK1XYZ9L3XhaSf0ifzH8d7N/PwFcHhE3S2r2U74ts9fkcNKIkVNJw9a+lzPuw6Tp16+wZFWhv0r6G3BMREzJEbs24uLjwFURMbOIURiU9/4tdbG1mOZyPWnBiJp3s30NHZUqrQq2FbC2Wq8ytBYFlGpQWlbym6R+eAEXSTo7In6aNzawUFJfllxPGEDzzuy9Kvv3vOXeqwHNnNDLKA16DWnY0RTaWX8QaLgoTuY5SZeR+jTPVSoC1swXnqH1iJEfRurnLuIP4XbghshWXpe0F6kg2s+AS0jDzBo1RdJtpCPUbygt4Jt7RAPlvX+1i61vAV8q8GLrShGx+KAnIt6RtEqOeMNIfx/r0Hpo3nyKGTTwddLZ8MsAktYjDTMsIqH/gNSFur6kbwMHkcqGNJ26g5lREdFqElzWndj4RKjuPv3o4NRkNGk21UkU1C1ScnvfRxr+t2m2vQFpbHO3t205bf4KacrxLSypfX1fAXHbW1Wo1qUxNWfsPtlnY51sez1gRDO/f6Qup77Zz6tTQHcD6Utz/7rtA4A7Coi7Y0mftQeAVeq2VwEeKDD+5qTRcSeQKi8W/n8o+PVor2vrkTwxm+6iaFeQdEe0WVm7vX0Nxt6FlBB+lh2JrREFXEzqSkWMGMmOoO8grf4DaUTDnqSJGpMiR/nVrHvlcGBoLBnR8IFYeiJaI7ELf/+y0TInA4Mi4lilsqnDIiLXmPRsTP/VpElAAp4ljexoqMqgpP+IiO9Kuoh2uisi/8inK4HhpIEOQfoCmp7diAJGsPUEkg4jLZyyC2kFpJo1gffy5KFm7nIpXDY88X1A/+wqeK0jcy3ggwXEP4M0DnYYqWuhNnOviBlrpZD0fuA7wIYR8TGluhU7ki7e5fEZUmnQG2m9+k9f0gSbPC4hG9FAGuM9nzRWOteIhhLfv5+Ruvlq/d3Pkfq6cyX0iHgK2EHSGtn263nikaa5A0zOGWdZnspuNf+X/Zt7MlQP8wBpkmR/0lj/mvnkXJqwVx2hZ/1T/0Y6onmOJQn9NeCKiPhhzvhTSUPoHo5s/KgKWpSjLJJ+R0o4p0XESEkrkU77Gh6Tn12cujIiDi+qnW3iPxwRo+vH6UqaFhEjc8adSgnvn6TJETGmhPZ2y0It1rx61RF6pAsQF0o6MXJOvliGdyIiahcVi5y5V6L+EfFLSd8AiIhFkt7t6EHLE2lSzsaSVom6i3YFam9EQxEXRct6/96RtBpL2rsJxSysUuhCLZJuYjkjQ6LxmuK1+GNI0/83pvUXUNMe8JRB0nzaf51FmmDdtnpop/WqhF4TERdJ2pqll366MmfoX2ajJNaRdAxp5t4VOWOW7Y1stEEt2exAShJ5PQ38UWmBgfoVzYvoJy1rRENZ798ZpAlFG0m6mtSFM66AuEUv1FL4MLo2riaNdHmUYr6Ae6SIKK2LqVd1udRkfaW7kRL6LcDHgPsj4qDlPa6TsQufuVcmpUWGLwK2JpUgHQAcFBH5+vKWzPhtJSLOyhO3Lv7mpBXYRRrZ0fDM0zZxS3n/si/NHbK4D0XE3wuIeTmpxGpXL9TSEJW4PmdPlF3MX0pEPNNwzF6a0B8FRpL6ikdmFwb/NyL27OamdYus33wYKdnMjlS9sKjY74tUzbFQWZfL+2l96t7wH0LZlMoTt+1quDdnzMdIJX7/TOpyqZ2yN9SFIemXEXFI9vex1DyNAq4lfAQ4jHLW5+xxste5poU0r2J2RDRc7bRXdrkAb0XEe5IWKa128yJpVmMu2ey6c0kleUUBfWJdZCxLLqyNlpS7+0mpguNPSEtqDZI0EvhiRByft7FKpY7PAP5Gmh1ZmxiWN+GU8v5JOpc0bHMmrdelzJXQSWeWRTop+3ffguPWlLk+Z4/TduBBdrac6++jtx6hXwL8P9Kapf8OvE6a7HJkzrhzgP2KOv3vCpKuAjYBprJk6nsUMOb4T6S+7Ql1IztmRMTWeeJmceaQyh+/nDdWO3ELf/8kzSZNfCriQmjb2KXMe5D0AdIXfZDmDeSuOClpdpS3PmclSHo0zwizXneEnk1K+a+I+CdwqaTfk0qkFrGm6N96UjLPjCHVcyn8mz0inlXrmiW5Rs/UeZZiLty2Vdb79zTpqLTQhF7WuHmVV3PlAUlbRsRjOeNUglqXCu9DqsP/fJ6YvS6hZ8PSbiHNWCMi5hYYfrKk60iTaXpKH+EMUgW8QlaDqvOspJ2AUKoVcxJLJq7k9TRwt6Sbaf065x1BU9b79yYwVVLbvuNcZ0Gk6o3bkAqhERHPK9W1yausmis7kF6HQvr8K2BNllyrWESqvvirPAF7XULPPCxpu4iYVHDctUh/vHvV7Wv2PsL+wGOSJtI62eQac0xaXepC0gzc50jrlX45Z8yaZ7LbKtmtKGW9f7XSqEUra9z8y6RZizXzs315FTnEsgpuIXX9DmZJLj6VHNeCemsf+uOk0QF/IY2R7rVHCkorFi0lIhqv+GZLySYWDYqI2QXGLHTForougFG0U3MlIsblbHIlah0VJbu28jXSWfLicfmRY83S3prQN25vf54XMov7c+CkrH+erF7M9yPiqDxxe6Lsj/UYlp6W3vBrUdZMRpVflGo/0qSdVSJiiKRRwNkFnAUVOm5+WXMHavLOIajv84+IzSRtSKoP37S1jspUxrj83trlUta32IhaMgeIiFckrdCagF2lzOnHmf8jVZL7A8VdDK3NZDyQ1O9fW8LtMNIQxkaVXZTqTNKIkbsBImKq0tqtuWUJvJDJT0VN+lqOsvr8e6ozJP2YAsfl99aEfjMpmYm6Af2k1Vry6CNp3Yh4BUBpZaSmfI3LnH6ceV8UsPhvvVo3kKTvR8SYul/dJKnhZBwRN2X//jxnE5dlYSy9SlHDU9/L/jLOzq7+g/T3UF8aY488cemZtY7KVPi4/KZMNmUrY0B/5vvAg5KuJ/1xHQR8u4C4pZO0Pq3/ePPOuvytpI9HxC0547RnddUtZCxpCGnRiFwkbUbq0xxM626ivIlspqTPAH2VaqF/hTRqpCFd8GV8NXAdaYLRcaQF1V8qIG5PrHVUpu2KHpffK/vQ25N3QH9dnC1JdboB7mz2MbeS9id9EW1ImjG7MTArz/TjLO58Uu35d4CFFDhrVtI+wOWk4Yu1VZa+GNlydzniTgMuJVUvXNxNFPnWP60tcHEaS0bP3AqcExFFLENX+JexpCkRsa3qSgdLmhQReevNn0vqglvc5w98tOgzuZ5C0s+A7xWZI3plQm9nQP9oYL2I2LvBePWLTy8l8i8+XZosie0B/CEitpG0O3BERHwhZ9w+pFWFhsSSVYU2iIg/5W/14lrgm2ebjxcxC7OWyPLGaSfu6Ih4uIS4ZX0ZPxQRO0i6lVTZ8nnS+rCb5Iz7cLRZqUpNvl5AmSTNIs3SLmxcfq/scqH1CimLSH3qeQb0l734dJkWRsTLkvpI6hMRd0m6oIC4F1PCqkIAkj7XZtfIPPVn6r6Ib5J0PKk0b/1FqrxfyN/PptLfAFwXETNyxqv5FmmyTqsv4wLiniNpbVJZjItI4/O/2mgwSV8idWkOlVQ/I3tN0kpWvVXh4/J75RG6LSHpD8C/ksYx9ycd6W0XETst73GdiFvKqkJZnPpx1i2kMroPR4Plj7OZi7WL5G1FROT+Qs4S+iGkIl1rkRL7OTlj1lZCmkaa2fleUa9xkbIvh3VJn7FT6341v5nPXnuiXpXQyxrHXBe/tMWny5KNNHiL1PV0OLA2qZRwrj80peJcO5EtCJ2NnLitltyLJGkd4NoodrGHUkgaThpBcmhE5JrlWvdl/N/AehT3ZbwZ8CPg/RGxtaQRwP55v4CsfH26uwFd7DxSn+OfSUnsiuz2Oq0Xr10hklqy0/b+ktaV1C+7DaaAxadL9s2IeC8iFkXEzyPiB0ARF6narip0P2kx6jK8QRp6moukL2dfDrXtdbMumLxxt5B0plL964tII1wG5o0L3EX6Aj6JtCLSU8B+BcS9AvgG6WI2kQrXfbqAuFayXnWEXlM7Ve1o3wrEK3Xx6TKVeaFK5a0qVH+m1RfYAvhlRJy67Ed1Ku7UiBjVZt8jec8qJD1IGgb4y4jIVU2vTdwzSN04/8jiXx8ReSZY1eJOiojt2nSXLfXaWPPprRdFCx3HHOUvPl24rrhQFRGPA48XEauN+rUvFwF/iYh5BcTtK0mRHeUorYqUu/hXROyYu2Xtxz0LOCvrEjkUuEfSvIj4aM7Qf1dayLr2OhxE8dU4rQS9NaF/lVR+tX4c87EFxP2rpDUjYr6k00nDIc8pY8haAa4BfkcPvFAVEfcoLRtYGzHzZEGhfw9cl01+Afhitq8hKnlJtzovAn8lVURcv4B4XyaN899c0nOkLsoiRs9YyXpllwssfxyzpD2jgSJHta4KpYpy5wDfI/VRb19Io0uinrc+5yGk1/ZuUnL8MPD1iLghZ9w+pCReu4h9O/DjiGioFo2kDSLiBZVXDO54UpfLAOB6UpdOcZNU0gXzPhExv8M7W1PotQl9edrrV+7k4x7JxgP/F/BoRFxTRB9smSSdQCoe9Tfq6kk082SPbJjenhHxYrY9gDQWu4ghkYWXuS1L9jm7LiKmFhz3O8B3o3XV0H+PiNOLfB4rnhN6OxpNwpJ+S7oouiepu+UtYGKzjQuup5LW5yyT2pRpyI6sp0XO0g3ZzMvvUVCZW5Vf0bIU7X3+Gz3Isa7VW/vQO9Lot9whpNlf50XEPyVtQFrOq5mVtT5nmX6fTUv/RbZ9KGn1l7zOYOkytw0Ph4zyi2iVpa+kVWvdkNlZy6rd3CbrBCf0YvUnq6mtVLsEyhnlUaSy1ucsTUR8XdKnWLIY8uUR8ZsCQrdX5rY3nsJeDdyhVDwKUpnXskoLW4Gc0Ns3t8HHlVVnvUxlrc9Zqoj4FTkX1G1HoWVue6qIODcbylq7OPytyFnJ0rpGr+xDl3RgO7tfJV3IfLHA5xkNHB8RRxcVsyyS3hcRb3Z3O5an7D5plVzm1qxsvTWh3wzsSJo6DbAbqVLiENJFsKsKfK5C6qyXRdKOwE9Ii/UOkjSSVFu8iAU/eqSe8OVWpuyA51zSmHbR5BdxbYne2uWyErBFbZp0NknlSmB74F6goYSu9uusFzbVuyQXAHsDEwAiYpqkf+nWFnUTSTsBPwbWAHrzl9t3gf2KKtVgXae3Feeq2ahNzYsXs33/ICtI1KA1626rkvrUD8gRr0tExLNtdhW1qHNPcz7py+1lSF9uQG/8cvubk3nP1FuP0O/Oxoxfn20flO1bHfhno0Gz2hpIWiPbfj1nO7vCs9mRaUhamVS5r9f+MUfEs21GufTGL7fJkq4DbqSg1eita/TWhP5l4EBgl2z758CvsqJMuzcaVNLWpO6aftn234HPR3Er1JThOOBCUpnf54DbSK9Pb+Qvt2Qt4E2WXByGnKvRW9folRdFYXG/+VjSB3ViEaNbJD0AnBYRd2XbuwHfiZwLDljXkNSf9OX2UVJ35K3AST1pFq31br0yoZdY3Gmp5b/a29dMspmQJwKDaV2cK9fqTdZzSWoBvkCaP9FS2x8RR3Vbo6xTemuXy2mkpbpaFXciLeKbx9OS/pMlo2SOIM3EbGY3koYt3sSS4ly9kqShpCP0HUhnbg8CX63Vze9FriLNcN6btMD34fTOrqcep7ceoZdV3Gld4CxS33wA9wFnRcQreeKWSdKfmr28b1eR9BBwMUtqxHwaOLG3vT51VUNr5aBXBu6LiB26u222fL31CL2U4k5Z4v5K3jhd7MJsKbPbaD2ioRkX5Sjb+9pMKvtfSc1eXK0MtaG7/8wu9P+VYhbOsJL1yiN0gDbFne4roriTpNuBg9vUkb42IvbOG7ssWU3tz5IWGK6vh75H97Wqe0g6F3gFuJZ0hnUosC7pegvNvpJTUSQdTaqTMxwYT5po9Z8RcdnyHmfdr9cm9DIso450sy9wMQfYMiLe6e62dDdJf67brP1h1AalR0QM7eImdak2M50X787+jWauwGlJr+py6YIFB96TNKi2fJukwct4vmYyA1iHNFu2tzsF+H1EvJZd3B5NqjTYW7qfavXbh5HWa52Qbe8HTOyWFtkK8RF6gSTtQ1pc9x6WDIc8tplLj0q6GxgBTKJ1H3qvG7bYZk3YbwHn0QPWhC2apHuBT9TWEpW0JnBzRPTGMgg9Sq86Qi9bRPxe0hjgWOAR0pDAt7q1UR07o7sb0ERq0/w/AVwRETdLOqc7G9RN3g/Ud8G9k+2zJueEXqDsYtJJwEBgKmk884NA015gjIh7ursNTeQ5SZeR1oQ9V9Kq9M4CdlcCEyXVBgr8K+niqDU5d7kUSNKjpL7HhyJilKTNSVP/21tQoym0ua6wCrAy8EZvrH2dLXCxD2mhkyezNWGHR8Rt3dy0LpctzvLhbPPeiHikO9tjneMj9GItiIgFksgW2X1c0rDubtTy1C9krFRm8ADSmUWvky1q8eu67ReAF7qvRd0nuxDcWy4GV0ZvPJ0s0zxJ65D6zm+X9H/AX7q1RSsgkhtJU77NrIdxl0tJJO0KrE0aBte0Y7zbrK/aBxgD7BoRO3ZTk8ysQe5yKUkPuti4X93Pi4C59IBVlsxsaU7ovZikvsD0iDi/u9tiZvm5D70Xi4h3gcO6ux1mVgz3ofdyks4nDVW8Dnijtr8XTXc3qwwn9F5O0l3t7O6V1RbNejon9F5O0tC2K/K0t8/Mmp/70K29Zfeu7/JWmFluHuXSS2VlCbYC1m4zFn0t6hYGNrOewwm99xoG7EuqhV4/Fn0+cEx3NMjM8nEfei8naceIeLC722Fm+bkP3T4paS1JK0u6Q9JLko7o7kaZ2YpzQre9IuI1UvfLXOBDQG9c6d6sx3NCt5Wzfz8BXB8Rr3ZnY8yscb4oajdJepy0VN6XJA0AFnRzm8ysAb4oakjqB7waEe9mq/asFRF/7e52mdmK8RG6AWwODJZU/3m4srsaY2aNcULv5SRdBWxCWtS6tup94IRu1uO4y6WXkzQL2DL8QTDr8TzKxWYAH+juRphZfu5ysf7AY5ImAm/XdkbE/t3XJDNrhBO6ndndDTCzYrgP3cysInyE3ktJuj8idpE0nzSqZfGvSCsWrdVNTTOzBvkI3cysIjzKxcysIpzQzcwqwgndKkfSVyTNknT1Cj5usKTPlNUus7I5oVsVHQ/sGRGHr+DjBgMrnNAl9V3Rx5iVwQndKkXSpcBQ4HeSTpP0U0kTJT0i6YDsPoMl3Sfp4ey2U/bw/wY+LGmqpK9KGifph3Wxfytpt+zn1yV9X9I0YEdJR2TPM1XSZU7y1h2c0K1SIuI44Hlgd2B14M6IGJttf0/S6sCLpCP40cChwA+yh58K3BcRoyLi/A6eanXgTxExEng5i7NzRIwiFTlb0bMDs9w8Dt2qbC9gf0lfy7ZbgEGkhP9DSaNIyXezBmK/C/wq+/kjwLbAJEkAq5G+NMy6lBO6VZmAT0XE7FY7pTOBvwEjSWepy1qhaRGtz2Jb6n5eEBG1csMCfh4R3yii0WaNcpeLVdmtwInKDpslbZPtXxt4ISLeAz4L1Pq75wNr1j1+LjBKUh9JGwFjl/E8dwAHSVo/e55+kjYu9H9i1glO6FZl3yItgj1d0sxsG+AS4PPZBc3NgTey/dOBdyVNk/RV4I/An4HHSP3sD7f3JBHxGHA6cJuk6cDtwAbl/JfMls1T/83MKsJH6GZmFeGEbmZWEU7oZmYV4YRuZlYRTuhmZhXhhG5mVhFO6GZmFfH/AfXsX+2ksnhxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp_rf = importances_rf.set_index('feature')\n",
    "imp_rf.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training and holdout\n",
    "X = music_disc.loc[:, music_disc.columns != 'popularity_disc']\n",
    "y = music_disc.loc[:, music_disc.columns == 'popularity_disc']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "modelSVC = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/utils/validation.py:63: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  return f(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the Model\n",
    "\n",
    "# X_train, X_test, y_train, y_test\n",
    "\n",
    "modelSVC.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make predictions with the model\n",
    "\n",
    "predictions = modelSVC.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Measure the performance of our model\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.68      0.95      0.79     24628\n",
      "           1       0.75      0.74      0.74     18398\n",
      "           2       0.00      0.00      0.00      8609\n",
      "           3       0.00      0.00      0.00       682\n",
      "\n",
      "    accuracy                           0.70     52317\n",
      "   macro avg       0.36      0.42      0.38     52317\n",
      "weighted avg       0.58      0.70      0.63     52317\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# precision    recall  f1-score   support\n",
    "#\n",
    "#           0       0.68      0.95      0.79     24628\n",
    "#           1       0.75      0.74      0.74     18398\n",
    "#           2       0.00      0.00      0.00      8609\n",
    "#           3       0.00      0.00      0.00       682\n",
    "\n",
    "#    accuracy                           0.70     52317\n",
    "#   macro avg       0.36      0.42      0.38     52317\n",
    "#weighted avg       0.58      0.70      0.63     52317"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[23294  1334     0     0]\n",
      " [ 4809 13589     0     0]\n",
      " [ 5380  3229     0     0]\n",
      " [  614    68     0     0]]\n"
     ]
    }
   ],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#[[23294  1334     0     0]\n",
    "# [ 4809 13589     0     0]\n",
    "# [ 5380  3229     0     0]\n",
    "# [  614    68     0     0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8cbb02c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e32c8417",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.704990729590764\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ac5a04e",
   "metadata": {},
   "outputs": [],
   "source": [
    "######### Get the VIP for SVM ############"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "462bd8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split into training and holdout\n",
    "# X = music_disc.loc[:, music_disc.columns != 'popularity_disc']\n",
    "# y = music_disc.loc[:, music_disc.columns == 'popularity_disc']\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "ada87ddb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method BaseSVC.decision_function of SVC()>"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelSVC.decision_function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "cc022380",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ovr'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modelSVC.decision_function_shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "ced23b3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedShuffleSplit\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d760a141",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e521aa56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d4d86b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "b3afd4eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cat Boost\n",
    "\n",
    "from catboost import CatBoostClassifier\n",
    "XGcat = CatBoostClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "fbc69056",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate set to 0.5\n",
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      "99:\tlearn: 0.5391888\ttotal: 2.04s\tremaining: 0us\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x15549d3d0>"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XGcat.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "8d5b9f65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.86      0.86     24628\n",
      "           1       0.71      0.81      0.76     18398\n",
      "           2       0.62      0.47      0.53      8609\n",
      "           3       0.42      0.09      0.15       682\n",
      "\n",
      "    accuracy                           0.77     52317\n",
      "   macro avg       0.65      0.56      0.57     52317\n",
      "weighted avg       0.76      0.77      0.76     52317\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# X_train, X_test, y_train, y_test\n",
    "print(classification_report(y_test,XGcat.predict(X_test)))\n",
    "\n",
    "#                precision    recall  f1-score   support\n",
    "#\n",
    "#           0       0.86      0.86      0.86     24628\n",
    "#           1       0.71      0.81      0.76     18398\n",
    "#           2       0.62      0.47      0.53      8609\n",
    "#           3       0.42      0.09      0.15       682\n",
    "\n",
    "#    accuracy                           0.77     52317\n",
    "#   macro avg       0.65      0.56      0.57     52317\n",
    "#weighted avg       0.76      0.77      0.76     52317"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "bcbea0c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get importance\n",
    "importance = XGcat.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "6e043e88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature: 0, Score: 39.11491\n",
      "Feature: 1, Score: 3.54325\n",
      "Feature: 2, Score: 1.60405\n",
      "Feature: 3, Score: 9.96120\n",
      "Feature: 4, Score: 5.46436\n",
      "Feature: 5, Score: 1.41319\n",
      "Feature: 6, Score: 9.09986\n",
      "Feature: 7, Score: 3.86759\n",
      "Feature: 8, Score: 4.14806\n",
      "Feature: 9, Score: 4.47113\n",
      "Feature: 10, Score: 5.03660\n",
      "Feature: 11, Score: 3.68511\n",
      "Feature: 12, Score: 4.36306\n",
      "Feature: 13, Score: 3.14006\n",
      "Feature: 14, Score: 1.08755\n"
     ]
    }
   ],
   "source": [
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "1a60bae6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8a1d3d54",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nJ/mnyUY6zF5gb1UdevdyG/1CX0leC3yrqmaq6n+B24HfmHCmI3kyyVqA7uuBCecZKMnvAm8A3lor7+KVl9D/g/1A9/uzHrg/yS+Ne8fHQoGv+Ev6k4T+vO2eqvrgpPMMUlXXVtX6qtpA/9/wi1W1oo4cq+oJ4LtJzuqGLgAenmCkQb4DnJvkBd3P/QJW2Aetc9wJbOmWtwB3TDDLQEk205/ae2NV/c+k88xVVQ9W1S9W1Ybu92cv8Mru/+tYNV/g3Ycbhy7p3wPcOuFL+gc5D7ic/lHtzu5x0aRDNeoPgZuT7AI2An8z2Tg/rXt3cBtwP/Ag/d+xFXEpeJJbgK8CZyXZm+QK4DrgwiSP0H/3cN0KzPh3wIuBHd3vzj+swIyTybLy3o1IkobR/BG4JB2vLHBJapQFLkmNssAlqVEWuCQ1ygKXpEZZ4JLUqP8DJawjMF/iPTMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance\n",
    "pyplot.bar([x for x in range(len(importance))], importance)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "14dbec7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.head of         decade tempo_disc multi_artist  log_duration_ms danceability_disc  \\\n",
       "0         1920          4            0        12.033699                 3   \n",
       "1         1920          1            0        11.919723                 4   \n",
       "2         1920          2            0        12.006566                 3   \n",
       "3         1920          3            0        12.952967                 4   \n",
       "4         1920          3            0        12.015057                 4   \n",
       "...        ...        ...          ...              ...               ...   \n",
       "174384    2020          3            1        11.902363                 4   \n",
       "174385    2020          2            0        11.882556                 1   \n",
       "174386    2020          2            0        12.292924                 4   \n",
       "174387    2020          4            0        12.404924                 1   \n",
       "174388    2020          2            0        12.194557                 4   \n",
       "\n",
       "       explicit instrumentalness_disc key liveness_disc loudness_disc  \\\n",
       "0             0                     0   5             3             2   \n",
       "1             0                     1   5             1             3   \n",
       "2             0                     0   0             4             2   \n",
       "3             0                     4   2             1             3   \n",
       "4             1                     0  10             3             3   \n",
       "...         ...                   ...  ..           ...           ...   \n",
       "174384        0                     0   6             2             3   \n",
       "174385        0                     0   4             2             2   \n",
       "174386        0                     4   4             1             2   \n",
       "174387        1                     0   0             1             2   \n",
       "174388        0                     4   7             2             2   \n",
       "\n",
       "       speechiness_disc valence_disc acousticness_disc energy_disc  mode  \n",
       "0                     4            3                 4           1     0  \n",
       "1                     3            4                 3           2     0  \n",
       "2                     4            3                 4           0     1  \n",
       "3                     2            0                 0           3     1  \n",
       "4                     4            1                 2           3     0  \n",
       "...                 ...          ...               ...         ...   ...  \n",
       "174384                2            0                 0           4     0  \n",
       "174385                2            1                 3           1     1  \n",
       "174386                0            3                 3           2     0  \n",
       "174387                2            2                 4           1     1  \n",
       "174388                0            4                 2           2     1  \n",
       "\n",
       "[174389 rows x 15 columns]>"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X.head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "ceb30490",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>decade</th>\n",
       "      <th>tempo_disc</th>\n",
       "      <th>multi_artist</th>\n",
       "      <th>log_duration_ms</th>\n",
       "      <th>danceability_disc</th>\n",
       "      <th>explicit</th>\n",
       "      <th>instrumentalness_disc</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness_disc</th>\n",
       "      <th>loudness_disc</th>\n",
       "      <th>speechiness_disc</th>\n",
       "      <th>valence_disc</th>\n",
       "      <th>acousticness_disc</th>\n",
       "      <th>energy_disc</th>\n",
       "      <th>mode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1920</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12.033699</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1920</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>11.919723</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1920</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12.006566</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1920</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12.952967</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1920</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>12.015057</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174384</th>\n",
       "      <td>2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>11.902363</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174385</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11.882556</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174386</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12.292924</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174387</th>\n",
       "      <td>2020</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>12.404924</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174388</th>\n",
       "      <td>2020</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>12.194557</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>174389 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        decade tempo_disc multi_artist  log_duration_ms danceability_disc  \\\n",
       "0         1920          4            0        12.033699                 3   \n",
       "1         1920          1            0        11.919723                 4   \n",
       "2         1920          2            0        12.006566                 3   \n",
       "3         1920          3            0        12.952967                 4   \n",
       "4         1920          3            0        12.015057                 4   \n",
       "...        ...        ...          ...              ...               ...   \n",
       "174384    2020          3            1        11.902363                 4   \n",
       "174385    2020          2            0        11.882556                 1   \n",
       "174386    2020          2            0        12.292924                 4   \n",
       "174387    2020          4            0        12.404924                 1   \n",
       "174388    2020          2            0        12.194557                 4   \n",
       "\n",
       "       explicit instrumentalness_disc key liveness_disc loudness_disc  \\\n",
       "0             0                     0   5             3             2   \n",
       "1             0                     1   5             1             3   \n",
       "2             0                     0   0             4             2   \n",
       "3             0                     4   2             1             3   \n",
       "4             1                     0  10             3             3   \n",
       "...         ...                   ...  ..           ...           ...   \n",
       "174384        0                     0   6             2             3   \n",
       "174385        0                     0   4             2             2   \n",
       "174386        0                     4   4             1             2   \n",
       "174387        1                     0   0             1             2   \n",
       "174388        0                     4   7             2             2   \n",
       "\n",
       "       speechiness_disc valence_disc acousticness_disc energy_disc  mode  \n",
       "0                     4            3                 4           1     0  \n",
       "1                     3            4                 3           2     0  \n",
       "2                     4            3                 4           0     1  \n",
       "3                     2            0                 0           3     1  \n",
       "4                     4            1                 2           3     0  \n",
       "...                 ...          ...               ...         ...   ...  \n",
       "174384                2            0                 0           4     0  \n",
       "174385                2            1                 3           1     1  \n",
       "174386                0            3                 3           2     0  \n",
       "174387                2            2                 4           1     1  \n",
       "174388                0            4                 2           2     1  \n",
       "\n",
       "[174389 rows x 15 columns]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X"
   ]
  }
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