Spotify_Scratch.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "af603bfc-3399-414d-97d1-8e597050cb9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Final Project Scratch\n",
    "\n",
    "# Packages\n",
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\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": 62,
   "id": "9b2f28c8-bafe-4bd6-9825-0a24c3965a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "music = pd.read_csv('music.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 64,
   "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": 65,
   "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": 66,
   "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": 67,
   "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": 68,
   "id": "599536d0-610a-49ff-b6b5-0ef1a6a43bbc",
   "metadata": {},
   "outputs": [],
   "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": 69,
   "id": "d957ccce-91b7-46a9-9b44-6b8b0a8d7689",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>decade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1920</td>\n",
       "      <td>1920</td>\n",
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       "      <th>1</th>\n",
       "      <td>1920</td>\n",
       "      <td>1920</td>\n",
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       "      <td>1920</td>\n",
       "      <td>1920</td>\n",
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       "      <th>3</th>\n",
       "      <td>1920</td>\n",
       "      <td>1920</td>\n",
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       "      <th>4</th>\n",
       "      <td>1920</td>\n",
       "      <td>1920</td>\n",
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       "      <th>174385</th>\n",
       "      <td>2021</td>\n",
       "      <td>2020</td>\n",
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       "      <th>174386</th>\n",
       "      <td>2020</td>\n",
       "      <td>2020</td>\n",
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       "      <th>174387</th>\n",
       "      <td>2021</td>\n",
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       "      <th>174388</th>\n",
       "      <td>2020</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": 69,
     "metadata": {},
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    }
   ],
   "source": [
    "music[['year', 'decade']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 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": 71,
   "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": 71,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 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": 72,
   "id": "116facb5-78c1-42ef-af4b-89cf3f405b3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    136808\n",
       "1     37581\n",
       "Name: multi_artist, dtype: int64"
      ]
     },
     "execution_count": 72,
     "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": 73,
   "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": 74,
   "id": "f199fdff-fd7c-415e-a7aa-fe897c083d25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 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": 75,
   "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": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 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": 76,
   "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": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 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": 77,
   "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": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 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$energy_disc)\n",
    "\n",
    "plt.hist(music.energy)\n",
    "music.energy.describe()\n",
    "music[\"energy_disc\"] = np.digitize(music.energy, [0.2, 0.4, 0.6, 0.8])\n",
    "music.energy_disc = music.energy_disc.astype(str)\n",
    "music.energy_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d6bc9c5e-e4c5-4d7a-9832-6f0a49f4fbcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    91786\n",
       "1    31978\n",
       "4    24535\n",
       "2    14290\n",
       "3    11800\n",
       "Name: instrumentalness_disc, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instrumentalness - discretize into 5 bins\n",
    "# hist(music$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$instrumentalness_disc)\n",
    "\n",
    "plt.hist(music.instrumentalness)\n",
    "music.instrumentalness.describe()\n",
    "music[\"instrumentalness_disc\"] = np.digitize(music.instrumentalness, [0.001, 0.1, 0.5, 0.8])\n",
    "music.instrumentalness_disc = music.instrumentalness_disc.astype(str)\n",
    "music.instrumentalness_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "5e980296-075f-4faa-ae05-540c9851dde7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    71246\n",
       "2    42656\n",
       "3    24118\n",
       "0    22946\n",
       "4    13423\n",
       "Name: liveness_disc, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Key - make factor\n",
    "# music$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$liveness_disc)\n",
    "\n",
    "plt.hist(music.liveness)\n",
    "music.liveness.describe()\n",
    "music[\"liveness_disc\"] = np.digitize(music.liveness, [0.08, 0.15, 0.3, 0.5])\n",
    "music.liveness_disc = music.liveness_disc.astype(str)\n",
    "music.liveness_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4381f99f-5130-4fe4-891e-7a374bcd71bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    62359\n",
       "2    54982\n",
       "1    27174\n",
       "0    15712\n",
       "4    14162\n",
       "Name: loudness_disc, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Loudness - discretize into 5 bins\n",
    "# hist(music$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$loudness_disc)\n",
    "\n",
    "plt.hist(music.loudness)\n",
    "music.loudness.describe()\n",
    "music[\"loudness_disc\"] = np.digitize(music.loudness, [-20, -15, -10, -5])\n",
    "music.loudness_disc = music.loudness_disc.astype(str)\n",
    "music.loudness_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "dfd494c9-7522-43d7-aac9-1b399e0d6bb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    44458\n",
       "3    44443\n",
       "2    43094\n",
       "1    26266\n",
       "0    16128\n",
       "Name: speechiness_disc, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Speechiness - discretize into 5 bins\n",
    "# hist(music$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$speechiness_disc)\n",
    "\n",
    "plt.hist(music.speechiness)\n",
    "music.speechiness.describe()\n",
    "music[\"speechiness_disc\"] = np.digitize(music.speechiness, [0.03, 0.035, 0.045, 0.075])\n",
    "music.speechiness_disc = music.speechiness_disc.astype(str)\n",
    "music.speechiness_disc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "701f7824-eeac-4bdb-ae60-5585ba780e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    44447\n",
       "3    44191\n",
       "2    42316\n",
       "0    25567\n",
       "1    17868\n",
       "Name: valence_disc, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Valence - discretize into 5 bins\n",
    "# table(discretize(music$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": 83,
   "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": 84,
   "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": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g1LF9z12bPQWA05IzcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGG7LZk8A+Afb99y12VNY1xPXXbbZUwAO48wcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhu3ZhX1XlV9ZmqeqSqHq6qDyzjZ1fV3qp6bPl61jJeVXV9Ve2vqs9W1VtWPdbuZf/Hqmr3qvG3VtXnlvtcX1X1UvywAHAq2siZ+fNJfrq7L0hyUZJrquqCJHuS3NvdO5Lcu6wnyaVJdiy3q5N8PFmJf5Jrk7wtyYVJrn3hF4Bln/etut+u4//RAOD0sG7Mu/vp7v7jZfmvkjya5Nwklye5ednt5iRXLMuXJ7mlV9yX5Myqel2SdyTZ292HuvvZJHuT7Fq2vbq77+vuTnLLqscCANZxTO+ZV9X2JG9Ocn+Sc7r76WXTl5Kcsyyfm+TJVXc7sIwdbfzAGuMAwAZsOOZV9Z1JfjvJT3X3V1dvW86o+wTPba05XF1V+6pq38GDB1/qbwcAI2wo5lX1rVkJ+a939+8sw19eXiLP8vWZZfypJOetuvu2Zexo49vWGP8m3X1Dd+/s7p1bt27dyNQB4JS3kavZK8mNSR7t7l9YtenOJC9ckb47yR2rxq9crmq/KMlzy8vx9yS5pKrOWi58uyTJPcu2r1bVRcv3unLVYwEA69jIv5r2A0n+XZLPVdWfLGM/m+S6JLdV1VVJvpjkXcu2u5O8M8n+JF9L8t4k6e5DVfXhJA8s+32ouw8ty+9P8skkr0zyqeUGAGzAujHv7v+V5Eh/933xGvt3kmuO8Fg3JblpjfF9Sd6w3lwAgG/mE+AAYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYbstmTwCYZfueuzZ7Ckf1xHWXbfYU4GXnzBwAhhNzABjOy+xDnOwvbQKweZyZA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAwnJgDwHBiDgDDiTkADCfmADCcmAPAcGIOAMOJOQAMJ+YAMJyYA8BwYg4Aw4k5AAwn5gAw3Loxr6qbquqZqvr8qrGzq2pvVT22fD1rGa+qur6q9lfVZ6vqLavus3vZ/7Gq2r1q/K1V9bnlPtdXVZ3oHxIATmUbOTP/ZJJdh43tSXJvd+9Icu+yniSXJtmx3K5O8vFkJf5Jrk3ytiQXJrn2hV8Aln3et+p+h38vAOAo1o15d/9hkkOHDV+e5OZl+eYkV6wav6VX3JfkzKp6XZJ3JNnb3Ye6+9kke5PsWra9urvv6+5OcsuqxwIANuDFvmd+Tnc/vSx/Kck5y/K5SZ5ctd+BZexo4wfWGAcANui4L4Bbzqj7BMxlXVV1dVXtq6p9Bw8efDm+JQCc9F5szL+8vESe5eszy/hTSc5btd+2Zexo49vWGF9Td9/Q3Tu7e+fWrVtf5NQB4NTyYmN+Z5IXrkjfneSOVeNXLle1X5TkueXl+HuSXFJVZy0Xvl2S5J5l21er6qLlKvYrVz0WALABW9bboap+M8kPJ3ltVR3IylXp1yW5raquSvLFJO9adr87yTuT7E/ytSTvTZLuPlRVH07ywLLfh7r7hYvq3p+VK+ZfmeRTyw0A2KB1Y97d7znCpovX2LeTXHOEx7kpyU1rjO9L8ob15gEArM0nwAHAcGIOAMOJOQAMt+575gCTbN9z12ZPYV1PXHfZZk+BU4wzcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4bZs9gQATjfb99y12VNY1xPXXbbZU+AYODMHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACGE3MAGE7MAWA4MQeA4cQcAIYTcwAYTswBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACG27LZEwDg5LN9z12bPYWjeuK6yzZ7CieVk+bMvKp2VdUXqmp/Ve3Z7PkAwBQnRcyr6owkv5zk0iQXJHlPVV2wubMCgBlOipgnuTDJ/u5+vLu/keTWJJdv8pwAYIST5T3zc5M8uWr9QJK3bdJcADjJnezv6Scv7/v6J0vMN6Sqrk5y9bL611X1hRP48K9N8hcn8PFOR57D4+c5PH6ewxPD83ic6qMn/Dn850facLLE/Kkk561a37aM/SPdfUOSG16KCVTVvu7e+VI89unCc3j8PIfHz3N4Yngej9/L+RyeLO+ZP5BkR1WdX1WvSPLuJHdu8pwAYIST4sy8u5+vqp9Mck+SM5Lc1N0Pb/K0AGCEkyLmSdLddye5exOn8JK8fH+a8RweP8/h8fMcnhiex+P3sj2H1d0v1/cCAF4CJ8t75gDAi3Tax9zHyB67qjqvqj5TVY9U1cNV9YFl/Oyq2ltVjy1fz9rsuZ7squqMqnqoqn5vWT+/qu5fjsffWi4I5Siq6syqur2q/rSqHq2q73csHpuq+k/Lf8ufr6rfrKpvdyweXVXdVFXPVNXnV42tedzViuuX5/KzVfWWEz2f0zrmPkb2RXs+yU939wVJLkpyzfK87Ulyb3fvSHLvss7RfSDJo6vWP5rkY9393UmeTXLVpsxqll9K8vvd/b1J3piV59OxuEFVdW6S/5hkZ3e/ISsXIb87jsX1fDLJrsPGjnTcXZpkx3K7OsnHT/RkTuuYx8fIvijd/XR3//Gy/FdZ+Z/nuVl57m5edrs5yRWbMsEhqmpbksuSfGJZryRvT3L7sovncB1V9ZokP5TkxiTp7m9091fiWDxWW5K8sqq2JPmOJE/HsXhU3f2HSQ4dNnyk4+7yJLf0ivuSnFlVrzuR8zndY77Wx8ieu0lzGamqtid5c5L7k5zT3U8vm76U5JzNmtcQv5jkZ5L8/bL+XUm+0t3PL+uOx/Wdn+Rgkl9d3q74RFW9Ko7FDevup5L81yR/npWIP5fkwTgWX4wjHXcveWtO95hzHKrqO5P8dpKf6u6vrt7WK38m4U8ljqCqfiTJM9394GbPZbgtSd6S5OPd/eYkf5PDXlJ3LB7d8r7u5Vn5xeifJXlVvvnlY47Ry33cne4x39DHyPLNqupbsxLyX+/u31mGv/zCS0fL12c2a34D/ECSH62qJ7Ly9s7bs/Le75nLS52J43EjDiQ50N33L+u3ZyXujsWN+1dJ/qy7D3b3/03yO1k5Ph2Lx+5Ix91L3prTPeY+RvZFWN7bvTHJo939C6s23Zlk97K8O8kdL/fcpujuD3b3tu7enpXj7tPd/RNJPpPkx5bdPIfr6O4vJXmyqr5nGbo4ySNxLB6LP09yUVV9x/Lf9gvPoWPx2B3puLszyZXLVe0XJXlu1cvxJ8Rp/6ExVfXOrLx3+cLHyH5kc2d08quqH0zyP5N8Lv/wfu/PZuV989uSvD7JF5O8q7sPv0CEw1TVDyf5z939I1X1L7Jypn52koeS/Nvu/vomTu+kV1VvyspFhK9I8niS92blRMWxuEFV9XNJ/k1W/lLloST/Pivv6ToWj6CqfjPJD2flX5f7cpJrk/z3rHHcLb8k/besvH3xtSTv7e59J3Q+p3vMAWC60/1ldgAYT8wBYDgxB4DhxBwAhhNzABhOzAFgODEHgOHEHACG+39tM+iHky7h9gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x720 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": 85,
   "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": 86,
   "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": 87,
   "id": "c5b09996",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47.07456467305082"
      ]
     },
     "execution_count": 87,
     "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": 89,
   "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": 29,
   "id": "ef186d2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "accuracy     0.761454\n",
       "criterion        gini\n",
       "splitter         best\n",
       "max_depth          10\n",
       "Name: 10, dtype: object"
      ]
     },
     "execution_count": 29,
     "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": "327027ca-8110-49b8-9c4e-7c7902a0c965",
   "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>0.454421</td>\n",
       "      <td>0.454421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>log_duration_ms</td>\n",
       "      <td>0.162238</td>\n",
       "      <td>0.616658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>key</td>\n",
       "      <td>0.057731</td>\n",
       "      <td>0.674389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>instrumentalness_disc</td>\n",
       "      <td>0.050898</td>\n",
       "      <td>0.725286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>liveness_disc</td>\n",
       "      <td>0.035073</td>\n",
       "      <td>0.760360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tempo_disc</td>\n",
       "      <td>0.032728</td>\n",
       "      <td>0.793088</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 feature  importance  cum_importance\n",
       "0                 decade    0.454421        0.454421\n",
       "3        log_duration_ms    0.162238        0.616658\n",
       "7                    key    0.057731        0.674389\n",
       "6  instrumentalness_disc    0.050898        0.725286\n",
       "8          liveness_disc    0.035073        0.760360\n",
       "1             tempo_disc    0.032728        0.793088"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Feature Importance values and plots for DT model\n",
    "importances_dt = pd.DataFrame({'feature': X.columns,\n",
    "                            'importance': dt.feature_importances_})\n",
    "importances_dt = importances_dt.sort_values('importance', ascending=False)\n",
    "importances_dt['cum_importance'] = importances_dt.importance.cumsum()\n",
    "importances_dt.loc[importances_dt.cum_importance <= .80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2ee3cb46-2ca7-4400-86e3-6f4771d64bf5",
   "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": [
    "imp_dt = importances_dt.set_index('feature')\n",
    "imp_dt.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "e3fa1bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5487814522196701"
      ]
     },
     "execution_count": 98,
     "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": 36,
   "id": "69601779",
   "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": 36,
     "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": 37,
   "id": "b4a221c7-26e6-4778-b307-0629b867adf5",
   "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": [
    "imp_rf = importances_rf.set_index('feature')\n",
    "imp_rf.plot(kind='bar');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e371b4ed-1b57-495b-a12f-007b0cda146a",
   "metadata": {},
   "source": [
    "## KNN Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "b02434ad-34dc-4337-8cff-a3f253207fed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.neighbors import KNeighborsClassifier \n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from xgboost import XGBClassifier\n",
    "from lightgbm import LGBMRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "1b56dc48-fb05-4c0d-8bba-d23b14eebd7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z78Grr77aPKc7eC3eysrKw/65bdu2jdWrVzNlyhTMjPj4eJYtW0ZeXl6rS8W2tqzskZx55pnNxznn+P73v8+bb75JTEwMmzZtYtu2bbz++utceumlzT9smo7/2te+xi9+8Qsuuugi/vrXv/LAAw8c8fPaQgF/KDExkDvZa8GLSNd1DC3tSAlforSty7M2LQ0Lh14eti3HHK3169fzy1/+kgULFtCjRw++9KUvfWrJ1cMdFxcXx/z583nttdeYPXs2v//973n99ddpbGzk3XffbfO67eD9gNi9e3fzj5U9e/Ywa9as5q73tgpfQvZwy8c+8sgjbN++nUWLFhEfH09+fn6r371JcXExJSUlzJkzh4aGBkaO/NTyLcdE5+APJ78Ydq+HPZv9rkREpIVDLbvaXk466STWrVtHSUkJAI899lirx51yyin885//BGDZsmUsXboU8EK0W7dupKens23bNl588cXm14QvIXuo4yorK6moqODcc8/l17/+NUuWLAHgrLPO4ne/+13zezUNRjzcErmzZs3ipZdeoqSkhJKSEhYtWsSjjz56yKViW1tWFiA/P59FixYBMHv27EP+2VVUVNC7d2/i4+N54403mpe4Pf3003niiSfYuXNni/cFuPrqq7niiiv48pe/fMj3PVoK+MPJC43G3ODfaFMRkdYcatnV9pKcnMx9993HOeecw7hx40hLS/vU8rHgDZCrrKxk2LBh3HnnnYwbNw6AwsJCxowZw9ChQ7niiisoLi5ufs11113HOeecw9SpUw953N69eznvvPMoKChgypQp/OpXvwK8QXoLFy6koKCA4cOHc//99wMcconcpjXhwy+PGzhwIOnp6bz33nutLhV7qGVlv/Od7/DHP/6RMWPGsGPHjkP+2V155ZUsXLiQUaNG8fDDDzcvITtixAh+8IMfcOqpp1JYWMi3v/3tFq/ZvXv3Icc6HIuILhfbkSKyXGxjA/w8H0ZOh/Pvbd/3FpGo1RWXi21NZWUlqampOOe48cYbGTJkCLfeeqvfZQXS7NmzefbZZ/n73/9+yGM603Kx0S8mFnIn6Xp4EemSHnjgAf72t79RW1vLmDFjuP766/0uKZBuvvlmXnzxRV544YV2fV8F/JHkFcPqf0PlJ5Da2+9qREQ6zK233qoWewcIH1PQnnQO/kiarodXK16kSwnK6UsJhmP5+6iAP5L+oyG+mwbaiXQhSUlJ7Ny5UyEvUcE5x86dO4/q0kBQF/2RxcZDzgRdDy/ShWRnZ1NWVsb27dv9LkUE8H50HjxB0ZEo4Nsivxhe/zFU7YKUI89oJCKdW3x8fPOkKCKdlbro2yJvinevbnoREekkFPBtMWAsxCUp4EVEpNNQwLdFXCJkj/cWnhEREekEFPBtlVfsLRlZU+F3JSIiIkekgG+r/GJwjbDxXb8rEREROSIFfFsNKIKYeChRN72IiEQ/BXxbJaTAgHEaaCciIp2CAv5o5BfD5g9gf6XflYiIiByWAv5o5BWDa4DS9/yuRERE5LAU8EcjZyJYrBaeERGRqKeAPxqJqd7iMzoPLyIiUU4Bf7TyimHTIqir9rsSERGRQ1LAH638KdBQC2UL/K5ERETkkBTwRyt3EliMlo8VEZGopoA/Wknp0HeUBtqJiEhUU8Afi7xir4u+fr/flYiIiLRKAX8s8oqhvgY2ve93JSIiIq1SwB+LvMnevZaPFRGRKKWAPxYpmdB7hAbaiYhI1FLAH6u8yVA6Hxrq/K5ERETkUxTwxyq/GOr2wZYlflciIiLyKQr4Y5VX7N1rfXgREYlCCvhjldobep6o6+FFRCQqKeCPR14xbHwXGhv8rkRERKQFBfzxyCuG/Xtg64d+VyIiItKCAv545IfOw6ubXkREoowC/nh07w89Bup6eBERiToK+OOVXwwb50Fjo9+ViIiINFPAH6+8YqjeDZ+s8LsSERGRZgr449V0PfyGef7WISIiEkYBf7x65EF6jhaeERGRqKKAbw95xV4L3jm/KxEREQEU8O0jvxj2bYcdH/tdiYiICKCAbx95uh5eRESiiwK+PWQOgtS+uh5eRESihgK+PZh53fQb5uo8vIiIRAUFfHvJK4a9W2DXOr8rERERUcC3m/wp3r3Ow4uISBRQwLeXnidCSk9NeCMiIlFBAd9ezCBvsgbaiYhIVFDAt6f8KVCxEco3+l2JiIh0cQr49tR0Pbxa8SIi4jMFfHvqPRySMjTQTkREfKeAb08xMd55eAW8iIj4TAHf3vKKvWvh92zxuxIREenCFPDtLV/z0ouIiP8U8O2tbwEkpEGJ1ocXERH/KODbW0ws5E7ShDciIuIrBXwk5BfDjlVQud3vSkREpItSwEdCnualFxERfyngI6H/aIhPUcCLiIhvFPCREBsPORN0Hl5ERHyjgI+UvCmwbTlU7fK7EhER6YIU8JGSXww42PiO35WIiEgXFNGAN7NzzGyVma0xs9tb2Z9nZq+Z2VIzm2Nm2WH7Gsxscej2XCTrjIgB4yAuSQvPiIiIL+Ii9cZmFgv8ATgTKAMWmNlzzrkVYYf9EnjYOfc3Mzsd+CnwxdC+aufc6EjVF3FxiZA9HjZowhsREel4kWzBTwDWOOfWOedqgUeBCw86ZjjweujxG63s79zyJsPWD6Gmwu9KRESki4lkwA8ASsOel4W2hVsCXBJ6fDGQZmZZoedJZrbQzN41s4ta+wAzuy50zMLt26NwUpm8YnCNsPE9vysREZEuxu9Bdt8BTjWzD4BTgU1AQ2hfnnOuCLgCuNfMBh/8YufcTOdckXOuqFevXh1WdJtlj4eYeHXTi4hIh4vYOXi8sM4Je54d2tbMObeZUAvezFKB6c658tC+TaH7dWY2BxgDrI1gve0vIcUbbKeBdiIi0sEi2YJfAAwxs4FmlgDMAFqMhjeznmbWVMP3gAdD23uYWWLTMUAxED44r/PIL4bNH8D+Sr8rERGRLiRiAe+cqwduAl4GPgIed84tN7O7zeyC0GGnAavM7GOgD3BPaPswYKGZLcEbfPezg0bfdx55k8E1QNl8vysREZEuJJJd9DjnXgBeOGjbnWGPZwOzW3ndPGBUJGvrMDkTwWK9bvrBp/tdjYiIdBF+D7ILvsQ0b/EZLTwjIiIdSAHfEfKKYdMiqKv2uxIREekiFPAdIa8YGmqhbKHflYiISBehgO8IuZMAUze9iIh0GAV8R0jOgL6joEQT3oiISMdQwHeU/ClQtgDq9/tdiYiIdAEK+I6SVwz1NbDpfb8rERGRLkAB31FyT/budR5eREQ6gAK+o3TLgt7DFfAiItIhFPAdKa/YWzq2oc7vSkREJOAU8B0pvxjq9sGWJX5XIiIiAaeA70i5k717ddOLiEiEKeA7UlofyBqi9eFFRCTiFPAdLb8YNr4DjQ1+VyIiIgGmgO9oeVNg/x7Y+qHflYiISIAp4Dtans7Di4hI5CngO1r6AOiRDxvm+V2JiIgEmALeD3lTvBZ8Y6PflYiISEAp4P2QXwzVu2H7R35XIiIiAaWA90NesXevy+VERCRCFPB+6JEH6TkaaCciIhGjgPdL3mQv4J3zuxIREQkgBbxf8oph33bYsdrvSkREJIAU8H7Jn+Ldb3jb3zpERCSQFPB+yRwEqX010E5ERCJCAe8Xs9B5+Hk6Dy8iIu1OAe+n/GLYuxl2r/e7EhERCRgFvJ/yQufh1U0vIiLtTAHvp14nQUpPXQ8vIiLtTgHvp6bz8GrBi4hIO1PA+y2vGCo2QvlGvysREZEAUcD7LT80L72WjxURkXakgPdb7xGQlAElmvBGRETajwLebzExB+alFxERaScK+GiQNxl2rYM9W/yuREREAkIBHw2a1odXK15ERNqJAj4a9C2AhDQFvIiItBsFfDSIjYPcSboeXkRE2o0CPlrkF8OOVVC53e9KREQkABTw0aLpPPxGXQ8vIiLHTwEfLfqPgfgUddOLiEi7UMBHi9h4yJmggXYiItIuFPDRJG8KbFsOVbv8rkRERDo5BXw0yZsMONj4rt+ViIhIJ6eAjyYDxkFsorrpRUTkuCngo0l8EmSP18IzIiJy3BTw0Sa/GLYuhZoKvysREZFOTAEfbfKKwTXCxvf8rkRERDoxBXy0yR4PMfE6Dy8iIsdFAR9tElJgwFgFvIiIHBcFfDTKK4bNH0DtPr8rERGRTkoBH43yi6GxHkp1Hl5ERI6NAj4a5UwEi4UNWnhGRESOjQI+GiWmQb9CLTwjIiLHTAEfrfKLYdNCqKv2uxIREemEFPDRKm8KNNRC2UK/KxERkU5IAR+tcicBpsvlRETkmCjgo1VyBvQdqYAXEZFjooCPZnlToHQB1Nf6XYmIiHQyCvholl8M9dWw+X2/KxERkU5GAR/Ncid791o+VkREjpICPpp1y4Lew3UeXkREjpoCPtrlTfaWjm2o97sSERHpRBTw0S6vGOr2wZYlflciIiKdiAI+2uUVe/cbdB5eRETaTgEf7dL6QNYQzUsvIiJHRQHfGeRNho3vQmOD35WIiEgnoYDvDPKnwP4K2LbM70pERKSTUMB3Bk3n4dVNLyIibRTRgDezc8xslZmtMbPbW9mfZ2avmdlSM5tjZtlh+64xs9Wh2zWRrDPqpQ+AHvm6Hl5ERNosYgFvZrHAH4DPAsOBy81s+EGH/RJ42DlXANwN/DT02kzgLmAiMAG4y8x6RKrWTiFvihfwjY1+VyIiIp1AJFvwE4A1zrl1zrla4FHgwoOOGQ68Hnr8Rtj+s4FXnHO7nHO7gVeAcyJYa/TLmwzVu2H7Sr8rERGRTiCSAT8AKA17XhbaFm4JcEno8cVAmplltfG1mNl1ZrbQzBZu37693QqPSvlN18Orm15ERI7M70F23wFONbMPgFOBTUCbrwVzzs10zhU554p69eoVqRqjQ0YedM/WwjMiItImkQz4TUBO2PPs0LZmzrnNzrlLnHNjgB+EtpW35bVdjpnXit8wF5zzuxoREYlykQz4BcAQMxtoZgnADOC58APMrKeZNdXwPeDB0OOXgbPMrEdocN1ZoW1dW14x7NsOO9f4XYmIiES5iAW8c64euAkvmD8CHnfOLTezu83sgtBhpwGrzOxjoA9wT+i1u4Af4f1IWADcHdrWtTVfD69uehEROTxzAenuLSoqcgsXLvS7jMhyDv7nJBh4Ckz/s9/ViIiIz8xskXOuqLV9fg+yk6Nh5rXiS3QeXkREDk8B39nkF8PezbB7vd+ViIhIFFPAdzbN68PP87cOERGJagr4zqbXUEjJ0sIzIiJyWAr4zsbMm7Z2g0bSi4jIoSngO6O8KVC+EcpLj3ysiIh0SQr4zihf5+FFROTwFPCdUe/hkJSubnoRETkkBXxnFBMLuZM10E5ERA5JAd9Z5RfDrrWwd6vflYiISBRSwHdWmpdeREQOQwHfWfUtgIQ0DbQTEZFWKeA7q9g4yJ3orQ8vIiJyEAV8Z5ZXDNtXwr4dflciIiJRRgHfmeVP8e7VihcRkYMo4DuzfqMhPkWXy4mIyKco4DuzuATIHq+BdiIi8ikK+M4ufwpsWwbVu/2uREREoogCvrPLKwYcbHjH70pERCSKKOA7uwHjIDZRA+1ERKQFBXxnF58UOg+vgBcRkQMU8EGQNxm2LIGaPX5XIiIiUUIBHwT5xeAaofQ9vysREZEooYAPguwJEBOvhWdERKSZAj4IElJgwFidhxcRkWYK+KDImwybP4DafX5XIiIiUUABHxR5U6CxHkrn+12JiIhEAQV8UOROBItVN72IiAAK+OBITIN+hVp4RkREAAV8sOQXw6aFUFfjdyUiIuIzBXyQ5BVDQ60X8iIi0qUp4IMk92TA1E0vIiIK+EBJzoC+I2GDJrwREenqFPBBkzcFShdAfa3flYiIiI8U8EGTNxnqq71Jb0REpMtSwAdNXrF3r256EZEuTQEfNN2yoNcwDbQTEeniFPBBlF/sLR3bUO93JSIi4hMFfBDlFUNtJWxd4nclIiLiEwV8EDWdh1c3vYhIl6WAD6K0PpB1ghaeERHpwhTwQZVXDBvegcYGvysREREfKOCDKn8K7K+Abcv8rkRERHyggA+qvMne/YZ5/tYhIiK+UMAHVXo2ZORBiSa8ERHpihTwQZY/xWvBNzb6XYmIiHQwBXyQ5RVD9S7YvtLvSkREpIMp4IMsv2leel0uJyLS1SjggywjD7oPUMCLiHRBCvggM/O66UvmgnN+VyMiIh1IAR90+cWw7xPYucbvSkREpAMp4IMub4p3r8vlRES6FAV80GUNhtQ+mvBGRKSLUcAHnZk3q90GnYcXEelKFPBdQV4x7NkEu0v8rkRERDqIAr4ryA+dh9flciIiXYYCvivoNRRSsrzL5UREpEtQwHcFZpB7slrwIiJdiAK+q8ifAuUboKLM70pERKQDKOC7irzQvPTqphcR6RIU8F1FnxGQlA4bNOGNiEhXoIDvKmJiIXeyJrwREekiFPBdSd5kb076vVv9rkRERCJMAd+VaH14EZEuQwHflfQthIQ0DbQTEekCFPBdSWwc5E5UC15EpAtQwHc1ecWwfSXs2+F3JSIiEkEK+K6m6Xp4jaYXEQk0BXxX038MxCWrm15EJOAiGvBmdo6ZrTKzNWZ2eyv7c83sDTP7wMyWmtm5oe35ZlZtZotDt/sjWWeXEpcAORM00E5EJOAiFvBmFgv8AfgsMBy43MyGH3TYHcDjzrkxwAzgvrB9a51zo0O3GyJVZ5eUPwW2LYPq3X5XIiIiEXLEgDez883sWH4ITADWOOfWOedqgUeBCw86xgHdQ4/Tgc3H8DlytPImAw42vut3JSIiEiFtCe4vAKvN7BdmNvQo3nsAUBr2vCy0LdwPgavMrAx4Abg5bN/AUNf9/5nZZ1r7ADO7zswWmtnC7du3H0VpXdyAIohNhBLNSy8iElRHDHjn3FXAGGAt8JCZvRMK1rR2+PzLgYecc9nAucDfQ70FW4DcUNf9t4F/mln3g1/snJvpnCtyzhX16tWrHcrpIuKTILtIA+1ERAKsTV3vzrk9wGy8bvZ+wMXA+2Z282FetgnICXueHdoW7qvA46HPeAdIAno65/Y753aGti/C+3FxYltqlTbKK4YtS6Bmj9+ViIhIBLTlHPwFZvY0MAeIByY45z4LFAL/cZiXLgCGmNlAM0vAG0T33EHHbASmhT5nGF7AbzezXqFBepjZIGAIsO5ovpgcQX4xuEYone93JSIiEgFxbThmOvBr59yb4Rudc1Vm9tVDvcg5V29mNwEvA7HAg8655WZ2N7DQOfcc3g+EB8zsVrwBd19yzjkzOwW428zqgEbgBufcrmP6htK67PEQE+etDz/kDL+rERGRdmbOucMfYDYQ2OKcqwk9Twb6OOdKIl9e2xUVFbmFCxf6XUbn8uczvfuvveJvHSIickzMbJFzrqi1fW05B/8EXiu6SUNom3R2+cWw+X2o3ed3JSIi0s7aEvBxoevYAQg9TohcSdJh8qZAYz2sehGO0JMjIiKdS1sCfruZXdD0xMwuBLQUWRDkTID4FHjyq/D/ToDHr4b3ZsK2FdDYeOTXi4hI1GrLILsbgEfM7PeA4U1ec3VEq5KOkdQdbloI697wJr1Z/xaseNbbl5LlXUqX/xlvatteQyFGaxOJiHQWRxxk13ygWSqAc64yohUdIw2yawfOQfkGL+xL5kLJW1ARmowwJcub4rY58Icp8EVEfHa4QXZtacFjZp8DRgBJZgaAc+7udqtQooMZ9Mj3bmOu8rbtbgr80O2jf3nbkzO9QXoKfBGRqHTEgA8t1ZoCTAX+DHwe0OwoXUWPPO825krv+e4N3hS3JW97LfzmwO/Rsku/93AFvoiIj9pyHfxS51xB2H0q8KJzrtUFYPyiLnqftAj8t70ufggL/CmhwB+hwBcRaWfH20VfE7qvMrP+wE68+ehFDrTwR1/hPS/fGDp/H2rhr/xfb3tSxoGwV+CLiERcWwL+X2aWAfw/4H28KWUfiGRR0oll5MLoXBh9ufe8vDTUwn/LC/3wwA9v4fcZqcAXEWlHh+2iDy3dOsk5Ny/0PBFIcs5VdFB9baYu+k6iReDPhd3rve3NgV8cFvixvpYqIhLtDtdF35Zz8B+E1mWPagr4Tqqi7MAleSVvhwV+eistfAW+iEi44z0H/5qZTQeecm29aF6krdKzofAL3g2gYlPLLv1VL3jbE9ND1+GHAr/vKAW+iMhhtKUFvxfoBtTjDbgzwDnnuke+vLZTCz6gWgT+XNi11tveHPihVn7fAgW+iHQ5x9WCd86ltX9JIm2UPgAKLvNuAHs2t+zS//hFb3tiOuSdHNbCV+CLSNfWloluTmltu3PuzfYvR+QIuveHgku9G8CeLS279D9+ydue2B1yDwr82DZN3CgiEght+RfvP8MeJwETgEXA6RGpSORodO8Hoz7v3eCgwJ8Lq1/2tid2h/Ffhc98BxJT/atXRKSDtHmxmeYXmOUA9zrnpkempGOjc/DSqr1bQ9ffPw/Ln4K0fnDm3TDqUm/ufRGRTuxw5+CPZWaRMmDY8ZUk0kHS+nqt+0v/Cl99BVL7wFPXwoNnw+bFflcnIhIxbTkH/zu82evA+0EwGm9GO5HOJWcCXPsGLP4HvPrfMPM0GHcNnP5f0K2n39WJiLSrtpyDD+/3rgdmOefmRqgekciKiYGxV8OwC+D/fgHv3Q/Ln4apP4Cir2ognogERluug+8G1DjnGkLPY4FE51xVB9TXZjoHL8fkk5Xw0m2wbo63xO05P4NBp/pdlYhImxzvOfjXgOSw58nAq+1RmIjveg+FLz4DX/gH1FbCwxfA41d7q+KJiHRibQn4JOdcZdOT0OOUyJUk0sHMYNj5cON8r6v+43/D7yfAnJ9DXbXf1YmIHJO2BPw+Mxvb9MTMxgH6V0+CJz4ZTv0u3LQATjoH5vzEC/oVz4KWYRCRTqYtAf8t4Akze8vM3gYeA26KaFUifsrIgUsfgmv+FxLTvC77hy+ATz7yuzIRkTZr00Q3ZhYPnBR6uso5VxfRqo6BBtlJRDTUw6K/wus/hv17YcK1cNrtkNzD78pERI5vkJ2Z3Qh0c84tc84tA1LN7BvtXaRIVIqN80L95ve9a+bf+xP8bhws+hs0NvhdnYjIIbWli/5a51x50xPn3G7g2ohVJBKNumXBeb+G6/8Pep4I/7oFHjgdNr7nd2UiIq1qS8DHmh2YtDt0HXxC5EoSiWL9CuHLL8L0v0DlJ/DgWfDU9d4iNyIiUaQtAf8S8JiZTTOzacAs4MXIliUSxcy8+e1vWgCf+Q9vEZvfF8Hb90L9fr+rExEB2hbwtwGvAzeEbh/ScuIbka4pMRWm3Qk3vgcDT4FX74L7TvauoxcR8dkRA9451wi8B5TgrQV/OqDrhUSaZA6Cy2fBlU+CxcA/L4VHLoOda/2uTES6sEOurGFmJwKXh2478K5/xzk3tWNKE+lkhpwBA+fB/D95s+D9YSKcfCOc8h3venoRkQ50uBb8SrzW+nnOuSnOud8Bui5I5HDiEmDyzXDzIii4DObeC78rgiWPaTY8EelQhwv4S4AtwBtm9kBogJ0d5ngRaZLWBy66D772GnTvD09fBw+eDZs/8LsyEekiDhnwzrlnnHMzgKHAG3hT1vY2sz+a2VkdVJ9I55Zd5IX8hX+AXetg5lR47hbYt8PvykQk4NoyyG6fc+6fzrnzgWzgA7yR9SLSFjExMOYqr9v+5Bth8SPwu7HerHgN9X5XJyIB1ZbL5Jo553Y752Y656ZFqiCRwEpKh7Pvga/Pg/5j4cXvwv1TYN3/+V2ZiATQUQW8iLSDXifBF5+GLzwCdfu8leoe+yLs3uB3ZSISIAp4ET+YwbDz4Mb5MPUOWP0K/GECzPkZ1FX7XZ2IBIACXsRP8clw6n/CzQvhpHNhzk/h9xNgxbO6rE5EjosCXiQapGfDpX+FLz0PSd3h8au9rvttK/yuTEQ6KQW8SDTJnwLX/R+c+0vYstQbhPfibVC92+/KRKSTUcCLRJvYOJhwLdzyARR9GebPhN+Ng0UPQaMmkxSRtlHAi0SrlEz43P94LfqeJ8G/vgkPTIWN7/ldmYh0Agp4kWjXrwC+/AJM/wtUbocHz4KnroM9W/yuTESimAJepDMwg1Gf90bbf+Y7sPxpr9v+7V9D/X6/qxORKKSAF+lMErrBtP+CG9+DQafBqz+E+ybBxy/7XZmIRBkFvEhnlDkILv8nXPUkWCz88zJ45FLYscbvykQkSsT5XYCIHIcTzvDmtp8/05sF775J3gp2fUdB3wLvvvcwiEv0u1IR6WDmAjJbVlFRkVu4cKHfZYj4Z+82mPdbKFsI25ZBbaW3PSYOeg0NhX7YLbmHv/WKyHEzs0XOuaLW9qkFLxIUaX281eoAGhth93rYuhS2fujd1r4BS2YdOD491wv6fgUHQj89xxvQJyKdngJeJIhiYiBrsHcbcfGB7ZWfhAI/LPhXvQCEevKSMg507zcFf88TITbej28hIsdBAS/SlaT2hhOmebcmtfu8Oe+3Lj0Q/Av/AvU13v7YBO88ft+CA+f1+46ExDR/voOItIkCXqSrS+gGOeO9W5OGeti5pmVrf9UL8MHfDxyTOShsMF8o+NP6qotfJEoo4EXk02LjoPdQ71ZwqbfNOdi7xQv7LaHW/pal3tK2Tbr1ChvIFwr+rMEQE+vP9xDpwhTwItI2ZtC9v3c78ewD22sqYNvyA639LUvhnfugsc7bH5cMfUaEDeYrgN7DISHFn+8h0kUo4EXk+CSlQ95k79akvhZ2fNxyMN+yJ2Hhg95+i4GsIQda+/1Crf1uPf35DiIBpIAXkfYXl+ANxOs78sA256B8Y8vz+qXvwbLZB45J6xc2kC8U/Bn53lUBInJUFPAi0jHMoEeedxt23oHtVbsOtPKbwn/Nq+AavP0JaaEfC2HBr9n5RI5IAS8i/krJhEGnercmdTWw/aPQYL5Q8C9+pOXsfH0LIHcS5Ez0bt37+VO/SJRSwItI9IlPgv5jvFuT8Nn5tiyB0vneOf137/P2Z+RCziTInejd9x6m0fvSpSngRaRzaG12vvra0Ln8d2Hju7D+/+DDx719id29hXeaQn9AESSm+le/SAfTYjMiEhzOwe4Sb/Be6Xuw8T34ZAXgvGV1+4480KWfOwnSs/2uWOS4HG6xGQW8iARbdbm3wl5TK3/TIqir8vZ1zz7QpZ8zAfqM9Cb5EekktJqciHRdyRkw5AzvBt40vNs+9Fr3pe/Chne8a/QBElJhwLgDg/eyx0NSd99KFzkeasGLiJSXhrr03/VCf9tycI2AebPwNXXp50z0BvNpvn2JEmrBi4gcTkaOdxv1ee/5/r2hbv1Q6C993FthD7zJeHImHBi817dAy+lKVIpowJvZOcBvgFjgz865nx20Pxf4G5AROuZ259wLoX3fA74KNAC3OOdejmStIiLNEtNg8FTvBtDY4LXqwwfvNS2yE5/ides3hX7OeEju4V/tIiER66I3s1jgY+BMoAxYAFzunFsRdsxM4APn3B/NbDjwgnMuP/R4FjAB6A+8CpzoXNPUVp+mLnoR6VB7Noe69EOt/K0fHph9r9ewloP3MgepW18iwq8u+gnAGufculARjwIXAivCjnFA0wiWdGBz6PGFwKPOuf3AejNbE3q/dyJYr4hI23XvDyMv8W4Atfu8EfpNg/eWPQ2LHvL2devtBX3uJC/0+xVoql2JuEgG/ACgNOx5GTDxoGN+CPzbzG4GugFnhL323YNeO+DgDzCz64DrAHJzc9ulaBGRY5LQDQae4t3Am3lv+8rQ5Xmh0F/5v96+2EQYMLbl4L2UTP9ql0Dye5Dd5cBDzrn/MbOTgb+b2cgjvaiJc24mMBO8LvoI1SgicvRiYqDPcO9W9BVv295tYefx34V3/gBz7/X2ZQ050K2fOwmyTlC3vhyXSAb8JiAn7Hl2aFu4rwLnADjn3jGzJKBnG18rItK5pPWB4Rd4N4C6atj0vte6L50PK5+HD/7h7UvJ8q7DT+vrDfpL7H7QfZp3jX749vhk/SiQZpEM+AXAEDMbiBfOM4ArDjpmIzANeMjMhgFJwHbgOeCfZvYrvEF2Q4D5EaxVRKTjxSdDfrF3A69bf+fqA4P3Nr0Pmz/wLttrmn3vcCz2QNgnhf0QSDzocav7uh/40RCXpB8KARCxgHfO1ZvZTcDLeJfAPeicW25mdwMLnXPPAf8BPGBmt+INuPuS84b1Lzezx/EG5NUDNx5uBL2ISCDExECvk7zbuGta7muo84K++bbnwOOaikPs2wOVn8DOtQf21Ve3oY64sNAPC/7mHwWH2xe2PS5RPxR8pJnsRES6kvpaqK30wr9mz0E/DMJ+KLTYt+egHxR7oGH/kT8rJv7Q4d/ih0F376qEjFxIz/GmF5Y20Ux2IiLiiUuAuMzjH7Vfvx/2V7Ye/uE9COE/IGr2wJ5NB/bV7IHGuk+/d2J3L+gzcg66z/Med+ulnoE2UMCLiMjRi0v0bt2yju996vd7K/7t2QTlG6Gi1FsboOl+4zveKYgWn53kLfXbHP65oemGQz0Aaf20KiAKeBER8VNcond1QVofb26A1tRUtAz9io0Hnq96CfZ90vJ4i4XuA1rpAQj1AnQfAPFJkf9uPlPAi4hIdEtKh77p0PcQ06TUVUNFWes9ABvmwoebQqsDhkntc1D45x64z8jxxgZ0cgp4ERHp3OKToecQ79aahjpv7YAW4R/6MbBlKax84dODBpMyWnb/HzwWICUz6scBKOBFRCTYYuOhR553a01jo9fNf3D3f3kp7F4P69+E2r0tXxOf0spAwLCxAKl9vcsefaSAFxGRri0mxpsxMK2vt9zvwZyD6t2f7v5v+jGw6X2o3nXQe8ZD+oCDuv9zYNBp3gDBDqCAFxERORwzr0s+JRP6FbZ+TO2+T3f/Nz1f+wbs3QI4uPxRBbyIiEinkdANeg/1bq2pr4U9Zd41/B1EAS8iIhJpcQmQOahDP9LfEQAiIiISEQp4ERGRAFLAi4iIBJACXkREJIAU8CIiIgGkgBcREQkgBbyIiEgAKeBFREQCSAEvIiISQAp4ERGRAFLAi4iIBJACXkREJIAU8CIiIgGkgBcREQkgBbyIiEgAKeBFREQCSAEvIiISQAp4ERGRAFLAi4iIBJACXkREJIAU8CIiIgGkgBcREQkgBbyIiEgAKeBFREQCSAEvIiISQAp4ERGRAFLAi4iIBJACXkREJIAU8CIiIgGkgBcREQkgBbyIiEgAKeBFREQCSAEvIiISQAp4ERGRAFLAi4iIBJACXkREJIAU8CIiIgGkgBcREQkgBbyIiEgAKeBFREQCSAEvIiISQAp4ERGRAFLAi4iIBJACXkREJIAU8CIiIgGkgBcREQkgBbyIiEgAKeBFREQCSAEvIiISQAp4ERGRAFLAi4iIBJACXkREJIAU8CIiIgGkgBcREQkgBbyIiEgAKeBFREQCSAEvIiISQAp4ERGRAFLAi4iIBFBcJN/czM4BfgPEAn92zv3soP2/BqaGnqYAvZ1zGaF9DcCHoX0bnXMXRLJWERGR9tDQ6NhSUc3GXVWU7fLuS3dXsXFXFXd8bhjj8jI7pI6IBbyZxQJ/AM4EyoAFZvacc25F0zHOuVvDjr8ZGBP2FtXOudGRqk9ERORYOOeoqK7zgjsU4Bt3VVEWCvFNu6upb3TNx8fGGP0zksjpkULY5oiLZAt+ArDGObcOwMweBS4EVhzi+MuBuyJYj4iISJvsr2+gbHc1pbuqKA0FeFOYl+6qYu/++hbHZ3ZLIKdHMqMGpHPuqH7kZqaQm5lCTo8U+mUkER/b8WfEIxnwA4DSsOdlwMTWDjSzPGAg8HrY5iQzWwjUAz9zzj3TyuuuA64DyM3NbZ+qRUQk8BobHdsr9zcHdlOANz3etrcGF9baToyLIbtHMrmZKYzP70FOZop365FCTmYyaUnx/n2ZQ4joOfijMAOY7ZxrCNuW55zbZGaDgNfN7EPn3NrwFznnZgIzAYqKijqw40NERKJd5f56Nu70zn+Ht8S97vRq9tc3Nh9rBn27e93oxSf0JCcz+UArPDOFXqmJxMSYj9/m6EUy4DcBOWHPs0PbWjMDuDF8g3NuU+h+nZnNwTs/v/bTLxURka6orqGRLeU1zQPYDrTEqyjdXc2ufbUtjk9LjCMnM4UhvdOYNqwPOT2Sm1viAzKSSYqP9embREYkA34BMMTMBuIF+wzgioMPMrOhQA/gnbBtPYAq59x+M+sJFAO/iGCtIiISZZxz7NpXS+nuA+e+m0N8dxWby2toCBu1FhdjZIdC+5wB6eT0SAlrhSeTnhyPWedqhR+PiAW8c67ezG4CXsa7TO5B59xyM7sbWOicey506AzgUefCz3YwDPiTmTXiXav/s/DR9yIi0vnV1DWwp7qO3VV1bCqvCnWntwzzfbUNLV7TMzWRnMxkxub24MLCA13oOZnJ9EtPJraTdaNHkrXM1c6rqKjILVy40O8yRES6DOcc1XUNVFTXebeqOvbU1B94Xl3HnkM8rqiua3EOvElyfGzz+e+mQWzhIZ6SEC1Dx6KDmS1yzhW1tk9/UiIiXZhzjsr99aEArj9kGDdvr2m5v67h8I3EtKQ40pPjm28n9E5tftw9dMtIjmdAj2RyeqTQMzWhS3WjR5ICXkSkk2tsdOytqW8O30Pd9rTWqq6pb3Ee+2AxBt3DAjo9OZ7+GckHQjqp5b7wW2pSnLrMfaSAFxGJMs45ynZXs3xzBTsqa5tDudUAr6pj7/56Dne2NS7GWrSaM1ISyMvqFnresoV9cJh3S4jrdJeHiUcBLyLis137allSVs6S0tCtrOJTl3glxMW0CN7eaUmc0Cv1kMGcnnKghZ2SEKtu7y5IAS8i0oGqautZvnkPS0rLWVxazpKyckp3VQPeZCsn9k5j2tDeFOZkUJCdTp/uSaQnxwfuGm2JPAW8iEiE1Dc08vG2yubW+eLSclZ/Utl8zntARjKFOelcNTGPwpwMRg5IJzVR/yxL+9DfJBGRduCco3RXNYvDutqXba6gps67FCwjJZ7C7AzOGt4n1DrPoFdaos9VS5Ap4EVEjsGOyv0sLStncWkFS0rLWVpWzu6qOsBbmGTUgHSumJBHYU46o3MyyM1M0Xlw6VAKeBGRI9i3v54PN1WwtKycJaUVLC4tZ1O5d948xuDEPmmcPaIvBdkZFOakc2KfNF+WBxUJp4AXEQlT19DIqq17w0a1V7D6k700XSqek5nMmNwMvlycT0F2BiMHdNfsahKV9LdSRLos5xwlO6tCl6Z5gb58857mKVQzuyVQmJ3OZ0f1pTDbG9Welarz5tI5KOBFpMv4ZG8NS0orQufOy1laVkFFtXfePDk+llED0rn65DwKsjMYnZNBdo9knTeXTksBLyKBtLemjg83VTQH+pLScjZX1AAQG2Oc1CeNc0f1Y3ROOgXZGQzpnUqczptLgCjgRaTTq61vZOXWPc2zwC0pLWfN9srm6VvzslIoys+kMCeDwux0RvRPJzlBE8dIsCngRaTTaGh0lFfVsqOyluWbK5oDfcXmPdQ2eOfNe6YmUJidwfmF/b3rzQek06Nbgs+Vi3Q8BbyI+KK2vpHyqlp2V9Wxa18t5VW17Kqqpbyqjt37wh5X1bJ7n3fcnpq6FouqpCR4582/XJzvtc5zMuifnqTz5iIo4EWkHVTXNrC7qjYU1KFQrqpl976wx6Hg3h0K7sr99Yd8v+T4WDK7JZCREk9mtwSye6TQIyWeHikJ3n23BIb27c4JvVO1HKnIISjgRaSZc47K/fWUh1rV4UHd1MLeXRV63LRtX23zZWWtSUuKaw7mrNQETuid2vw8o1sCmU2PUxKaQ10Lq4gcPwW8SEA1Njr21DQF9YEwPriF7XWFHzimrqH1hcXNICM51IrulsCAjCRG9O/eHMpeaHth7W3ztmtGNxF/KOBFAsA5x1urd/Dnt9dTtruK3ftqqaiua5597WBxMUZGWHf3wJ7dGBcK5R7hYd3twOPuyfHqDhfpRBTwIp3cu+t28qt/f8z8kl30T09iTG6P5mD2ur1D3d+hoM7oFk9aYpwGookEnAJepJN6f+NufvXvj3l7zQ76dE/kRxeO4LLxOSTG6fy1iCjgRTqdZZsq+NUrH/P6yk/I6pbAHZ8bxlWT8jQwTURaUMCLdBKrtu7l1698zEvLt5KeHM93zzmJa07Op1ui/jcWkU/TvwwiUW7d9krufXU1/1q6mdSEOL51xhC+MmUg3ZPi/S5NRKKYAl4kSpXuquI3r63mqffLSIyL5eunDua6UwaRkaJpV0XkyBTwIlFmS0U1v3t9DY8vKCU2xvhK8UBuOG0wPbUOuYgcBQW8SJT4ZG8N972xln/O34hzjism5nLj1BPo0z3J79JEpBNSwIv4bNe+Wv705lr+Nq+EugbHpeOyuen0E8jukeJ3aSLSiSngRXxSUV3HX95ax1/eXk9VXQMXjR7AN6cNIb9nN79LE5EAUMCLdLDK/fU8NHc9M99cx56aej43qh/fOmMIQ/qk+V2aiASIAl6kg1TXNvD3d0v445y17K6q44xhfbj1zCGM6J/ud2kiEkAKeJEI21/fwKz3NvKHOWvZvnc/p5zYi2+feSKjczL8Lk1EAkwBLxIhdQ2NPLGwjN+9vpotFTVMHJjJfVeOZXx+pt+liUgXoIAXaWf1DY08s3gzv31tNRt3VTE2N4NfXlrI5MFZWsFNRDqMAl6knTQ2Ov73wy3c++rHrNu+j5EDuvPXL43ntJN6KdhFpMMp4EWOk3OOl5dv495XP2bl1r2c1CeN+68ax9kj+ijYRcQ3CniRY+ScY86q7fzqlY/5cFMFg3p247eXj+G8Uf2IiVGwi4i/FPAix2Demh388t+reH9jOTmZyfzy0kIuGt2fuNgYv0sTEQEU8CJHZUHJLv7n36t4d90u+qUn8ZOLR3FpUTbxCnYRiTIKeJE2WFJazv+88jFvfrydnqmJ/PD84cyYkEtSfKzfpYmItEoBL3IYKzbv4VevfMyrH22jR0o83z93KF+clE9ygoJdRKKbAl6kFWs+2cuvX13N80u3kJYUx3+ceSJfnjKQ1ET9LyMinYP+tRIJU7JjH799bTXPLN5EcnwsN59+Al+bMoj0lHi/SxMROSoKeBGgbHcVv399DU8sKiM+1rj2M4O4/tTBZHZL8Ls0EZFjooCXLm3bnhr+8MYaZs3fiGF8cVIe35g6mN5pSX6XJiJyXBTw0iXtqNzP/XPW8vd3N9DQ6LhsfA43TT2B/hnJfpcmItIuFPDSpZRX1TLzzXU8NK+EmroGLhmbzS2nDyE3K8Xv0kRE2pUCXrqEvTV1/OXt9fzlrfVU1tZzfkF/vnnGEAb3SvW7NBGRiFDASyA1NjpWbt3LvLU7mLd2J++t28m+2gbOGdGXW888kZP6pvldoohIRCngJRCcc6zfsY95a3fyztqdvLNuJ7v21QIwqGc3LhozgBnjcxmVne5zpSIiHUMBL53W5vJq5q3dyby1O3hn7U62VNQA0C89iakn9Wby4Cwmn5BFv3QNnBORrkcBL53Gzsr9vLNuZ3Mrff2OfQBkdkvg5MFZTB6cRfHgnuRlpWgddhHp8hTwErX21tTx3rpdza30lVv3ApCaGMekQZlcNSmPyYOzOKlPmtZfFxE5iAJeokZNXQMLS3Y3D4z7cFMFDY2OxLgYivJ78J9nn8TkwVmMGpCudddFRI5AAS++qWtoZGlZOXPXeC309zeUU9vQSFyMUZiTwTdOG8zkwT0Zk5uhZVlFRI6SAl46TGOjY8WWPbyzdidz1+5g/vpdVNU2YAbD+3Xnmsl5TD6hJ+PzM7Vqm4jIcdK/ohIxzjnWbt/HO2t3MHfNTt5dv5PyqjoABvfqxvSx2UwenMWkQVn00KIuIiLtSgEv7apsd1XzKPd5a3ewbc9+AAZkJHPmsD5MPiGLkwf1pG+6FnMREYkkBbwcl+17vUvXmlrpG3dVAdAzNYGTB/f0rkUfnEVupi5dExHpSAp4OSoV1XW8F3Yt+qpt3qVraYlxTByUxZeL85k8uCcn9klVoIuI+EgBL4dVXdvAgpJdoUDfwYebKmh0kBQfw/j8TC4c05/iwT0Z0b+7Ll0TEYkiCnhpoba+kSVl5cxd412L/sHG3dQ1OOJijDG5Gdx0+hAmD85iTG4GiXG6dE1EJFop4Lu4hkbHis17mBuaXGbB+l1U13mXro3sn85Xigdy8uAsxudn0k2XromIdBr6F7sLe2XFNu545sPmke4n9E7lsqJsTh7ck0mDMslI0aVrIiKdlQK+C6qoruPuf63gyffLGNavO9/77DAmD86id3dduiYiEhQK+C7mrdXb+e7spXyydz83n34CN58+hIQ4DY4TEQkaBXwXsW9/PT998SP+8e5GBvfqxpNfn8zonAy/yxIRkQhRwHcB89fv4jtPLKF0dxVfmzKQ75x9khZvEREJuIj2zZrZOWa2yszWmNntrez/tZktDt0+NrPysH3XmNnq0O2aSNYZVDV1Ddzz/Aq+MPMdHI5Hr53EHecNV7iLiHQBEWvBm1ks8AfgTKAMWGBmzznnVjQd45y7Nez4m4ExoceZwF1AEeCARaHX7o5UvUGzpLSc/3hiCWs+qeTKibl8/9xhusxNRKQLieS/+BOANc65dQBm9ihwIbDiEMdfjhfqAGcDrzjndoVe+wpwDjArgvUGQm19I79/fTV/mLOWXqmJ/O0rEzj1xF5+lyUiIh0skgE/ACgNe14GTGztQDPLAwYCrx/mtQMiUGOgrNy6h28/toQVW/ZwydgB3HX+CNKT4/0uS0REfBAtfbYzgNnOuYajeZGZXQdcB5CbmxuJujqF+oZGZr61jl+/8jHpyfH86YvjOHtEX7/LEhERH0Uy4DcBOWHPs0PbWjMDuPGg15520GvnHPwi59xMYCZAUVGRO/ZSO6912yv5jyeW8MHGcj47si8/vmgkWamJfpclIiI+i2TALwCGmNlAvMCeAVxx8EFmNhToAbwTtvll4Cdm1iP0/CzgexGstdNpbHT87Z0Sfv7SShLjYvnNjNFcUNhfS7SKiAgQwYB3ztWb2U14YR0LPOicW25mdwMLnXPPhQ6dATzqnHNhr91lZj/C+5EAcHfTgDuB0l1V/OfsJby7bhdTT+rFz6YX0EfTzIqISBgLy9VOraioyC1cuNDvMiLKOcdjC0r50f+uwMz4r/OGcVlRjlrtIiJdlJktcs4VtbYvWgbZyRFs21PDbU8uZc6q7Zw8KItffL6AnMwUv8sSEZEopYCPcs45nluymTufXc7++gZ+eP5wrj45n5gYtdpFROTQFPBRbGflfu54ZhkvLtvKmNwM/ufSQgb1SvW7LBER6QQU8FHq5eVb+f5TH7K3pp7bzhnKdacMIlatdhERaSMFfJSpqKrjv/+1nKc+2MSI/t155NpChvbt7ndZIiLSySjgo8j/fbyd22YvZXvlfm6ZNoSbpp5AQlxEF/wTEZGAUsBHgcr99fzkhY/453sbOaF3KjOvHkdBdobfZYmISCemgPfZu+t28p+zl1C2u5rrThnEt888Ueu1i4jIcVPA+6SmroH/9/IqHpy7npweKTx+/cmMz8/0uywREQkIBbwPFpeW8+3HF7Nu+z6+OCmP2z87lG6J+k8hIiLtR6nSgWrrG/nta6u5b84a+nRP4u9fncBnhvTyuywREQkgBXwHWbF5D99+fDErt+7l8+OyufP84XRPive7LBERCSgFfITVNzTypzfXce+rH5OenMADVxdx5vA+fpclIiIBp4CPoDWfVPIfTyxhSWk5nyvox48uHElmtwS/yxIRkS5AAR8BjY2Ov84r4RcvrSQ5IZbfXT6G8wv7+12WiIh0IQr4dla6q4rvPLGE99bvYtrQ3vz0klH07p7kd1kiItLFKODbiXOOWfNL+fHzK4gx4xefL+DScdmYaYEYERHpeAr4drC1oobvPrmUNz/ezuTBWfy/SwsZkJHsd1kiItKFKeCPg3OOZxZv4q5nl1PX4Lj7whFcNTGPGC3rKiIiPlPAH6Mdlfv5wdMf8vLybYzL68EvLy1kYM9ufpclIiICKOCPyUvLtvD9p5dRWVPP9z47lK99ZhCxarWLiEgUUcAfhYqqOu56bhnPLN7MyAHd+dVlozmxT5rfZYmIiHyKAr6N3lj1Cbc/uZSdlbV864wh3Dj1BOJjY/wuS0REpFUK+COo3F/PPc+vYNb8Uk7sk8qfrx7PqOx0v8sSERE5LAX8Ybyzdif/OXsJm8qruf7UQdx6xokkxcf6XZaIiMgRKeBbUV3bwC9eXslf55aQn5XC7BtOZlxept9liYiItJkCvhW19Y28tGwr15ycx22fHUpKgv6YRESkc1FytSI9JZ6Xbz1F67WLiEinpWHgh6BwFxGRzkwBLyIiEkAKeBERkQBSwIuIiASQAl5ERCSAFPAiIiIBpIAXEREJIAW8iIhIACngRUREAkgBLyIiEkAKeBERkQBSwIuIiASQAl5ERCSAFPAiIiIBpIAXEREJIAW8iIhIACngRUREAkgBLyIiEkAKeBERkQBSwIuIiASQAl5ERCSAFPAiIiIBpIAXEREJIHPO+V1DuzCz7cCGdn7bnsCOdn7PaKTvGSz6nsGi7xks7f0985xzvVrbEZiAjwQzW+icK/K7jkjT9wwWfc9g0fcMlo78nuqiFxERCSAFvIiISAAp4A9vpt8FdBB9z2DR9wwWfc9g6bDvqXPwIiIiAaQWvIiISAAp4EVERAJIAd8KM3vQzD4xs2V+1xIpZpZjZm+Y2QozW25m3/S7pkgwsyQzm29mS0Lf87/9rimSzCzWzD4ws//1u5ZIMbMSM/vQzBab2UK/64kUM8sws9lmttLMPjKzk/2uqb2Z2Umh/45Ntz1m9i2/64oEM7s19G/QMjObZWZJEf9MnYP/NDM7BagEHnbOjfS7nkgws35AP+fc+2aWBiwCLnLOrfC5tHZlZgZ0c85Vmlk88DbwTefcuz6XFhFm9m2gCOjunDvP73oiwcxKgCLnXKAnRTGzvwFvOef+bGYJQIpzrtznsiLGzGKBTcBE51x7T1rmKzMbgPdvz3DnXLWZPQ684Jx7KJKfqxZ8K5xzbwK7/K4jkpxzW5xz74ce7wU+Agb4W1X7c57K0NP40C2Qv2rNLBv4HPBnv2uR42Nm6cApwF8AnHO1QQ73kGnA2qCFe5g4INnM4oAUYHOkP1ABL5hZPjAGeM/nUiIi1G29GPgEeMU5F8jvCdwLfBdo9LmOSHPAv81skZld53cxETIQ2A78NXTK5c9m1s3voiJsBjDL7yIiwTm3CfglsBHYAlQ45/4d6c9VwHdxZpYKPAl8yzm3x+96IsE51+CcGw1kAxPMLHCnXczsPOAT59wiv2vpAFOcc2OBzwI3hk6pBU0cMBb4o3NuDLAPuN3fkiIndAriAuAJv2uJBDPrAVyI98OtP9DNzK6K9Ocq4Luw0DnpJ4FHnHNP+V1PpIW6ON8AzvG5lEgoBi4InZ9+FDjdzP7hb0mREWoN4Zz7BHgamOBvRRFRBpSF9TbNxgv8oPos8L5zbpvfhUTIGcB659x251wd8BQwOdIfqoDvokKDz/4CfOSc+5Xf9USKmfUys4zQ42TgTGClr0VFgHPue865bOdcPl5X5+vOuYi3EDqamXULDQol1GV9FhC4q12cc1uBUjM7KbRpGhCoAbAHuZyAds+HbAQmmVlK6N/eaXjjniJKAd8KM5sFvAOcZGZlZvZVv2uKgGLgi3gtvaZLVM71u6gI6Ae8YWZLgQV45+ADewlZF9AHeNvMlgDzgeedcy/5XFOk3Aw8Evq7Oxr4ib/lREboh9qZeK3aQAr1xMwG3gc+xMveiE9Zq8vkREREAkgteBERkQBSwIuIiASQAl5ERCSAFPAiIiIBpIAXEREJIAW8iIhIACngRaRNzKy/mc1uw3GVh9j+kJl9vv0rE5HWKOBFpE2cc5udc74EdGgFLhE5Cgp4kQAxs3wz+8jMHjCz5Wb279AUva0dO8fMfm5m883sYzP7TGh7rJn9PzNbYGZLzez6sPdeFnqcYmaPm9kKM3vazN4zs6Kw977HzJaY2btm1ifsY88ws4WhzzsvdGySmf3VzD4MrZw2NbT9S2b2nJm9DrxmZv3M7M3QrIvLmuoVkdYp4EWCZwjwB+fcCKAcmH6YY+OccxOAbwF3hbZ9FW85y/HAeOBaMxt40Ou+Aex2zg0H/gsYF7avG/Cuc64QeBO4NmxfPt7iMJ8D7jezJOBGwDnnRuHNSf630HbwFlj5vHPuVOAK4OXQyoCFwOIj/kmIdGHq9hIJnvXOucWhx4vwQvVQnmrluLOAgrDz5el4Pxo+DnvdFOA3AM65ZaH50pvUAk3z/S/Cm2e8yePOuUZgtZmtA4aG3ut3ofdaaWYbgBNDx7/inNsVerwAeDC0CuIzYd9RRFqhFrxI8OwPe9zA4X/I72/lOANuds6NDt0GOuf+fRSfX+cOLHJx8OcfvPjFkRbD2Nd8oHNvAqcAm4CHzOzqo6hJpMtRwIvIwV4Gvh5qKWNmJ4ZW/Ao3F7gstH84MKqN732pmcWY2WBgELAKeAu4sumzgNzQ9hbMLA/Y5px7APgzwV4fXeS4qYteRA72Z7zu+vdDa1dvBy466Jj78M6VrwBWAsuBija890a8ZV67Azc452rM7D7gj2b2IVAPfMk5t9/76BZOA/7TzOqASkAteJHD0HKxInLUzCwWiA8F9GDgVeAk51ytz6WJSIha8CJyLFKAN0Ld+AZ8Q+EuEl3UghcJODP7A1B80ObfOOf+6kc9ItIxFPAiIiIBpFH0IiIiAaSAFxERCSAFvIiISAAp4EVERALo/wOBfCK4RykfBQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighbors = np.arange(1, 9)\n",
    "train_accuracy = np.empty(len(neighbors))\n",
    "test_accuracy = np.empty(len(neighbors))\n",
    "\n",
    "# Loop over K values\n",
    "for i, k in enumerate(neighbors):\n",
    "    knn = KNeighborsClassifier(n_neighbors=k, n_jobs=5)\n",
    "    knn.fit(X_train, y_train)\n",
    "    \n",
    "    # Compute training and test data accuracy\n",
    "    train_accuracy[i] = knn.score(X_train, y_train)\n",
    "    test_accuracy[i] = knn.score(X_test, y_test)\n",
    "    \n",
    "    \n",
    "# Generate plot\n",
    "plt.plot(neighbors, test_accuracy, label = 'Testing dataset Accuracy')\n",
    "plt.plot(neighbors, train_accuracy, label = 'Training dataset Accuracy')\n",
    " \n",
    "plt.legend()\n",
    "plt.xlabel('n_neighbors')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "c46ada92-9d85-4c9f-8879-f40ebfc32d9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.99615801, 0.84478013, 0.8355233 , 0.81376565, 0.80676158,\n",
       "       0.79823383, 0.79616128, 0.79164755])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "abf34809-0562-4e5e-932e-8035025c79aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.6797217 , 0.7049525 , 0.71456697, 0.73364298, 0.73645278,\n",
       "       0.74425139, 0.74669801, 0.75157215])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "63a219a3-f3d8-437e-bfe4-41ec2fc55c76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7466980140298565"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn= KNeighborsClassifier(n_neighbors=7, n_jobs=5)\n",
    "knn.fit(X_train,y_train)\n",
    "y_pred= knn.predict(X_test)\n",
    "metrics.accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89781df0-fa96-4fb7-bb57-adc9ca8f7cc3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Feature importance is not defined for the KNN Classification algorithm. There is no easy way to compute the features\n",
    "#responsible for a classification here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a511f19a-9918-4106-8ca9-ee4a5cf09ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best alpha using built-in LassoCV: 0.010124\n",
      "Best score using built-in LassoCV: 0.312563\n"
     ]
    }
   ],
   "source": [
    "#Using Lasso Model to further investigate feature importance \n",
    "from sklearn.linear_model import RidgeCV, LassoCV, Ridge, Lasso\n",
    "reg = LassoCV()\n",
    "reg.fit(X_train, y_train)\n",
    "print(\"Best alpha using built-in LassoCV: %f\" % reg.alpha_)\n",
    "print(\"Best score using built-in LassoCV: %f\" %reg.score(X,y))\n",
    "coef = pd.Series(reg.coef_, index = X.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "67922c08-ac70-4797-b090-511c0abdf30d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lasso picked 13 variables and eliminated the other 2 variables\n"
     ]
    }
   ],
   "source": [
    "print(\"Lasso picked \" + str(sum(coef != 0)) + \" variables and eliminated the other \" +  str(sum(coef == 0)) + \" variables\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b1049a89-2a2d-4168-9e96-ec038919550b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Feature importance using Lasso Model')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "imp_coef = coef.sort_values()\n",
    "import matplotlib\n",
    "matplotlib.rcParams['figure.figsize'] = (8.0, 10.0)\n",
    "imp_coef.plot(kind = \"barh\")\n",
    "plt.title(\"Feature importance using Lasso Model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12fb4898-91da-426b-8ea6-d422b94f452d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "272d8818-4d46-4ed9-ae0e-9eb41601166d",
   "metadata": {},
   "source": [
    "## XGBOOST Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f1690cdc-d722-4c52-bf6e-a5b2f4be95f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting xgboost\n",
      "  Downloading xgboost-1.5.0-py3-none-macosx_10_14_x86_64.macosx_10_15_x86_64.macosx_11_0_x86_64.whl (1.3 MB)\n",
      "     |████████████████████████████████| 1.3 MB 2.2 MB/s            \n",
      "\u001b[?25hRequirement already satisfied: scipy in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from xgboost) (1.6.3)\n",
      "Requirement already satisfied: numpy in /Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages (from xgboost) (1.19.5)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.5.0\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fb23dd96-d0c8-4468-844d-5973ea6addd1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decade                     int64\n",
      "tempo_disc                object\n",
      "multi_artist              object\n",
      "log_duration_ms          float64\n",
      "danceability_disc         object\n",
      "explicit                  object\n",
      "instrumentalness_disc     object\n",
      "key                       object\n",
      "liveness_disc             object\n",
      "loudness_disc             object\n",
      "speechiness_disc          object\n",
      "valence_disc              object\n",
      "acousticness_disc         object\n",
      "energy_disc               object\n",
      "mode                       int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X_test.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9b47a0db-13cc-4a40-8e0d-3797206f8e5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "popularity_disc    object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(y_train.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d8b444ad-10fe-47ff-af75-4119f0050870",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "lbl = preprocessing.LabelEncoder()\n",
    "X_test['tempo_disc'] = lbl.fit_transform(X_test['tempo_disc'].astype(str))\n",
    "X_test['multi_artist'] = lbl.fit_transform(X_test['multi_artist'].astype(str))\n",
    "X_test['danceability_disc'] = lbl.fit_transform(X_test['danceability_disc'].astype(str))\n",
    "X_test['explicit'] = lbl.fit_transform(X_test['explicit'].astype(str))\n",
    "X_test['instrumentalness_disc'] = lbl.fit_transform(X_test['instrumentalness_disc'].astype(str))\n",
    "X_test['key'] = lbl.fit_transform(X_test['key'].astype(str))\n",
    "X_test['liveness_disc'] = lbl.fit_transform(X_test['liveness_disc'].astype(str))\n",
    "X_test['loudness_disc'] = lbl.fit_transform(X_test['loudness_disc'].astype(str))\n",
    "X_test['speechiness_disc'] = lbl.fit_transform(X_test['speechiness_disc'].astype(str))\n",
    "X_test['valence_disc'] = lbl.fit_transform(X_test['valence_disc'].astype(str))\n",
    "X_test['acousticness_disc'] = lbl.fit_transform(X_test['acousticness_disc'].astype(str))\n",
    "X_test['energy_disc'] = lbl.fit_transform(X_test['energy_disc'].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "20208a90-9fc6-4d08-b5f9-d55536229fe2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decade                     int64\n",
      "tempo_disc                 int64\n",
      "multi_artist               int64\n",
      "log_duration_ms          float64\n",
      "danceability_disc          int64\n",
      "explicit                   int64\n",
      "instrumentalness_disc      int64\n",
      "key                        int64\n",
      "liveness_disc              int64\n",
      "loudness_disc              int64\n",
      "speechiness_disc           int64\n",
      "valence_disc               int64\n",
      "acousticness_disc          int64\n",
      "energy_disc                int64\n",
      "mode                       int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X_test.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e710aa7f-82e1-4f7c-8f26-e753d9cec8c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import xgboost as xgb\n",
    "# model=xgb.XGBClassifier(random_state=1,learning_rate=0.01)\n",
    "# model.fit(X_train, y_train)\n",
    "# model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "acf5e773-dc82-4114-a72c-bf9873e2f5dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decade                     int64\n",
      "tempo_disc                object\n",
      "multi_artist              object\n",
      "log_duration_ms          float64\n",
      "danceability_disc         object\n",
      "explicit                  object\n",
      "instrumentalness_disc     object\n",
      "key                       object\n",
      "liveness_disc             object\n",
      "loudness_disc             object\n",
      "speechiness_disc          object\n",
      "valence_disc              object\n",
      "acousticness_disc         object\n",
      "energy_disc               object\n",
      "mode                       int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X_train.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2e4be189-9aa8-4fdf-91a9-b4c489bf88bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "lbl = preprocessing.LabelEncoder()\n",
    "X_train['tempo_disc'] = lbl.fit_transform(X_train['tempo_disc'].astype(str))\n",
    "X_train['multi_artist'] = lbl.fit_transform(X_train['multi_artist'].astype(str))\n",
    "X_train['danceability_disc'] = lbl.fit_transform(X_train['danceability_disc'].astype(str))\n",
    "X_train['explicit'] = lbl.fit_transform(X_train['explicit'].astype(str))\n",
    "X_train['instrumentalness_disc'] = lbl.fit_transform(X_train['instrumentalness_disc'].astype(str))\n",
    "X_train['key'] = lbl.fit_transform(X_train['key'].astype(str))\n",
    "X_train['liveness_disc'] = lbl.fit_transform(X_train['liveness_disc'].astype(str))\n",
    "X_train['loudness_disc'] = lbl.fit_transform(X_train['loudness_disc'].astype(str))\n",
    "X_train['speechiness_disc'] = lbl.fit_transform(X_train['speechiness_disc'].astype(str))\n",
    "X_train['valence_disc'] = lbl.fit_transform(X_train['valence_disc'].astype(str))\n",
    "X_train['acousticness_disc'] = lbl.fit_transform(X_train['acousticness_disc'].astype(str))\n",
    "X_train['energy_disc'] = lbl.fit_transform(X_train['energy_disc'].astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6c67aa29-398d-42b2-8402-6156c3d15ffb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "decade                     int64\n",
      "tempo_disc                 int64\n",
      "multi_artist               int64\n",
      "log_duration_ms          float64\n",
      "danceability_disc          int64\n",
      "explicit                   int64\n",
      "instrumentalness_disc      int64\n",
      "key                        int64\n",
      "liveness_disc              int64\n",
      "loudness_disc              int64\n",
      "speechiness_disc           int64\n",
      "valence_disc               int64\n",
      "acousticness_disc          int64\n",
      "energy_disc                int64\n",
      "mode                       int64\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(X_train.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f1780b45-fd7c-4e33-a355-dead23880c02",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[20:27:11] WARNING: /Users/travis/build/dmlc/xgboost/src/learner.cc:1115: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'multi:softprob' was changed from 'merror' to 'mlogloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, enable_categorical=False,\n",
       "              gamma=0, gpu_id=-1, importance_type=None,\n",
       "              interaction_constraints='', learning_rate=0.01, max_delta_step=0,\n",
       "              max_depth=6, min_child_weight=1, missing=nan,\n",
       "              monotone_constraints='()', n_estimators=100, n_jobs=4,\n",
       "              num_parallel_tree=1, objective='multi:softprob', predictor='auto',\n",
       "              random_state=1, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n",
       "              subsample=1, tree_method='exact', validate_parameters=1,\n",
       "              verbosity=None)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import xgboost as xgb\n",
    "model=xgb.XGBClassifier(random_state=1,learning_rate=0.01)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6bb7dbda-6b2b-4bbe-b1d9-e572b30b39da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7604411567941587"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_xg= model.predict(X_test)\n",
    "metrics.accuracy_score(y_test,y_pred_xg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "f32b8e32-60e4-4e24-a2fe-639c4ea8dfaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature importance of XGBOOST MOdel \n",
    "importances = pd.DataFrame(data={\n",
    "    'Attribute': X_train.columns,\n",
    "    'Importance': model.feature_importances_\n",
    "})\n",
    "importances = importances.sort_values(by='Importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3e2e5b25-e3be-44db-bcd4-91617b3315c3",
   "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>Attribute</th>\n",
       "      <th>Importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>decade</td>\n",
       "      <td>0.829424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>instrumentalness_disc</td>\n",
       "      <td>0.065661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>explicit</td>\n",
       "      <td>0.032620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>acousticness_disc</td>\n",
       "      <td>0.014279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>loudness_disc</td>\n",
       "      <td>0.008825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>liveness_disc</td>\n",
       "      <td>0.008751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>log_duration_ms</td>\n",
       "      <td>0.007997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tempo_disc</td>\n",
       "      <td>0.006648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>danceability_disc</td>\n",
       "      <td>0.005317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>energy_disc</td>\n",
       "      <td>0.005285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>speechiness_disc</td>\n",
       "      <td>0.004194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>multi_artist</td>\n",
       "      <td>0.004009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>mode</td>\n",
       "      <td>0.003808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>valence_disc</td>\n",
       "      <td>0.002623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>key</td>\n",
       "      <td>0.000559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Attribute  Importance\n",
       "0                  decade    0.829424\n",
       "6   instrumentalness_disc    0.065661\n",
       "5                explicit    0.032620\n",
       "12      acousticness_disc    0.014279\n",
       "9           loudness_disc    0.008825\n",
       "8           liveness_disc    0.008751\n",
       "3         log_duration_ms    0.007997\n",
       "1              tempo_disc    0.006648\n",
       "4       danceability_disc    0.005317\n",
       "13            energy_disc    0.005285\n",
       "10       speechiness_disc    0.004194\n",
       "2            multi_artist    0.004009\n",
       "14                   mode    0.003808\n",
       "11           valence_disc    0.002623\n",
       "7                     key    0.000559"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "bd83dbd7-18f6-4e91-9fd4-76fbf9ff9a77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature importance xgboost \n",
    "from xgboost import plot_importance\n",
    "plot_importance(model)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "664896f1-2d2a-489e-a6e7-0fbac6085739",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e57a5605",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73a81101",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 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": null,
   "id": "d1b2c794",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6c9c927",
   "metadata": {},
   "outputs": [],
   "source": [
    "modelSVC = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8bd2708",
   "metadata": {},
   "outputs": [],
   "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": null,
   "id": "c44f5a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make predictions with the model\n",
    "\n",
    "predictions = modelSVC.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31258f72",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Measure the performance of our model\n",
    "\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f5db95d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(classification_report(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d321a32d",
   "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": null,
   "id": "0c34b38f",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(confusion_matrix(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "967faa7e",
   "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": null,
   "id": "9ede26f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f19ee66",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Accuracy:\",metrics.accuracy_score(y_test, predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b80a74fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c28be97c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# XG CatBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9a34ab1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build Model\n",
    "\n",
    "from catboost import CatBoostClassifier\n",
    "XGcat = CatBoostClassifier(n_estimators=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80d50883",
   "metadata": {},
   "outputs": [],
   "source": [
    "XGcat.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2f0b7c0",
   "metadata": {},
   "outputs": [],
   "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": null,
   "id": "bff1c33f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get importance\n",
    "importance = XGcat.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58198a2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# summarize feature importance\n",
    "for i,v in enumerate(importance):\n",
    "\tprint('Feature: %0d, Score: %.5f' % (i,v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1819c40",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22e0a49c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot feature importance\n",
    "pyplot.bar([x for x in range(len(importance))], importance)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d991fd8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "179b6d57",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d623b8f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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