spotify_knn_rf.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Assessment - BCI3333 Machine Learning Application\n",
    "## Noel Foo Sei Wee CB20066\n",
    "\n",
    "spotify_all_genres_tracks.csv (https://www.kaggle.com/datasets/laurabarreda/spotify-tracks-by-genre-8-genres-classification)\n",
    "\n",
    "KNN & Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 track_id                                       playlist_url  \\\n",
      "0  4Gia17DzXBhYFbYiJj6SyW  https://open.spotify.com/playlist/7qACZGMjyo64...   \n",
      "1  1BjYNhg7JhVfQdxqEThBwn  https://open.spotify.com/playlist/7qACZGMjyo64...   \n",
      "2  2Cg3GUkhjX96nO4p2WRlIa  https://open.spotify.com/playlist/7qACZGMjyo64...   \n",
      "3  5bC6ONDsL88snGN6QasjZH  https://open.spotify.com/playlist/7qACZGMjyo64...   \n",
      "4  2TKykeHeVKsBqZC8M3SKcN  https://open.spotify.com/playlist/7qACZGMjyo64...   \n",
      "\n",
      "        playlist_name               track_name  track_popularity  \\\n",
      "0  The Sound of Blues              Working Man                51   \n",
      "1  The Sound of Blues            Long Way Home                38   \n",
      "2  The Sound of Blues        She's A Sweet One                49   \n",
      "3  The Sound of Blues                  Help Me                59   \n",
      "4  The Sound of Blues  Take Out Some Insurance                51   \n",
      "\n",
      "                  artist_name  \\\n",
      "0                   Otis Rush   \n",
      "1  Clarence \"Gatemouth\" Brown   \n",
      "2                Junior Wells   \n",
      "3     Sonny Boy Williamson II   \n",
      "4                  Jimmy Reed   \n",
      "\n",
      "                                               album  \\\n",
      "0                            Mourning In The Morning   \n",
      "1                                      Long Way Home   \n",
      "2  Calling All Blues - The Chief, Profile & USA R...   \n",
      "3                               More Real Folk Blues   \n",
      "4                                  Rockin' With Reed   \n",
      "\n",
      "                                         album_cover  \\\n",
      "0  https://i.scdn.co/image/ab67616d0000b273fea221...   \n",
      "1  https://i.scdn.co/image/ab67616d0000b2730e1f13...   \n",
      "2  https://i.scdn.co/image/ab67616d0000b27399b18c...   \n",
      "3  https://i.scdn.co/image/ab67616d0000b273b48c81...   \n",
      "4  https://i.scdn.co/image/ab67616d0000b2739b7573...   \n",
      "\n",
      "                                       artist_genres  artist_popularity  ...  \\\n",
      "0  ['blues', 'blues rock', 'chicago blues', 'elec...                 41  ...   \n",
      "1  ['blues', 'blues rock', 'memphis blues', 'mode...                 33  ...   \n",
      "2  ['blues', 'blues rock', 'chicago blues', 'elec...                 41  ...   \n",
      "3  ['acoustic blues', 'blues', 'blues rock', 'chi...                 46  ...   \n",
      "4  ['blues', 'blues rock', 'chicago blues', 'elec...                 42  ...   \n",
      "\n",
      "   mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
      "0     1       0.0436         0.492          0.000418     0.204    0.841   \n",
      "1     0       0.0380         0.910          0.048000     0.120    0.425   \n",
      "2     1       0.0542         0.150          0.026500     0.202    0.713   \n",
      "3     0       0.0430         0.597          0.021300     0.610    0.771   \n",
      "4     1       0.0513         0.663          0.000000     0.122    0.566   \n",
      "\n",
      "     tempo  duration_ms  time_signature  genre  \n",
      "0  103.355       147800               4  blues  \n",
      "1   78.033       338333               4  blues  \n",
      "2  122.863       181786               4  blues  \n",
      "3  114.216       188200               4  blues  \n",
      "4  111.330       143332               4  blues  \n",
      "\n",
      "[5 rows x 24 columns] \n",
      "\n",
      "\n",
      "(9198, 24) \n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9198 entries, 0 to 9197\n",
      "Data columns (total 24 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   track_id           9198 non-null   object \n",
      " 1   playlist_url       9198 non-null   object \n",
      " 2   playlist_name      9198 non-null   object \n",
      " 3   track_name         9198 non-null   object \n",
      " 4   track_popularity   9198 non-null   int64  \n",
      " 5   artist_name        9198 non-null   object \n",
      " 6   album              9198 non-null   object \n",
      " 7   album_cover        9198 non-null   object \n",
      " 8   artist_genres      9198 non-null   object \n",
      " 9   artist_popularity  9198 non-null   int64  \n",
      " 10  danceability       9198 non-null   float64\n",
      " 11  energy             9198 non-null   float64\n",
      " 12  key                9198 non-null   int64  \n",
      " 13  loudness           9198 non-null   float64\n",
      " 14  mode               9198 non-null   int64  \n",
      " 15  speechiness        9198 non-null   float64\n",
      " 16  acousticness       9198 non-null   float64\n",
      " 17  instrumentalness   9198 non-null   float64\n",
      " 18  liveness           9198 non-null   float64\n",
      " 19  valence            9198 non-null   float64\n",
      " 20  tempo              9198 non-null   float64\n",
      " 21  duration_ms        9198 non-null   int64  \n",
      " 22  time_signature     9198 non-null   int64  \n",
      " 23  genre              9198 non-null   object \n",
      "dtypes: float64(9), int64(6), object(9)\n",
      "memory usage: 1.7+ MB\n",
      "None \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "# import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# import dataset\n",
    "dataset = pd.read_csv('spotify_all_genres_tracks.csv')\n",
    "print(dataset.head(), '\\n\\n')\n",
    "print(dataset.shape, '\\n\\n')\n",
    "print(dataset.info(), '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   track_popularity  artist_popularity  danceability  energy  key  loudness  \\\n",
      "0                51                 41         0.633   0.625    0   -10.951   \n",
      "1                38                 33         0.728   0.054   11   -22.557   \n",
      "2                49                 41         0.705   0.483    1   -12.209   \n",
      "3                59                 46         0.745   0.436    5    -9.617   \n",
      "4                51                 42         0.747   0.288    9   -14.435   \n",
      "\n",
      "   mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
      "0     1       0.0436         0.492          0.000418     0.204    0.841   \n",
      "1     0       0.0380         0.910          0.048000     0.120    0.425   \n",
      "2     1       0.0542         0.150          0.026500     0.202    0.713   \n",
      "3     0       0.0430         0.597          0.021300     0.610    0.771   \n",
      "4     1       0.0513         0.663          0.000000     0.122    0.566   \n",
      "\n",
      "     tempo  duration_ms  time_signature  genre  \n",
      "0  103.355       147800               4  blues  \n",
      "1   78.033       338333               4  blues  \n",
      "2  122.863       181786               4  blues  \n",
      "3  114.216       188200               4  blues  \n",
      "4  111.330       143332               4  blues   \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# drop unnecessary columns\n",
    "dataset.drop(['track_id', 'playlist_url', 'playlist_name', 'track_name', 'artist_name', 'album', 'album_cover', 'artist_genres'], axis=1, inplace=True)\n",
    "print(dataset.head(), '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "track_popularity     0\n",
      "artist_popularity    0\n",
      "danceability         0\n",
      "energy               0\n",
      "key                  0\n",
      "loudness             0\n",
      "mode                 0\n",
      "speechiness          0\n",
      "acousticness         0\n",
      "instrumentalness     0\n",
      "liveness             0\n",
      "valence              0\n",
      "tempo                0\n",
      "duration_ms          0\n",
      "time_signature       0\n",
      "genre                0\n",
      "dtype: int64 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# check for null values\n",
    "print(dataset.isnull().sum(), '\\n\\n')\n",
    "dataset = dataset.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 \n",
      "\n",
      "\n",
      "0 \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# check for duplicate rows\n",
    "print(dataset.duplicated().sum(), '\\n\\n')\n",
    "\n",
    "# drop duplicate rows\n",
    "dataset.drop_duplicates(inplace=True)\n",
    "print(dataset.duplicated().sum(), '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       track_popularity  artist_popularity  danceability       energy  \\\n",
      "count       9197.000000        9197.000000   9197.000000  9197.000000   \n",
      "mean          48.892247          51.161683      0.595370     0.535905   \n",
      "std           17.502421          16.081778      0.192937     0.273215   \n",
      "min            0.000000           0.000000      0.000000     0.000885   \n",
      "25%           38.000000          42.000000      0.474000     0.334000   \n",
      "50%           49.000000          52.000000      0.621000     0.579000   \n",
      "75%           61.000000          62.000000      0.744000     0.757000   \n",
      "max           95.000000         100.000000      0.984000     0.999000   \n",
      "\n",
      "               key     loudness         mode  speechiness  acousticness  \\\n",
      "count  9197.000000  9197.000000  9197.000000  9197.000000   9197.000000   \n",
      "mean      5.358921   -10.705532     0.596390     0.084686      0.362160   \n",
      "std       3.559050     6.573180     0.490648     0.081617      0.368856   \n",
      "min       0.000000   -47.001000     0.000000     0.000000      0.000002   \n",
      "25%       2.000000   -13.255000     0.000000     0.038200      0.030400   \n",
      "50%       6.000000    -8.760000     1.000000     0.049000      0.191000   \n",
      "75%       8.000000    -6.128000     1.000000     0.087500      0.731000   \n",
      "max      11.000000     1.342000     1.000000     0.827000      0.996000   \n",
      "\n",
      "       instrumentalness     liveness      valence        tempo   duration_ms  \\\n",
      "count       9197.000000  9197.000000  9197.000000  9197.000000  9.197000e+03   \n",
      "mean           0.227400     0.170267     0.503034   116.588431  2.532620e+05   \n",
      "std            0.350825     0.139710     0.265924    29.271563  1.019597e+05   \n",
      "min            0.000000     0.014500     0.000000     0.000000  3.033300e+04   \n",
      "25%            0.000002     0.090100     0.282000    93.982000  1.896920e+05   \n",
      "50%            0.001670     0.116000     0.519000   117.906000  2.282670e+05   \n",
      "75%            0.458000     0.198000     0.724000   132.699000  2.863870e+05   \n",
      "max            0.983000     0.979000     0.986000   216.090000  1.430840e+06   \n",
      "\n",
      "       time_signature  \n",
      "count     9197.000000  \n",
      "mean         3.896923  \n",
      "std          0.402023  \n",
      "min          0.000000  \n",
      "25%          4.000000  \n",
      "50%          4.000000  \n",
      "75%          4.000000  \n",
      "max          5.000000   \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# check for outliers\n",
    "print(dataset.describe(), '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   track_popularity  artist_popularity  danceability  energy  key  loudness  \\\n",
      "0                51                 41         0.633   0.625    0   -10.951   \n",
      "1                38                 33         0.728   0.054   11   -22.557   \n",
      "2                49                 41         0.705   0.483    1   -12.209   \n",
      "3                59                 46         0.745   0.436    5    -9.617   \n",
      "4                51                 42         0.747   0.288    9   -14.435   \n",
      "\n",
      "   mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
      "0     1       0.0436         0.492          0.000418     0.204    0.841   \n",
      "1     0       0.0380         0.910          0.048000     0.120    0.425   \n",
      "2     1       0.0542         0.150          0.026500     0.202    0.713   \n",
      "3     0       0.0430         0.597          0.021300     0.610    0.771   \n",
      "4     1       0.0513         0.663          0.000000     0.122    0.566   \n",
      "\n",
      "     tempo  duration_ms  time_signature  genre  \n",
      "0  103.355       147800               4      0  \n",
      "1   78.033       338333               4      0  \n",
      "2  122.863       181786               4      0  \n",
      "3  114.216       188200               4      0  \n",
      "4  111.330       143332               4      0   \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# convert categorical data to numerical data - genre\n",
    "# blues = 0, classical = 1, jazz = 2, hiphop = 3, pop = 4, reggae = 5, rock = 6, electronic = 7 as integers\n",
    "dataset['genre'] = dataset['genre'].map({'blues': 0, 'classical': 1, 'jazz': 2, 'hiphop': 3, 'pop': 4, 'reggae': 5, 'rock': 6, 'electronic': 7})\n",
    "print(dataset.head(), '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   track_popularity  artist_popularity  danceability  \\\n",
      "track_popularity           1.000000           0.780368      0.129456   \n",
      "artist_popularity          0.780368           1.000000     -0.023593   \n",
      "danceability               0.129456          -0.023593      1.000000   \n",
      "energy                     0.288364           0.150920      0.446537   \n",
      "key                       -0.001324          -0.014163      0.054717   \n",
      "loudness                   0.338657           0.207480      0.526008   \n",
      "mode                       0.005078           0.023899     -0.071770   \n",
      "speechiness                0.081427           0.062168      0.326777   \n",
      "acousticness              -0.288495          -0.137530     -0.520689   \n",
      "instrumentalness          -0.347600          -0.292190     -0.293468   \n",
      "liveness                   0.046892           0.044741     -0.002550   \n",
      "valence                    0.068579          -0.032466      0.562963   \n",
      "tempo                      0.074668           0.040968      0.025804   \n",
      "duration_ms               -0.179772          -0.138638     -0.214548   \n",
      "time_signature             0.086295           0.019213      0.281267   \n",
      "genre                      0.240143           0.081233      0.363029   \n",
      "\n",
      "                     energy       key  loudness      mode  speechiness  \\\n",
      "track_popularity   0.288364 -0.001324  0.338657  0.005078     0.081427   \n",
      "artist_popularity  0.150920 -0.014163  0.207480  0.023899     0.062168   \n",
      "danceability       0.446537  0.054717  0.526008 -0.071770     0.326777   \n",
      "energy             1.000000  0.052929  0.821609 -0.057085     0.212320   \n",
      "key                0.052929  1.000000  0.039482 -0.178142     0.031298   \n",
      "loudness           0.821609  0.039482  1.000000 -0.046373     0.213758   \n",
      "mode              -0.057085 -0.178142 -0.046373  1.000000    -0.053321   \n",
      "speechiness        0.212320  0.031298  0.213758 -0.053321     1.000000   \n",
      "acousticness      -0.800095 -0.053343 -0.697335  0.071358    -0.246566   \n",
      "instrumentalness  -0.331542 -0.013582 -0.514702 -0.014020    -0.252072   \n",
      "liveness           0.189245  0.022487  0.149467  0.005076     0.104251   \n",
      "valence            0.477138  0.040718  0.460997 -0.018051     0.221939   \n",
      "tempo              0.260068  0.014909  0.225794 -0.003908     0.021850   \n",
      "duration_ms       -0.161980 -0.024376 -0.225265 -0.010980    -0.118734   \n",
      "time_signature     0.261558  0.005188  0.254103 -0.022946     0.112004   \n",
      "genre              0.558908  0.035044  0.453318 -0.084593     0.023163   \n",
      "\n",
      "                   acousticness  instrumentalness  liveness   valence  \\\n",
      "track_popularity      -0.288495         -0.347600  0.046892  0.068579   \n",
      "artist_popularity     -0.137530         -0.292190  0.044741 -0.032466   \n",
      "danceability          -0.520689         -0.293468 -0.002550  0.562963   \n",
      "energy                -0.800095         -0.331542  0.189245  0.477138   \n",
      "key                   -0.053343         -0.013582  0.022487  0.040718   \n",
      "loudness              -0.697335         -0.514702  0.149467  0.460997   \n",
      "mode                   0.071358         -0.014020  0.005076 -0.018051   \n",
      "speechiness           -0.246566         -0.252072  0.104251  0.221939   \n",
      "acousticness           1.000000          0.333553 -0.110948 -0.408893   \n",
      "instrumentalness       0.333553          1.000000 -0.113325 -0.376083   \n",
      "liveness              -0.110948         -0.113325  1.000000  0.073369   \n",
      "valence               -0.408893         -0.376083  0.073369  1.000000   \n",
      "tempo                 -0.221150         -0.087466  0.030291  0.141534   \n",
      "duration_ms            0.119859          0.312098 -0.030901 -0.263244   \n",
      "time_signature        -0.250738         -0.121186  0.025781  0.204233   \n",
      "genre                 -0.612914         -0.025197  0.037699  0.140215   \n",
      "\n",
      "                      tempo  duration_ms  time_signature     genre  \n",
      "track_popularity   0.074668    -0.179772        0.086295  0.240143  \n",
      "artist_popularity  0.040968    -0.138638        0.019213  0.081233  \n",
      "danceability       0.025804    -0.214548        0.281267  0.363029  \n",
      "energy             0.260068    -0.161980        0.261558  0.558908  \n",
      "key                0.014909    -0.024376        0.005188  0.035044  \n",
      "loudness           0.225794    -0.225265        0.254103  0.453318  \n",
      "mode              -0.003908    -0.010980       -0.022946 -0.084593  \n",
      "speechiness        0.021850    -0.118734        0.112004  0.023163  \n",
      "acousticness      -0.221150     0.119859       -0.250738 -0.612914  \n",
      "instrumentalness  -0.087466     0.312098       -0.121186 -0.025197  \n",
      "liveness           0.030291    -0.030901        0.025781  0.037699  \n",
      "valence            0.141534    -0.263244        0.204233  0.140215  \n",
      "tempo              1.000000    -0.045781       -0.002198  0.181701  \n",
      "duration_ms       -0.045781     1.000000       -0.049959 -0.041771  \n",
      "time_signature    -0.002198    -0.049959        1.000000  0.227111  \n",
      "genre              0.181701    -0.041771        0.227111  1.000000   \n",
      "\n",
      "\n",
      "genre                1.000000\n",
      "energy               0.558908\n",
      "loudness             0.453318\n",
      "danceability         0.363029\n",
      "track_popularity     0.240143\n",
      "time_signature       0.227111\n",
      "tempo                0.181701\n",
      "valence              0.140215\n",
      "artist_popularity    0.081233\n",
      "liveness             0.037699\n",
      "key                  0.035044\n",
      "speechiness          0.023163\n",
      "instrumentalness    -0.025197\n",
      "duration_ms         -0.041771\n",
      "mode                -0.084593\n",
      "acousticness        -0.612914\n",
      "Name: genre, dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   track_popularity  artist_popularity  danceability  energy  loudness  mode  \\\n",
      "0                51                 41         0.633   0.625   -10.951     1   \n",
      "1                38                 33         0.728   0.054   -22.557     0   \n",
      "2                49                 41         0.705   0.483   -12.209     1   \n",
      "3                59                 46         0.745   0.436    -9.617     0   \n",
      "4                51                 42         0.747   0.288   -14.435     1   \n",
      "\n",
      "   acousticness  valence    tempo  time_signature  genre  \n",
      "0         0.492    0.841  103.355               4      0  \n",
      "1         0.910    0.425   78.033               4      0  \n",
      "2         0.150    0.713  122.863               4      0  \n",
      "3         0.597    0.771  114.216               4      0  \n",
      "4         0.663    0.566  111.330               4      0   \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# correlation matrix\n",
    "print(dataset.corr(), '\\n\\n')\n",
    "\n",
    "#Data Visualization for correlation\n",
    "import seaborn as sns\n",
    "corr = dataset.corr().sort_values(by='genre', ascending=False)\n",
    "print(corr['genre'])\n",
    "sns.heatmap(corr['genre'].drop('genre').to_frame(), cmap='coolwarm', annot=True, fmt='.2f')\n",
    "plt.show()\n",
    "\n",
    "# drop weak correlated columns\n",
    "# liveness, key, duration_ms, instrumentalness, speechiness\n",
    "dataset.drop(['liveness', 'key', 'duration_ms', 'instrumentalness', 'speechiness'], axis=1, inplace=True)\n",
    "print(dataset.head(), '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 51.     41.      0.633 ...   0.841 103.355   4.   ]\n",
      " [ 38.     33.      0.728 ...   0.425  78.033   4.   ]\n",
      " [ 49.     41.      0.705 ...   0.713 122.863   4.   ]\n",
      " ...\n",
      " [  7.      7.      0.716 ...   0.181 130.012   4.   ]\n",
      " [ 24.     27.      0.763 ...   0.687 147.998   4.   ]\n",
      " [ 35.     37.      0.677 ...   0.407 134.774   4.   ]] \n",
      "\n",
      "\n",
      "[0 0 0 ... 7 7 7] \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# set independent and dependent variables\n",
    "x = dataset.iloc[:, :-1].values\n",
    "y = dataset.iloc[:, -1].values\n",
    "\n",
    "print(x, '\\n\\n')\n",
    "print(y, '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 57.     55.      0.575 ...   0.738  89.846   4.   ]\n",
      " [ 63.     57.      0.848 ...   0.787  97.736   4.   ]\n",
      " [ 18.     27.      0.404 ...   0.81  203.196   4.   ]\n",
      " ...\n",
      " [ 36.     52.      0.388 ...   0.484  98.193   3.   ]\n",
      " [ 70.     58.      0.65  ...   0.649  89.887   4.   ]\n",
      " [ 32.     49.      0.619 ...   0.42  143.64    4.   ]] \n",
      "\n",
      "\n",
      "[[4.90000e+01 4.50000e+01 6.89000e-01 ... 9.61000e-01 1.21019e+02\n",
      "  4.00000e+00]\n",
      " [7.90000e+01 7.80000e+01 9.24000e-01 ... 6.21000e-01 1.33974e+02\n",
      "  4.00000e+00]\n",
      " [5.20000e+01 3.90000e+01 8.08000e-01 ... 9.14000e-01 1.40847e+02\n",
      "  4.00000e+00]\n",
      " ...\n",
      " [4.60000e+01 5.50000e+01 1.62000e-01 ... 9.03000e-02 9.40090e+01\n",
      "  3.00000e+00]\n",
      " [4.50000e+01 4.00000e+01 6.14000e-01 ... 7.50000e-02 1.12972e+02\n",
      "  4.00000e+00]\n",
      " [8.60000e+01 7.30000e+01 3.45000e-01 ... 2.32000e-01 1.79773e+02\n",
      "  3.00000e+00]] \n",
      "\n",
      "\n",
      "[6 3 0 ... 1 4 2] \n",
      "\n",
      "\n",
      "[7 3 5 ... 1 7 4] \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# split dataset into training and testing set\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, stratify=y, test_size=0.2, random_state=0)\n",
    "print(x_train, '\\n\\n')\n",
    "print(x_test, '\\n\\n')\n",
    "print(y_train, '\\n\\n')\n",
    "print(y_test, '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.46679234  0.23793489 -0.10424746 ...  0.88774608 -0.91443321\n",
      "   0.25441277]\n",
      " [ 0.81011992  0.3620081   1.30999354 ...  1.07192201 -0.64432691\n",
      "   0.25441277]\n",
      " [-1.76483688 -1.49909013 -0.99009073 ...  1.15837194  2.96599127\n",
      "   0.25441277]\n",
      " ...\n",
      " [-0.73485416  0.05182506 -1.07297665 ... -0.06696179 -0.62868197\n",
      "  -2.25795601]\n",
      " [ 1.21066875  0.42404471  0.28428028 ...  0.55322246 -0.91302962\n",
      "   0.25441277]\n",
      " [-0.96373921 -0.13428476  0.12368881 ... -0.30751811  0.92715086\n",
      "   0.25441277]] \n",
      "\n",
      "\n",
      "[[ 0.00902225 -0.38243119  0.48631471 ...  1.72593449  0.15274343\n",
      "   0.25441277]\n",
      " [ 1.72566011  1.66477687  1.70370166 ...  0.44797907  0.59624497\n",
      "   0.25441277]\n",
      " [ 0.18068603 -0.75465084  1.10277874 ...  1.54927595  0.83153528\n",
      "   0.25441277]\n",
      " ...\n",
      " [-0.16264154  0.23793489 -2.24374026 ... -1.546759   -0.77191705\n",
      "  -2.25795601]\n",
      " [-0.2198628  -0.69261423  0.09778696 ... -1.60426699 -0.12273761\n",
      "   0.25441277]\n",
      " [ 2.12620895  1.35459383 -1.29573256 ... -1.01415228  2.16412817\n",
      "  -2.25795601]] \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# feature scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "x_train = sc.fit_transform(x_train)\n",
    "x_test = sc.transform(x_test)\n",
    "print(x_train, '\\n\\n')\n",
    "print(x_test, '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(criterion='entropy', random_state=0)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# fitting knn classifier to training set\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "classifierKNN = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=2)\n",
    "classifierKNN.fit(x_train, y_train)\n",
    "\n",
    "# fitting random forest classifier to training set\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifierRF = RandomForestClassifier(n_estimators=100, criterion='entropy', random_state=0)\n",
    "classifierRF.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7 4 5 ... 1 7 4] \n",
      "\n",
      "\n",
      "[7 4 5 ... 1 7 4] \n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# predict test set results\n",
    "y_predKNN = classifierKNN.predict(x_test)\n",
    "y_predRF = classifierRF.predict(x_test)\n",
    "print(y_predKNN, '\\n\\n')\n",
    "print(y_predRF, '\\n\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[123   0  29   6   2  21  15  11]\n",
      " [  9 167  22   0   0   0   0   0]\n",
      " [ 35  16 136   2   4   1   4   2]\n",
      " [ 12   0   1  99  44  22   8  19]\n",
      " [  4   3   5  29 177   3  17  12]\n",
      " [ 25   0   1  33   7 122   6  14]\n",
      " [  9   0   5  12  40   1 141  19]\n",
      " [ 18   2  11  34  17  19  17 227]] \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[139   0  24   5   1  12   9  17]\n",
      " [  5 175  17   0   0   0   0   1]\n",
      " [ 39  17 136   0   2   1   1   4]\n",
      " [ 11   0   1  93  38  23  14  25]\n",
      " [  0   0   3  29 185   3  21   9]\n",
      " [ 25   0   1  22   7 134   4  15]\n",
      " [  9   0   3   3  21   2 175  14]\n",
      " [ 15   1  10  19  16  19  17 248]] \n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# making the confusion matrix - KNN\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cmKNN = confusion_matrix(y_test, y_predKNN)\n",
    "print(cmKNN, '\\n\\n')\n",
    "\n",
    "# plot confusion matrix -KNN\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cmKNN, annot=True, cmap='Blues', fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix KNN')\n",
    "plt.show()\n",
    "\n",
    "# making the confusion matrix - RF\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cmRF = confusion_matrix(y_test, y_predRF)\n",
    "print(cmRF, '\\n\\n')\n",
    "\n",
    "# plot confusion matrix -RF\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(10, 7))\n",
    "sns.heatmap(cmRF, annot=True, cmap='Blues', fmt='d')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.title('Confusion Matrix RF')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K Nearest Neighbors Classifier\n",
      "Precision Score:  0.6554174799681497\n",
      "Recall Score:  0.6478260869565218\n",
      "F1 Score:  0.6498827397197631\n",
      "Accuracy Score:  0.6478260869565218 \n",
      "\n",
      "\n",
      "Random Forest Classifier\n",
      "Precision Score:  0.6987265515997398\n",
      "Recall Score:  0.6983695652173914\n",
      "F1 Score:  0.6972579157418715\n",
      "Accuracy Score:  0.6983695652173914\n"
     ]
    }
   ],
   "source": [
    "# precision score, recall score, f-score, accuracy score - KNN\n",
    "from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n",
    "print('K Nearest Neighbors Classifier')\n",
    "print('Precision Score: ', precision_score(y_test, y_predKNN, average='weighted'))\n",
    "print('Recall Score: ', recall_score(y_test, y_predKNN, average='weighted'))\n",
    "print('F1 Score: ', f1_score(y_test, y_predKNN, average='weighted'))\n",
    "print('Accuracy Score: ', accuracy_score(y_test, y_predKNN), '\\n\\n')\n",
    "\n",
    "# precision score, recall score, f-score, accuracy score - RF\n",
    "print('Random Forest Classifier')\n",
    "print('Precision Score: ', precision_score(y_test, y_predRF, average='weighted'))\n",
    "print('Recall Score: ', recall_score(y_test, y_predRF, average='weighted'))\n",
    "print('F1 Score: ', f1_score(y_test, y_predRF, average='weighted'))\n",
    "print('Accuracy Score: ', accuracy_score(y_test, y_predRF))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random Forest Classifier has the highest accuracy score\n",
    "# therefore, it is the best model for this dataset\n",
    "# save random forest classifier model to pickle file\n",
    "import pickle\n",
    "pickle.dump(classifierRF, open('spotify_rf.pkl', 'wb'))"
   ]
  }
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