ml/song_genre-multi_class_classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Song Genre - Multi-Class Classification**\n",
    "\n",
    "---\n",
    "\n",
    "## **Introduction**\n",
    "\n",
    "In this notebook, I will be working on a multi-class classification problem. The dataset that I will be using is a collection of about 30000 songs from spotify. Each song has 23 columns, including the genre of the song. The dataset can be found [here](https://www.kaggle.com/datasets/joebeachcapital/30000-spotify-songs).\n",
    "\n",
    "**The goal of this notebook is to predict the genre of a song based on the given features.**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **Table of Contents**\n",
    "\n",
    "1. [Importing Libraries and Data](#1.-Importing-Libraries-and-Data)\n",
    "2. [Exploratory Data Analysis](#2.-Exploratory-Data-Analysis)\n",
    "3. [Data Preprocessing](#3.-Data-Preprocessing)  \n",
    "   3.1 [Dropping Columns & Missing Values](#3.1-Dropping-Columns-&-Missing-Values)  \n",
    "   3.2 [Distribution and Outliers](#3.2-Distribution-and-Outliers)  \n",
    "   3.3 [Scaling Numerical Columns](#3.3-Scaling-Numerical-Columns)  \n",
    "   3.4 [Seperating Features and Target Variable + One-Hot Encoding](#3.4-Seperating-Features-and-Target-Variable-+-One-Hot-Encoding)\n",
    "4. [Building the Model](#4.-Building-the-Model)  \n",
    "   4.1 [Trying Different Models](#4.1-Trying-Different-Models)  \n",
    "   4.2 [Hyperparameter Tuning](#4.2-Hyperparameter-Tuning)  \n",
    "   4.3 [Recursive Feature Elimination](#4.3-Recursive-Feature-Elimination)\n",
    "5. [Conclusion](#5.-Conclusion)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **1. Importing Libraries and Data**\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>track_id</th>\n",
       "      <th>track_name</th>\n",
       "      <th>track_artist</th>\n",
       "      <th>track_popularity</th>\n",
       "      <th>track_album_id</th>\n",
       "      <th>track_album_name</th>\n",
       "      <th>track_album_release_date</th>\n",
       "      <th>playlist_name</th>\n",
       "      <th>playlist_id</th>\n",
       "      <th>playlist_genre</th>\n",
       "      <th>...</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6f807x0ima9a1j3VPbc7VN</td>\n",
       "      <td>I Don't Care (with Justin Bieber) - Loud Luxur...</td>\n",
       "      <td>Ed Sheeran</td>\n",
       "      <td>66</td>\n",
       "      <td>2oCs0DGTsRO98Gh5ZSl2Cx</td>\n",
       "      <td>I Don't Care (with Justin Bieber) [Loud Luxury...</td>\n",
       "      <td>2019-06-14</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.634</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0653</td>\n",
       "      <td>0.518</td>\n",
       "      <td>122.036</td>\n",
       "      <td>194754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0r7CVbZTWZgbTCYdfa2P31</td>\n",
       "      <td>Memories - Dillon Francis Remix</td>\n",
       "      <td>Maroon 5</td>\n",
       "      <td>67</td>\n",
       "      <td>63rPSO264uRjW1X5E6cWv6</td>\n",
       "      <td>Memories (Dillon Francis Remix)</td>\n",
       "      <td>2019-12-13</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>-4.969</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.3570</td>\n",
       "      <td>0.693</td>\n",
       "      <td>99.972</td>\n",
       "      <td>162600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1z1Hg7Vb0AhHDiEmnDE79l</td>\n",
       "      <td>All the Time - Don Diablo Remix</td>\n",
       "      <td>Zara Larsson</td>\n",
       "      <td>70</td>\n",
       "      <td>1HoSmj2eLcsrR0vE9gThr4</td>\n",
       "      <td>All the Time (Don Diablo Remix)</td>\n",
       "      <td>2019-07-05</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.432</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0742</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.613</td>\n",
       "      <td>124.008</td>\n",
       "      <td>176616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>75FpbthrwQmzHlBJLuGdC7</td>\n",
       "      <td>Call You Mine - Keanu Silva Remix</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "      <td>60</td>\n",
       "      <td>1nqYsOef1yKKuGOVchbsk6</td>\n",
       "      <td>Call You Mine - The Remixes</td>\n",
       "      <td>2019-07-19</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>-3.778</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.2040</td>\n",
       "      <td>0.277</td>\n",
       "      <td>121.956</td>\n",
       "      <td>169093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1e8PAfcKUYoKkxPhrHqw4x</td>\n",
       "      <td>Someone You Loved - Future Humans Remix</td>\n",
       "      <td>Lewis Capaldi</td>\n",
       "      <td>69</td>\n",
       "      <td>7m7vv9wlQ4i0LFuJiE2zsQ</td>\n",
       "      <td>Someone You Loved (Future Humans Remix)</td>\n",
       "      <td>2019-03-05</td>\n",
       "      <td>Pop Remix</td>\n",
       "      <td>37i9dQZF1DXcZDD7cfEKhW</td>\n",
       "      <td>pop</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.672</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0833</td>\n",
       "      <td>0.725</td>\n",
       "      <td>123.976</td>\n",
       "      <td>189052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 track_id                                         track_name  \\\n",
       "0  6f807x0ima9a1j3VPbc7VN  I Don't Care (with Justin Bieber) - Loud Luxur...   \n",
       "1  0r7CVbZTWZgbTCYdfa2P31                    Memories - Dillon Francis Remix   \n",
       "2  1z1Hg7Vb0AhHDiEmnDE79l                    All the Time - Don Diablo Remix   \n",
       "3  75FpbthrwQmzHlBJLuGdC7                  Call You Mine - Keanu Silva Remix   \n",
       "4  1e8PAfcKUYoKkxPhrHqw4x            Someone You Loved - Future Humans Remix   \n",
       "\n",
       "       track_artist  track_popularity          track_album_id  \\\n",
       "0        Ed Sheeran                66  2oCs0DGTsRO98Gh5ZSl2Cx   \n",
       "1          Maroon 5                67  63rPSO264uRjW1X5E6cWv6   \n",
       "2      Zara Larsson                70  1HoSmj2eLcsrR0vE9gThr4   \n",
       "3  The Chainsmokers                60  1nqYsOef1yKKuGOVchbsk6   \n",
       "4     Lewis Capaldi                69  7m7vv9wlQ4i0LFuJiE2zsQ   \n",
       "\n",
       "                                    track_album_name track_album_release_date  \\\n",
       "0  I Don't Care (with Justin Bieber) [Loud Luxury...               2019-06-14   \n",
       "1                    Memories (Dillon Francis Remix)               2019-12-13   \n",
       "2                    All the Time (Don Diablo Remix)               2019-07-05   \n",
       "3                        Call You Mine - The Remixes               2019-07-19   \n",
       "4            Someone You Loved (Future Humans Remix)               2019-03-05   \n",
       "\n",
       "  playlist_name             playlist_id playlist_genre  ... key  loudness  \\\n",
       "0     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...   6    -2.634   \n",
       "1     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...  11    -4.969   \n",
       "2     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...   1    -3.432   \n",
       "3     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...   7    -3.778   \n",
       "4     Pop Remix  37i9dQZF1DXcZDD7cfEKhW            pop  ...   1    -4.672   \n",
       "\n",
       "   mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
       "0     1       0.0583        0.1020          0.000000    0.0653    0.518   \n",
       "1     1       0.0373        0.0724          0.004210    0.3570    0.693   \n",
       "2     0       0.0742        0.0794          0.000023    0.1100    0.613   \n",
       "3     1       0.1020        0.0287          0.000009    0.2040    0.277   \n",
       "4     1       0.0359        0.0803          0.000000    0.0833    0.725   \n",
       "\n",
       "     tempo  duration_ms  \n",
       "0  122.036       194754  \n",
       "1   99.972       162600  \n",
       "2  124.008       176616  \n",
       "3  121.956       169093  \n",
       "4  123.976       189052  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, RandomizedSearchCV\n",
    "from sklearn.preprocessing import RobustScaler  \n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "\n",
    "df = pd.read_csv('data/spotify_songs.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **2. Exploratory Data Analysis**\n",
    "\n",
    "[Back to top](#Table-of-Contents)\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32833, 23)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the dataset has 32833 rows and 23 columns. The columns are as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['track_id', 'track_name', 'track_artist', 'track_popularity',\n",
       "       'track_album_id', 'track_album_name', 'track_album_release_date',\n",
       "       'playlist_name', 'playlist_id', 'playlist_genre', 'playlist_subgenre',\n",
       "       'danceability', 'energy', 'key', 'loudness', 'mode', 'speechiness',\n",
       "       'acousticness', 'instrumentalness', 'liveness', 'valence', 'tempo',\n",
       "       'duration_ms'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 32833 entries, 0 to 32832\n",
      "Data columns (total 23 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   track_id                  32833 non-null  object \n",
      " 1   track_name                32828 non-null  object \n",
      " 2   track_artist              32828 non-null  object \n",
      " 3   track_popularity          32833 non-null  int64  \n",
      " 4   track_album_id            32833 non-null  object \n",
      " 5   track_album_name          32828 non-null  object \n",
      " 6   track_album_release_date  32833 non-null  object \n",
      " 7   playlist_name             32833 non-null  object \n",
      " 8   playlist_id               32833 non-null  object \n",
      " 9   playlist_genre            32833 non-null  object \n",
      " 10  playlist_subgenre         32833 non-null  object \n",
      " 11  danceability              32833 non-null  float64\n",
      " 12  energy                    32833 non-null  float64\n",
      " 13  key                       32833 non-null  int64  \n",
      " 14  loudness                  32833 non-null  float64\n",
      " 15  mode                      32833 non-null  int64  \n",
      " 16  speechiness               32833 non-null  float64\n",
      " 17  acousticness              32833 non-null  float64\n",
      " 18  instrumentalness          32833 non-null  float64\n",
      " 19  liveness                  32833 non-null  float64\n",
      " 20  valence                   32833 non-null  float64\n",
      " 21  tempo                     32833 non-null  float64\n",
      " 22  duration_ms               32833 non-null  int64  \n",
      "dtypes: float64(9), int64(4), object(10)\n",
      "memory usage: 5.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "track_id                    0\n",
       "track_name                  5\n",
       "track_artist                5\n",
       "track_popularity            0\n",
       "track_album_id              0\n",
       "track_album_name            5\n",
       "track_album_release_date    0\n",
       "playlist_name               0\n",
       "playlist_id                 0\n",
       "playlist_genre              0\n",
       "playlist_subgenre           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",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are barely any missing values in the dataset. The only columns with missing values are track_name and track_artist. Since there are only 15 total missing values, I will drop these rows from the dataset later.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>track_popularity</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
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       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
       "      <td>32833.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.477081</td>\n",
       "      <td>0.654850</td>\n",
       "      <td>0.698619</td>\n",
       "      <td>5.374471</td>\n",
       "      <td>-6.719499</td>\n",
       "      <td>0.565711</td>\n",
       "      <td>0.107068</td>\n",
       "      <td>0.175334</td>\n",
       "      <td>0.084747</td>\n",
       "      <td>0.190176</td>\n",
       "      <td>0.510561</td>\n",
       "      <td>120.881132</td>\n",
       "      <td>225799.811622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.984074</td>\n",
       "      <td>0.145085</td>\n",
       "      <td>0.180910</td>\n",
       "      <td>3.611657</td>\n",
       "      <td>2.988436</td>\n",
       "      <td>0.495671</td>\n",
       "      <td>0.101314</td>\n",
       "      <td>0.219633</td>\n",
       "      <td>0.224230</td>\n",
       "      <td>0.154317</td>\n",
       "      <td>0.233146</td>\n",
       "      <td>26.903624</td>\n",
       "      <td>59834.006182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-46.448000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.563000</td>\n",
       "      <td>0.581000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-8.171000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041000</td>\n",
       "      <td>0.015100</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.092700</td>\n",
       "      <td>0.331000</td>\n",
       "      <td>99.960000</td>\n",
       "      <td>187819.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>0.672000</td>\n",
       "      <td>0.721000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>-6.166000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.062500</td>\n",
       "      <td>0.080400</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.127000</td>\n",
       "      <td>0.512000</td>\n",
       "      <td>121.984000</td>\n",
       "      <td>216000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.761000</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>-4.645000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132000</td>\n",
       "      <td>0.255000</td>\n",
       "      <td>0.004830</td>\n",
       "      <td>0.248000</td>\n",
       "      <td>0.693000</td>\n",
       "      <td>133.918000</td>\n",
       "      <td>253585.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.983000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>1.275000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.918000</td>\n",
       "      <td>0.994000</td>\n",
       "      <td>0.994000</td>\n",
       "      <td>0.996000</td>\n",
       "      <td>0.991000</td>\n",
       "      <td>239.440000</td>\n",
       "      <td>517810.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       track_popularity  danceability        energy           key  \\\n",
       "count      32833.000000  32833.000000  32833.000000  32833.000000   \n",
       "mean          42.477081      0.654850      0.698619      5.374471   \n",
       "std           24.984074      0.145085      0.180910      3.611657   \n",
       "min            0.000000      0.000000      0.000175      0.000000   \n",
       "25%           24.000000      0.563000      0.581000      2.000000   \n",
       "50%           45.000000      0.672000      0.721000      6.000000   \n",
       "75%           62.000000      0.761000      0.840000      9.000000   \n",
       "max          100.000000      0.983000      1.000000     11.000000   \n",
       "\n",
       "           loudness          mode   speechiness  acousticness  \\\n",
       "count  32833.000000  32833.000000  32833.000000  32833.000000   \n",
       "mean      -6.719499      0.565711      0.107068      0.175334   \n",
       "std        2.988436      0.495671      0.101314      0.219633   \n",
       "min      -46.448000      0.000000      0.000000      0.000000   \n",
       "25%       -8.171000      0.000000      0.041000      0.015100   \n",
       "50%       -6.166000      1.000000      0.062500      0.080400   \n",
       "75%       -4.645000      1.000000      0.132000      0.255000   \n",
       "max        1.275000      1.000000      0.918000      0.994000   \n",
       "\n",
       "       instrumentalness      liveness       valence         tempo  \\\n",
       "count      32833.000000  32833.000000  32833.000000  32833.000000   \n",
       "mean           0.084747      0.190176      0.510561    120.881132   \n",
       "std            0.224230      0.154317      0.233146     26.903624   \n",
       "min            0.000000      0.000000      0.000000      0.000000   \n",
       "25%            0.000000      0.092700      0.331000     99.960000   \n",
       "50%            0.000016      0.127000      0.512000    121.984000   \n",
       "75%            0.004830      0.248000      0.693000    133.918000   \n",
       "max            0.994000      0.996000      0.991000    239.440000   \n",
       "\n",
       "         duration_ms  \n",
       "count   32833.000000  \n",
       "mean   225799.811622  \n",
       "std     59834.006182  \n",
       "min      4000.000000  \n",
       "25%    187819.000000  \n",
       "50%    216000.000000  \n",
       "75%    253585.000000  \n",
       "max    517810.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the columns have different scales. Meaning we'll have to scale the data before training the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['playlist_genre', 'playlist_subgenre']] = df[['playlist_genre', 'playlist_subgenre']] \\\n",
    "                                                  .apply(lambda x: x.str.capitalize(), axis=1)\n",
    "fig = px.sunburst(df,\n",
    "                  path=['playlist_genre', 'playlist_subgenre'], \n",
    "                  color='track_popularity', \n",
    "                  labels={'track_popularity': 'Popularity'})\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When looking at the target variable, we can see there are 6 unique genres. These genres have sub-genres as well.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **3. Data Preprocessing**\n",
    "\n",
    "[Back to top](#Table-of-Contents)\n",
    "\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **3.1 Dropping Columns & Missing Values**\n",
    "\n",
    "I will start by dropping the columns that are not needed. After that, I will drop the rows with missing values.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>track_popularity</th>\n",
       "      <th>playlist_genre</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>66</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.916</td>\n",
       "      <td>6</td>\n",
       "      <td>-2.634</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0583</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0653</td>\n",
       "      <td>0.518</td>\n",
       "      <td>122.036</td>\n",
       "      <td>194754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>67</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.726</td>\n",
       "      <td>0.815</td>\n",
       "      <td>11</td>\n",
       "      <td>-4.969</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0373</td>\n",
       "      <td>0.0724</td>\n",
       "      <td>0.004210</td>\n",
       "      <td>0.3570</td>\n",
       "      <td>0.693</td>\n",
       "      <td>99.972</td>\n",
       "      <td>162600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>70</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.675</td>\n",
       "      <td>0.931</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.432</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0742</td>\n",
       "      <td>0.0794</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>0.613</td>\n",
       "      <td>124.008</td>\n",
       "      <td>176616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.718</td>\n",
       "      <td>0.930</td>\n",
       "      <td>7</td>\n",
       "      <td>-3.778</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1020</td>\n",
       "      <td>0.0287</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.2040</td>\n",
       "      <td>0.277</td>\n",
       "      <td>121.956</td>\n",
       "      <td>169093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.833</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.672</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0359</td>\n",
       "      <td>0.0803</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0833</td>\n",
       "      <td>0.725</td>\n",
       "      <td>123.976</td>\n",
       "      <td>189052</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   track_popularity playlist_genre  danceability  energy  key  loudness  mode  \\\n",
       "0                66            Pop         0.748   0.916    6    -2.634     1   \n",
       "1                67            Pop         0.726   0.815   11    -4.969     1   \n",
       "2                70            Pop         0.675   0.931    1    -3.432     0   \n",
       "3                60            Pop         0.718   0.930    7    -3.778     1   \n",
       "4                69            Pop         0.650   0.833    1    -4.672     1   \n",
       "\n",
       "   speechiness  acousticness  instrumentalness  liveness  valence    tempo  \\\n",
       "0       0.0583        0.1020          0.000000    0.0653    0.518  122.036   \n",
       "1       0.0373        0.0724          0.004210    0.3570    0.693   99.972   \n",
       "2       0.0742        0.0794          0.000023    0.1100    0.613  124.008   \n",
       "3       0.1020        0.0287          0.000009    0.2040    0.277  121.956   \n",
       "4       0.0359        0.0803          0.000000    0.0833    0.725  123.976   \n",
       "\n",
       "   duration_ms  \n",
       "0       194754  \n",
       "1       162600  \n",
       "2       176616  \n",
       "3       169093  \n",
       "4       189052  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['track_id', 'track_album_id', 'track_album_release_date', 'playlist_id', 'track_name', 'track_artist', 'track_album_name', 'playlist_name', 'playlist_subgenre'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have chosen to drop the playlist_subgenre column. Because I think it will be too difficult to predict the sub-genre of a song. I will only focus on predicting the genre of a song.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "track_popularity    0\n",
       "playlist_genre      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",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **3.2 Distribution and Outliers**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Categorical columns: Index(['playlist_genre'], dtype='object')\n",
      "Numerical columns: Index(['track_popularity', 'danceability', 'energy', 'key', 'loudness', 'mode',\n",
      "       'speechiness', 'acousticness', 'instrumentalness', 'liveness',\n",
      "       'valence', 'tempo', 'duration_ms'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "categorical = df.select_dtypes(include='object').columns\n",
    "numerical = df.select_dtypes(exclude='object').columns\n",
    "print(f'Categorical columns: {categorical}')\n",
    "print(f'Numerical columns: {numerical}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm seperating the numerical and categorical columns for convenience.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x2500 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_cols = len(numerical)\n",
    "num_rows = num_cols // 3 + (num_cols % 3 > 0)\n",
    "\n",
    "fig, axs = plt.subplots(num_rows, 3, figsize=(20, num_rows*5))\n",
    "\n",
    "for i, col in enumerate(numerical): # enumerate() returns the index and the value of the column\n",
    "    row_idx = i // 3\n",
    "    col_idx = i % 3\n",
    "    axs[row_idx, col_idx].hist(df[col], bins=15)\n",
    "    axs[row_idx, col_idx].set_title(f'Distribution of {col}')\n",
    "    axs[row_idx, col_idx].set_xlabel(col)\n",
    "    axs[row_idx, col_idx].set_ylabel('Frequency')\n",
    "    axs[row_idx, col_idx].grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the distribution of the numerical columns we can see that there are some extremely skewed columns. I've previously tried to remove the outliers from these columns, but this didn't improve the model (Removes 11k rows). Therefore, I will leave the columns as they are.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **3.3 Scaling Numerical Columns**\n",
    "\n",
    "Now that the categorical columns are encoded, I will scale the numerical columns using the RobustScaler. As I have not removed the outliers, using the RobustScaler is the better option.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>track_popularity</th>\n",
       "      <th>playlist_genre</th>\n",
       "      <th>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>key</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.552632</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.752896</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.001702</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.046154</td>\n",
       "      <td>0.090038</td>\n",
       "      <td>-0.003333</td>\n",
       "      <td>-0.397296</td>\n",
       "      <td>0.016575</td>\n",
       "      <td>0.001531</td>\n",
       "      <td>-0.323054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.578947</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.362934</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.339478</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.276923</td>\n",
       "      <td>-0.033347</td>\n",
       "      <td>0.868302</td>\n",
       "      <td>1.481005</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.648212</td>\n",
       "      <td>-0.811970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.657895</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.015152</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>-0.714286</td>\n",
       "      <td>0.775383</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.128571</td>\n",
       "      <td>-0.004168</td>\n",
       "      <td>0.001491</td>\n",
       "      <td>-0.109466</td>\n",
       "      <td>0.279006</td>\n",
       "      <td>0.059603</td>\n",
       "      <td>-0.598850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.394737</td>\n",
       "      <td>Pop</td>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.806950</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.677255</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.434066</td>\n",
       "      <td>-0.215506</td>\n",
       "      <td>-0.001381</td>\n",
       "      <td>0.495815</td>\n",
       "      <td>-0.649171</td>\n",
       "      <td>-0.000825</td>\n",
       "      <td>-0.713241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.631579</td>\n",
       "      <td>Pop</td>\n",
       "      <td>-0.111111</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>-0.714286</td>\n",
       "      <td>0.423710</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.292308</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>-0.003333</td>\n",
       "      <td>-0.281391</td>\n",
       "      <td>0.588398</td>\n",
       "      <td>0.058661</td>\n",
       "      <td>-0.409756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   track_popularity playlist_genre  danceability    energy       key  \\\n",
       "0          0.552632            Pop      0.383838  0.752896  0.000000   \n",
       "1          0.578947            Pop      0.272727  0.362934  0.714286   \n",
       "2          0.657895            Pop      0.015152  0.810811 -0.714286   \n",
       "3          0.394737            Pop      0.232323  0.806950  0.142857   \n",
       "4          0.631579            Pop     -0.111111  0.432432 -0.714286   \n",
       "\n",
       "   loudness  mode  speechiness  acousticness  instrumentalness  liveness  \\\n",
       "0  1.001702   0.0    -0.046154      0.090038         -0.003333 -0.397296   \n",
       "1  0.339478   0.0    -0.276923     -0.033347          0.868302  1.481005   \n",
       "2  0.775383  -1.0     0.128571     -0.004168          0.001491 -0.109466   \n",
       "3  0.677255   0.0     0.434066     -0.215506         -0.001381  0.495815   \n",
       "4  0.423710   0.0    -0.292308     -0.000417         -0.003333 -0.281391   \n",
       "\n",
       "    valence     tempo  duration_ms  \n",
       "0  0.016575  0.001531    -0.323054  \n",
       "1  0.500000 -0.648212    -0.811970  \n",
       "2  0.279006  0.059603    -0.598850  \n",
       "3 -0.649171 -0.000825    -0.713241  \n",
       "4  0.588398  0.058661    -0.409756  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = RobustScaler()\n",
    "df[numerical] = scaler.fit_transform(df[numerical])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **3.4 Seperating Features and Target Variable + One-Hot Encoding**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['playlist_genre'], axis=1)\n",
    "y = df['playlist_genre']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the target column is seperated from the features, I will one-hot encode the target column.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le = LabelEncoder()\n",
    "y = le.fit_transform(y)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **4. Building the Model**\n",
    "\n",
    "[Back to top](#Table-of-Contents)\n",
    "\n",
    "---\n",
    "\n",
    "It's time to build the model. I will start by splitting the data into a training and test set. After that, I will build three different models and compare their performance. The models that I will use are:\n",
    "\n",
    "1. Decision Tree\n",
    "2. Random Forest\n",
    "3. Naive Bayes\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **4.1 Trying Different Models**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree accuracy: 0.4391655245926603\n",
      "Decision Tree cross-validation scores: [0.37642759 0.43642455 0.42211055 0.42354554 0.40191898]\n",
      "Decision Tree cross-validation mean score: 0.4120854412825998\n",
      "\n",
      "Random Forest accuracy: 0.5583980508603624\n",
      "Random Forest cross-validation scores: [0.46505254 0.5360134  0.53890665 0.54279622 0.51294548]\n",
      "Random Forest cross-validation mean score: 0.5191428579777575\n",
      "\n",
      "Gaussian Naive Bayes accuracy: 0.4476930105070809\n",
      "Gaussian Naive Bayes cross-validation scores: [0.38465053 0.42591747 0.42013096 0.41486445 0.43192202]\n",
      "Gaussian Naive Bayes cross-validation mean score: 0.4154970850152516\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "models = {\n",
    "    'Decision Tree': DecisionTreeClassifier(),\n",
    "    'Random Forest': RandomForestClassifier(),\n",
    "    'Gaussian Naive Bayes': GaussianNB(),\n",
    "}\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    scores = cross_val_score(model, X, y, cv=5)\n",
    " \n",
    "    print(f'{name} accuracy: {accuracy}')\n",
    "    print(f'{name} cross-validation scores: {scores}')\n",
    "    print(f'{name} cross-validation mean score: {scores.mean()}\\n')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While Random Forest has the highest accuracy, the scores are not that high. I will try to improve the model by tuning the hyperparameters of the Random Forest model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **4.2 Hyperparameter Tuning**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 150,\n",
       " 'min_samples_split': 5,\n",
       " 'min_samples_leaf': 4,\n",
       " 'max_depth': 20}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_distributions = {\n",
    "    'n_estimators': [50, 100, 150],\n",
    "    'max_depth': [10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "random_search = RandomizedSearchCV(estimator=rf, param_distributions=param_distributions, \n",
    "                                   n_iter=10, cv=5, n_jobs=-1, verbose=2)\n",
    "random_search.fit(X_train, y_train)\n",
    "\n",
    "random_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I have used RandomizedSearchCV to tune the hyperparameters of the Random Forest model. RandomizedSearchCV is a method that searches for the best hyperparameters by sampling from a distribution of hyperparameters. GridSearchCV could also be used, it however takes too long to run.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest accuracy: 0.5643368356936196\n",
      "Random Forest cross-validation scores: [0.46931628 0.53388153 0.5529161  0.54995431 0.52177886]\n",
      "Random Forest cross-validation mean score: 0.5248078650945865\n"
     ]
    }
   ],
   "source": [
    "rf = RandomForestClassifier(**random_search.best_params_)\n",
    "rf.fit(X_train, y_train)\n",
    "y_pred = rf.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "\n",
    "print(f'Random Forest accuracy: {accuracy}')\n",
    "print(f'Random Forest cross-validation scores: {cross_val_score(rf, X, y, cv=5)}')\n",
    "print(f'Random Forest cross-validation mean score: {cross_val_score(rf, X, y, cv=5).mean()}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy of the Random Forest model has improved from 51.9% to 52.6% after tuning the hyperparameters. This is still not a great score, but it's slightly better than before.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **4.3 Recursive Feature Elimination**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if I can improve the model by selecting the best features. I will use Recursive Feature Elimination for this.\n",
    "\n",
    "> Recursive Feature Elimination (RFE) is a feature selection method that fits a model and removes the weakest feature (or features) until the specified number of features is reached.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>danceability</th>\n",
       "      <th>energy</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>valence</th>\n",
       "      <th>tempo</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>0.752896</td>\n",
       "      <td>-0.046154</td>\n",
       "      <td>0.090038</td>\n",
       "      <td>0.016575</td>\n",
       "      <td>0.001531</td>\n",
       "      <td>-0.323054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.362934</td>\n",
       "      <td>-0.276923</td>\n",
       "      <td>-0.033347</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.648212</td>\n",
       "      <td>-0.811970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.015152</td>\n",
       "      <td>0.810811</td>\n",
       "      <td>0.128571</td>\n",
       "      <td>-0.004168</td>\n",
       "      <td>0.279006</td>\n",
       "      <td>0.059603</td>\n",
       "      <td>-0.598850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.232323</td>\n",
       "      <td>0.806950</td>\n",
       "      <td>0.434066</td>\n",
       "      <td>-0.215506</td>\n",
       "      <td>-0.649171</td>\n",
       "      <td>-0.000825</td>\n",
       "      <td>-0.713241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.111111</td>\n",
       "      <td>0.432432</td>\n",
       "      <td>-0.292308</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>0.588398</td>\n",
       "      <td>0.058661</td>\n",
       "      <td>-0.409756</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   danceability    energy  speechiness  acousticness   valence     tempo  \\\n",
       "0      0.383838  0.752896    -0.046154      0.090038  0.016575  0.001531   \n",
       "1      0.272727  0.362934    -0.276923     -0.033347  0.500000 -0.648212   \n",
       "2      0.015152  0.810811     0.128571     -0.004168  0.279006  0.059603   \n",
       "3      0.232323  0.806950     0.434066     -0.215506 -0.649171 -0.000825   \n",
       "4     -0.111111  0.432432    -0.292308     -0.000417  0.588398  0.058661   \n",
       "\n",
       "   duration_ms  \n",
       "0    -0.323054  \n",
       "1    -0.811970  \n",
       "2    -0.598850  \n",
       "3    -0.713241  \n",
       "4    -0.409756  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfe = RFE(RandomForestClassifier(n_estimators=100, random_state=42), n_features_to_select=7)\n",
    "rfe.fit(X, y)\n",
    "\n",
    "X.columns[rfe.support_]\n",
    "\n",
    "X = X[X.columns[rfe.support_]]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree accuracy: 0.42835389066544843\n",
      "Decision Tree cross-validation scores: [0.37795036 0.42256738 0.41830364 0.43070362 0.39780688]\n",
      "Decision Tree cross-validation mean score: 0.40946637766129684\n",
      "\n",
      "Random Forest accuracy: 0.5358611238008223\n",
      "Random Forest cross-validation scores: [0.45957058 0.51667428 0.52474494 0.53837953 0.50609199]\n",
      "Random Forest cross-validation mean score: 0.5090922634846946\n",
      "\n",
      "Gaussian Naive Bayes accuracy: 0.42348104157149385\n",
      "Gaussian Naive Bayes cross-validation scores: [0.37399117 0.41967413 0.42104462 0.42750533 0.41593055]\n",
      "Gaussian Naive Bayes cross-validation mean score: 0.4116291590035576\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "models = {\n",
    "    'Decision Tree': DecisionTreeClassifier(),\n",
    "    'Random Forest': RandomForestClassifier(),\n",
    "    'Gaussian Naive Bayes': GaussianNB(),\n",
    "}\n",
    "\n",
    "for name, model in models.items():\n",
    "    model.fit(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    scores = cross_val_score(model, X, y, cv=5)\n",
    " \n",
    "    print(f'{name} accuracy: {accuracy}')\n",
    "    print(f'{name} cross-validation scores: {scores}')\n",
    "    print(f'{name} cross-validation mean score: {scores.mean()}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sadly, the scores have not improved after selecting the best features. I've tried a a wide range of features, none of them improved the model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## **5. Conclusion**\n",
    "\n",
    "[Back to top](#Table-of-Contents)\n",
    "\n",
    "---\n",
    "\n",
    "In this notebook, I tackled a multi-class classification problem aimed at predicting the genre of songs based on various features. The dataset comprised approximately 30,000 songs from Spotify, with 23 columns including the target variable, playlist_genre.\n",
    "\n",
    "After an initial exploratory data analysis (EDA), I performed data preprocessing steps, including dropping irrelevant columns, handling missing values, and scaling numerical features using RobustScaler. I also encoded categorical variables and prepared the data for modeling.\n",
    "\n",
    "I used three different classification models: Decision Tree, Random Forest, and Gaussian Naive Bayes. Despite tuning the hyperparameters of the Random Forest model using RandomizedSearchCV, the model's accuracy remained mediocre. I also tried to use Recursive Feature Elimination (RFE) to select the most informative features. However, even after selecting the best features, the model's accuracy actually decreased.\n",
    "\n",
    "In conclusion, the best model I've built is the Random Forest model with an accuracy of 52.6%. This is not a great score. My guess is that the dataset is too complex to predict the genre of a song based on the given features. It could also be that the features are not informative enough to predict the genre of a song.\n"
   ]
  }
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