capstone_data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "22ecb677-b0f6-48dd-9d04-2d248a11c1d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished successfully loading packages\n"
     ]
    }
   ],
   "source": [
    "# Numerical/scientific computing packages.\n",
    "import numpy as np\n",
    "import scipy\n",
    "import copy\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import csv\n",
    "\n",
    "# Machine learning package.\n",
    "import sklearn\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_squared_log_error, mean_absolute_error\n",
    "\n",
    "# Useful for saving our models.\n",
    "import pickle\n",
    "\n",
    "# Plotting packages.\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "print('Finished successfully loading packages')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e89c49a2-5ce8-43b8-bce9-019664ceaefd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing my N-number\n",
    "import random\n",
    "random.seed(18618637)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d4778ca-95a2-4265-a8b5-690742ac9283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instance_id         5\n",
       "artist_name         5\n",
       "track_name          5\n",
       "popularity          5\n",
       "acousticness        5\n",
       "danceability        5\n",
       "duration_ms         5\n",
       "energy              5\n",
       "instrumentalness    5\n",
       "key                 5\n",
       "liveness            5\n",
       "loudness            5\n",
       "mode                5\n",
       "speechiness         5\n",
       "tempo               5\n",
       "obtained_date       5\n",
       "valence             5\n",
       "music_genre         5\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"dataSet/musicData.csv\")\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a76213cc-130a-4c49-b7cd-1937245103ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "music_genre\n",
       "Electronic     5000\n",
       "Anime          5000\n",
       "Jazz           5000\n",
       "Alternative    5000\n",
       "Country        5000\n",
       "Rap            5000\n",
       "Blues          5000\n",
       "Rock           5000\n",
       "Classical      5000\n",
       "Hip-Hop        5000\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=0, how='any', inplace=True)\n",
    "df.music_genre.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92560a19-2ad5-40e1-bc26-893c6811dd16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique key values:  {'F#', 'A#', 'C', 'F', 'A', 'G#', 'C#', 'B', 'E', 'G', 'D', 'D#'}\n",
      "Unique mode values:  {'Minor', 'Major'}\n",
      "Unique music_genre values:  {'Classical', 'Alternative', 'Jazz', 'Electronic', 'Blues', 'Country', 'Hip-Hop', 'Rock', 'Anime', 'Rap'}\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique key values: \", set(df[\"key\"]))\n",
    "print(\"Unique mode values: \", set(df[\"mode\"]))\n",
    "print(\"Unique music_genre values: \", set(df[\"music_genre\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7fc6f51b-501e-41b2-bb1d-3b70a86195dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7473, 18)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicates = df[df.duplicated(subset=['artist_name', 'track_name'], keep=False)]\n",
    "duplicates = duplicates.sort_values(by=\"track_name\")\n",
    "duplicates.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9e8703db-0425-4ae9-9130-fc0857dbedb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 18)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "55a057c8-d04d-43bf-93f9-43ad7fcbe9eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(46165, 18)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop duplicates from df to keep only one instance of each unique set of features\n",
    "df.drop_duplicates(subset=['artist_name', 'track_name'], inplace=True)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7d87f3fe-eb54-48ce-bee8-63f1a459ed65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Number of values equal to -1 in 'duration_ms' column: 4548\""
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def count_values_per_col(df: pd.DataFrame, col_name: str, value_to_count: any):\n",
    "    count_vals = (df[col_name] == value_to_count).sum()\n",
    "    return \"Number of values equal to {} in '{}' column: {}\".format(value_to_count, col_name, count_vals)\n",
    "\n",
    "count_values_per_col(df, \"duration_ms\", -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e9bf17b0-3fde-4ca1-902a-80a61232ad88",
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_duration = df.loc[df['duration_ms'] != -1, 'duration_ms'].mean()\n",
    "# replace -1 values with the mean\n",
    "df.loc[df['duration_ms'] == -1, 'duration_ms'] = mean_duration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e859c18e-b887-46b4-81a8-4f516684e071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Number of values equal to -1 in 'duration_ms' column: 0\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_values_per_col(df, \"duration_ms\", -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "63f19d0e-4958-49d0-ba8a-e01bf3ea279d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Number of values equal to ? in 'tempo' column: 4633\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_values_per_col(df, \"tempo\", \"?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a7a82ca8-aec6-43a1-99e4-9998947084fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['tempo'] = df['tempo'].replace(\"?\", float(\"NaN\"))\n",
    "df['tempo'] = df['tempo'].astype(float)\n",
    "mean_tempo = df.loc[~df['tempo'].isna(), 'tempo'].mean()\n",
    "df['tempo'] = df['tempo'].replace(float(\"NaN\"), mean_tempo)\n",
    "############################################################\n",
    "df['popularity'] = df['popularity'] / 100\n",
    "mean_popularity = df.loc[~df['popularity'].isna(), 'popularity'].mean()\n",
    "df['popularity'] = df['popularity'].replace(float(\"NaN\"), mean_popularity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3307726d-8be3-4370-9a49-b78c23de2bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Number of values equal to ? in 'tempo' column: 0\""
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_values_per_col(df, \"tempo\", \"?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ae6ea03b-da7b-422b-89ee-12590ca77bb6",
   "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>instance_id</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>key</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>mode</th>\n",
       "      <th>speechiness</th>\n",
       "      <th>tempo</th>\n",
       "      <th>obtained_date</th>\n",
       "      <th>valence</th>\n",
       "      <th>music_genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32894.0</td>\n",
       "      <td>Röyksopp</td>\n",
       "      <td>Röyksopp's Night Out</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.00468</td>\n",
       "      <td>0.652</td>\n",
       "      <td>246660.675637</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.79200</td>\n",
       "      <td>A#</td>\n",
       "      <td>0.115</td>\n",
       "      <td>-5.201</td>\n",
       "      <td>Minor</td>\n",
       "      <td>0.0748</td>\n",
       "      <td>100.889</td>\n",
       "      <td>4-Apr</td>\n",
       "      <td>0.759</td>\n",
       "      <td>Electronic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46652.0</td>\n",
       "      <td>Thievery Corporation</td>\n",
       "      <td>The Shining Path</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.01270</td>\n",
       "      <td>0.622</td>\n",
       "      <td>218293.000000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.95000</td>\n",
       "      <td>D</td>\n",
       "      <td>0.124</td>\n",
       "      <td>-7.043</td>\n",
       "      <td>Minor</td>\n",
       "      <td>0.0300</td>\n",
       "      <td>115.002</td>\n",
       "      <td>4-Apr</td>\n",
       "      <td>0.531</td>\n",
       "      <td>Electronic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30097.0</td>\n",
       "      <td>Dillon Francis</td>\n",
       "      <td>Hurricane</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00306</td>\n",
       "      <td>0.620</td>\n",
       "      <td>215613.000000</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.01180</td>\n",
       "      <td>G#</td>\n",
       "      <td>0.534</td>\n",
       "      <td>-4.617</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.0345</td>\n",
       "      <td>127.994</td>\n",
       "      <td>4-Apr</td>\n",
       "      <td>0.333</td>\n",
       "      <td>Electronic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62177.0</td>\n",
       "      <td>Dubloadz</td>\n",
       "      <td>Nitro</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.02540</td>\n",
       "      <td>0.774</td>\n",
       "      <td>166875.000000</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00253</td>\n",
       "      <td>C#</td>\n",
       "      <td>0.157</td>\n",
       "      <td>-4.498</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.2390</td>\n",
       "      <td>128.014</td>\n",
       "      <td>4-Apr</td>\n",
       "      <td>0.270</td>\n",
       "      <td>Electronic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24907.0</td>\n",
       "      <td>What So Not</td>\n",
       "      <td>Divide &amp; Conquer</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.638</td>\n",
       "      <td>222369.000000</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.90900</td>\n",
       "      <td>F#</td>\n",
       "      <td>0.157</td>\n",
       "      <td>-6.266</td>\n",
       "      <td>Major</td>\n",
       "      <td>0.0413</td>\n",
       "      <td>145.036</td>\n",
       "      <td>4-Apr</td>\n",
       "      <td>0.323</td>\n",
       "      <td>Electronic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instance_id           artist_name            track_name  popularity  \\\n",
       "0      32894.0              Röyksopp  Röyksopp's Night Out        0.27   \n",
       "1      46652.0  Thievery Corporation      The Shining Path        0.31   \n",
       "2      30097.0        Dillon Francis             Hurricane        0.28   \n",
       "3      62177.0              Dubloadz                 Nitro        0.34   \n",
       "4      24907.0           What So Not      Divide & Conquer        0.32   \n",
       "\n",
       "   acousticness  danceability    duration_ms  energy  instrumentalness key  \\\n",
       "0       0.00468         0.652  246660.675637   0.941           0.79200  A#   \n",
       "1       0.01270         0.622  218293.000000   0.890           0.95000   D   \n",
       "2       0.00306         0.620  215613.000000   0.755           0.01180  G#   \n",
       "3       0.02540         0.774  166875.000000   0.700           0.00253  C#   \n",
       "4       0.00465         0.638  222369.000000   0.587           0.90900  F#   \n",
       "\n",
       "   liveness  loudness   mode  speechiness    tempo obtained_date  valence  \\\n",
       "0     0.115    -5.201  Minor       0.0748  100.889         4-Apr    0.759   \n",
       "1     0.124    -7.043  Minor       0.0300  115.002         4-Apr    0.531   \n",
       "2     0.534    -4.617  Major       0.0345  127.994         4-Apr    0.333   \n",
       "3     0.157    -4.498  Major       0.2390  128.014         4-Apr    0.270   \n",
       "4     0.157    -6.266  Major       0.0413  145.036         4-Apr    0.323   \n",
       "\n",
       "  music_genre  \n",
       "0  Electronic  \n",
       "1  Electronic  \n",
       "2  Electronic  \n",
       "3  Electronic  \n",
       "4  Electronic  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "66480cdc-8625-43cf-860f-40696b9ef899",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OHQ0ZMkQDBgxQtWrViiNWAABwhxjH7z2xsbFKTEzMtT6/GbUA/k9SSooyMzP12evTFegfkGObqNMx6v/WZCUmJjIbHQAA4C5R6CS6+UAbG/Xo0UOdO3fWokWLNHHiRL3yyiv6+9//rl69eumdd96Rt7d3UcYKAACKCON42VDcS0LExsYqMDBQqampt70PANkF+geoWZ16pR0GAAAASshtJ9H379+vZcuWac2aNapUqZJeeeUVDR06VH/88YemTZumrl27Furr4bNmzdLEiRM1evRozZ8/X5J09epVvfzyy1qzZo3S0tIUGhqqRYsWydPT03xcbGysRowYoe3bt6ty5coaNGiQZs6cKRub/+vajh07NG7cOB09elS+vr564403NHjw4NvtOgAA5V5Rj+MonJuXhMiLo6OjoqKick2kF2SWeWpqap6zZjfs2aVJy5YUPHgAAAAAuMcUOok+d+5cLV++XNHR0erUqZM+/fRTderUSdbWN5ZXDwgI0IoVK1SjRo0C73Pfvn368MMP1ahRI4vysWPHav369frqq6/k4uKikSNHqkePHtq1a5ck6fr16+rcubO8vLy0e/duxcXFaeDAgapYsaLefvttSVJMTIw6d+6sF154QatWrdK2bdv03HPPydvbW6GhoYXtPgAA5VpxjOMovKJYEqIws8zzmjUbdfpUoWIHAAAAgHtNoZPoixcv1pAhQzR48OBcv+bt4eGhTz75pED7S0lJUb9+/bR06VK9+eabZvmlS5f0ySefaPXq1Xr88cclScuXL1dgYKD27Nmjli1bavPmzTp27Ji2bt0qT09PNWnSRDNmzNCECRM0depU2draasmSJQoICNCcOXMkSYGBgfr55581b948kugAgHtOUY/juDN3siREYmIis8wBAAAAoAQUOol+/PjxfNvY2tpq0KBBBdpfWFiYOnfurJCQEIskekREhDIyMhQSEmKW1atXT35+fgoPD1fLli0VHh6uhg0bWizvEhoaqhEjRujo0aNq2rSpwsPDLfaR1WbMmDG5xpSWlqa0tDRzOzk5uUB9AQCgrCvqcRylj1nmAAAAAFC8rAv7gOXLl+urr77KVv7VV19p5cqVhdrXmjVr9Ouvv2rmzJnZ6uLj42VraytXV1eLck9PT8XHx5ttbk6gZ9Vn1eXVJjk5WVeuXMkxrpkzZ8rFxcX88fX1LVS/AAAoq4pyHAcAAAAA4F5Q6CT6zJkz5e7unq3cw8PDXIe8IM6cOaPRo0dr1apVsre3L2wYxWrixIm6dOmS+XPmzJnSDgkAgCJRVOM4AAAAAAD3ikIn0WNjYxUQkH3dTX9/f8XGxhZ4PxERETp37pyaNWsmGxsb2djYaOfOnXr//fdlY2MjT09PpaenKykpyeJxCQkJ8vLykiR5eXkpISEhW31WXV5tnJ2d5eDgkGNsdnZ2cnZ2tvgBAOBuUFTjuCT9+OOP6tKli3x8fGRlZaW1a9da1BuGocmTJ8vb21sODg4KCQnJtpzMhQsX1K9fPzk7O8vV1VVDhw5VSkqKRZtDhw7p0Ucflb29vXx9fTV79uxCxQkAAAAAwJ0odBLdw8NDhw4dylZ+8OBBVa1atcD7adeunQ4fPqzIyEjz58EHH1S/fv3M3ytWrKht27aZj4mOjlZsbKyCg4MlScHBwTp8+LDOnTtnttmyZYucnZ1Vv359s83N+8hqk7UPAADuJUU1jkvS5cuX1bhxYy1cuDDH+tmzZ+v999/XkiVLtHfvXlWqVEmhoaG6evWq2aZfv346evSotmzZonXr1unHH3/U8OHDzfrk5GS1b99e/v7+ioiI0LvvvqupU6fqo48+KlSs5VlUVJR+/fXXbD9RUVGlHRoAAAAA3BMKfWPRPn36aNSoUXJyclLr1q0lSTt37tTo0aPVu3fvAu/HyclJDzzwgEVZpUqVVLVqVbN86NChGjdunNzc3OTs7KyXXnpJwcHBatmypSSpffv2ql+/vgYMGKDZs2crPj5eb7zxhsLCwmRnZydJeuGFF/SPf/xD48eP15AhQ/TDDz/oyy+/1Pr16wvbdQAAyr2iGsclqWPHjurYsWOOdYZhaP78+XrjjTfUtWtXSdKnn34qT09PrV27Vr1791ZUVJQ2bdqkffv26cEHH5QkffDBB+rUqZPee+89+fj4aNWqVUpPT9eyZctka2urBg0aKDIyUnPnzrVItt+N4s4nytraWv379y/tUADchvw+6HJ3d5efn18JRQMAAIA7Uegk+owZM3Tq1Cm1a9dONjY3Hp6ZmamBAwcW+Vqq8+bNk7W1tXr27Km0tDSFhoZq0aJFZn2FChW0bt06jRgxQsHBwapUqZIGDRqk6dOnm20CAgK0fv16jR07VgsWLFD16tX18ccfKzQ0tEhjBQCgPCipcTwmJkbx8fEKCQkxy1xcXBQUFKTw8HD17t1b4eHhcnV1NRPokhQSEiJra2vt3btX3bt3V3h4uFq3bi1bW1uzTWhoqN555x1dvHhRVapUyXbstLQ0paWlmdvJyclF1q+SlJSSoszMTH32+nQF+mdfgmfDnl2atGxJKUQGIC8F/QDM0dFRUVFRJNIBAADKgUIn0W1tbfXFF19oxowZOnjwoBwcHNSwYUP5+/vfcTA7duyw2La3t9fChQtz/Zq4dGMN1w0bNuS53zZt2ujAgQN3HB8AAOVdcY7jN4uPj5ckeXp6WpR7enqadfHx8fLw8LCot7GxkZubm0WbW9dwz9pnfHx8jkn0mTNnatq0aUXTkTIg0D9AzerUy1YedfpUyQcDIF/5fQAmSVGnY9T/rclKTEwkiQ4AAFAOFDqJnqVOnTqqU6dOUcYCAABKyN08jk+cOFHjxo0zt5OTk+Xr61uKEQG4F+X2ARgAAADKn0In0a9fv64VK1Zo27ZtOnfunDIzMy3qf/jhhyILDgAAFK2SGse9vLwkSQkJCfL29jbLExIS1KRJE7PNzTcHl6Rr167pwoUL5uO9vLyUkJBg0SZrO6vNrezs7Mx7owAAAAAAcKcKnUQfPXq0VqxYoc6dO+uBBx6QlZVVccQFAACKQUmN4wEBAfLy8tK2bdvMpHlycrL27t2rESNGSJKCg4OVlJSkiIgINW/eXNKNJH5mZqaCgoLMNq+//royMjJUsWJFSdKWLVtUt27dHJdyAYDyhJuPAgAAlA+FTqKvWbNGX375pTp16lQc8QAAgGJUlON4SkqKTpw4YW7HxMQoMjJSbm5u8vPz05gxY/Tmm2+qdu3aCggI0KRJk+Tj46Nu3bpJkgIDA9WhQwcNGzZMS5YsUUZGhkaOHKnevXvLx8dHktS3b19NmzZNQ4cO1YQJE3TkyBEtWLBA8+bNu+P4AaC0cPNRAACA8uW2bixaq1at4ogFAAAUs6Icx/fv36+2bdua21nrkA8aNEgrVqzQ+PHjdfnyZQ0fPlxJSUl65JFHtGnTJtnb25uPWbVqlUaOHKl27drJ2tpaPXv21Pvvv2/Wu7i4aPPmzQoLC1Pz5s3l7u6uyZMna/jw4UXSBwAoDdx8FAAAoHwpdBL95Zdf1oIFC/SPf/yDpVwAAChninIcb9OmjQzDyLXeyspK06dP1/Tp03Nt4+bmptWrV+d5nEaNGumnn3667TgBoKzi5qMAAADlQ6GT6D///LO2b9+ujRs3qkGDBub6pFm++eabIgsOAAAULcZxAAAAAAAKp9BJdFdXV3Xv3r04YgEAAMWMcRwAAAAAgMIpdBJ9+fLlxREHAAAoAYzjAAAAAAAUjvXtPOjatWvaunWrPvzwQ/3111+SpLNnzyolJaVIgwMAAEWPcRwAAAAAgIIr9Ez006dPq0OHDoqNjVVaWpqeeOIJOTk56Z133lFaWpqWLFlSHHECAIAiwDgOAAAAAEDhFHom+ujRo/Xggw/q4sWLcnBwMMu7d++ubdu2FWlwAACgaDGOAwAAAABQOIWeif7TTz9p9+7dsrW1tSivUaOG/vzzzyILDAAAFD3GcQAAAAAACqfQM9EzMzN1/fr1bOV//PGHnJyciiQoAABQPBjHAQAAAAAonEIn0du3b6/58+eb21ZWVkpJSdGUKVPUqVOnoowNAAAUMcZxAAAAAAAKp9DLucyZM0ehoaGqX7++rl69qr59++r48eNyd3fX559/XhwxAgCAIsI4DgB3l9jYWCUmJubZxt3dXX5+fiUUEQAAwN2n0En06tWr6+DBg1qzZo0OHTqklJQUDR06VP369bO4QRkAACh7GMcBoHyJiorKtS4uLk5PP/20rly5kuc+HB0dFRUVRSIdAADgNhU6iS5JNjY26t+/f1HHAgAASgDjOACUfXHnE2VtbV2g5+vPXp+uQP+AHOuiTseo/1uTlZiYSBIdAADgNhU6if7pp5/mWT9w4MDbDgYAABQvxnEAKB+SUlKUmZmZZ4J8w55dmrRsiQL9A9SsTr0SjhAAAODeUegk+ujRoy22MzIylJqaKltbWzk6OvLmGwCAMoxxHADKl7wS5FGnT5VsMAAAAPco68I+4OLFixY/KSkpio6O1iOPPMINyQAAKOMYxwEAAAAAKJxCJ9FzUrt2bc2aNSvb7DYAAFD2MY4DAAAAAJC7IkmiSzduUnb27Nmi2h0AAChBjOMAAAAAAOSs0Guif/fddxbbhmEoLi5O//jHP9SqVasiCwwAABQ9xnEAAAAAAAqn0En0bt26WWxbWVmpWrVqevzxxzVnzpyiigsAABQDxnEAAAAAAAqn0En0zMzM4ogDAACUAMZxAAAAAAAKp8jWRL8dixcvVqNGjeTs7CxnZ2cFBwdr48aNZv3Vq1cVFhamqlWrqnLlyurZs6cSEhIs9hEbG6vOnTvL0dFRHh4eevXVV3Xt2jWLNjt27FCzZs1kZ2enWrVqacWKFSXRPQAAAAAAAABAOVfomejjxo0rcNu5c+fmWV+9enXNmjVLtWvXlmEYWrlypbp27aoDBw6oQYMGGjt2rNavX6+vvvpKLi4uGjlypHr06KFdu3ZJkq5fv67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",
      "text/plain": [
       "<Figure size 1500x1500 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_freq_dist(predictor, dataframe, color: str = \"red\", ax=None):\n",
    "    \"\"\"\n",
    "    data: 1d dataframe to plot on the graph\n",
    "    \"\"\"\n",
    "    data = dataframe[predictor]\n",
    "    data_size = data.shape[0]\n",
    "    # Let's use the Scott's rule for choosing the right bin width\n",
    "    w = 3.49 * np.std(data)/data_size**(1./3)\n",
    "\n",
    "    # define number of bins with accordance to the appropriate size \n",
    "    bins = np.arange(data.min(), data.max() + w, w, dtype=float)\n",
    "\n",
    "    if ax is None:\n",
    "        ax = plt.gca()\n",
    "    ax.hist(data, color=color, edgecolor=\"black\", bins=bins) \n",
    "    ax.set_xlabel(\"{} distribution\".format(predictor))\n",
    "    ax.set_ylabel(\"frequency\")\n",
    "\n",
    "\n",
    "predictors = [\"popularity\", \"acousticness\", \"danceability\", \n",
    "              \"duration_ms\", \"energy\", \"instrumentalness\", \n",
    "              \"liveness\", \"loudness\", \"speechiness\", \n",
    "              \"tempo\", \"valence\"]\n",
    "\n",
    "colors = ['red', 'blue', 'green', 'orange', 'purple',\n",
    "          'yellow', 'cyan', 'magenta', 'gray', 'brown',\n",
    "          'pink', 'teal', 'lavender']\n",
    "\n",
    "num_rows = 5\n",
    "num_cols = 3\n",
    "fig, axes = plt.subplots(num_rows, num_cols, figsize=(15, 15))\n",
    "\n",
    "for i, predictor in enumerate(predictors):\n",
    "    row_index = i // num_cols\n",
    "    col_index = i % num_cols\n",
    "    plot_freq_dist(predictor, dataframe=df, color=colors[i], ax=axes[row_index, col_index])\n",
    "\n",
    "for i in range(len(predictors), num_rows*num_cols):\n",
    "    row_index = i // num_cols\n",
    "    col_index = i % num_cols\n",
    "    fig.delaxes(axes[row_index, col_index])\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3f6b7060-8de3-4a7e-a5cb-97c46248eb77",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "instance_id         float64\n",
       "artist_name          object\n",
       "track_name           object\n",
       "popularity          float64\n",
       "acousticness        float64\n",
       "danceability        float64\n",
       "duration_ms         float64\n",
       "energy              float64\n",
       "instrumentalness    float64\n",
       "key                  object\n",
       "liveness            float64\n",
       "loudness            float64\n",
       "mode                 object\n",
       "speechiness         float64\n",
       "tempo               float64\n",
       "obtained_date        object\n",
       "valence             float64\n",
       "music_genre          object\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b9f9f28d-4b3a-4a4a-b62d-2e203d6dd873",
   "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>instance_id</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>...</th>\n",
       "      <th>key_B</th>\n",
       "      <th>key_C</th>\n",
       "      <th>key_C#</th>\n",
       "      <th>key_D</th>\n",
       "      <th>key_D#</th>\n",
       "      <th>key_E</th>\n",
       "      <th>key_F</th>\n",
       "      <th>key_F#</th>\n",
       "      <th>key_G</th>\n",
       "      <th>key_G#</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32894.0</td>\n",
       "      <td>Röyksopp</td>\n",
       "      <td>Röyksopp's Night Out</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.00468</td>\n",
       "      <td>0.652</td>\n",
       "      <td>246660.675637</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.79200</td>\n",
       "      <td>0.115</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46652.0</td>\n",
       "      <td>Thievery Corporation</td>\n",
       "      <td>The Shining Path</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.01270</td>\n",
       "      <td>0.622</td>\n",
       "      <td>218293.000000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.95000</td>\n",
       "      <td>0.124</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30097.0</td>\n",
       "      <td>Dillon Francis</td>\n",
       "      <td>Hurricane</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00306</td>\n",
       "      <td>0.620</td>\n",
       "      <td>215613.000000</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.01180</td>\n",
       "      <td>0.534</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62177.0</td>\n",
       "      <td>Dubloadz</td>\n",
       "      <td>Nitro</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.02540</td>\n",
       "      <td>0.774</td>\n",
       "      <td>166875.000000</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00253</td>\n",
       "      <td>0.157</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24907.0</td>\n",
       "      <td>What So Not</td>\n",
       "      <td>Divide &amp; Conquer</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.638</td>\n",
       "      <td>222369.000000</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.90900</td>\n",
       "      <td>0.157</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instance_id           artist_name            track_name  popularity  \\\n",
       "0      32894.0              Röyksopp  Röyksopp's Night Out        0.27   \n",
       "1      46652.0  Thievery Corporation      The Shining Path        0.31   \n",
       "2      30097.0        Dillon Francis             Hurricane        0.28   \n",
       "3      62177.0              Dubloadz                 Nitro        0.34   \n",
       "4      24907.0           What So Not      Divide & Conquer        0.32   \n",
       "\n",
       "   acousticness  danceability    duration_ms  energy  instrumentalness  \\\n",
       "0       0.00468         0.652  246660.675637   0.941           0.79200   \n",
       "1       0.01270         0.622  218293.000000   0.890           0.95000   \n",
       "2       0.00306         0.620  215613.000000   0.755           0.01180   \n",
       "3       0.02540         0.774  166875.000000   0.700           0.00253   \n",
       "4       0.00465         0.638  222369.000000   0.587           0.90900   \n",
       "\n",
       "   liveness  ...  key_B  key_C  key_C#  key_D  key_D#  key_E  key_F  key_F#  \\\n",
       "0     0.115  ...  False  False   False  False   False  False  False   False   \n",
       "1     0.124  ...  False  False   False   True   False  False  False   False   \n",
       "2     0.534  ...  False  False   False  False   False  False  False   False   \n",
       "3     0.157  ...  False  False    True  False   False  False  False   False   \n",
       "4     0.157  ...  False  False   False  False   False  False  False    True   \n",
       "\n",
       "   key_G  key_G#  \n",
       "0  False   False  \n",
       "1  False   False  \n",
       "2  False    True  \n",
       "3  False   False  \n",
       "4  False   False  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['mode'] = df['mode'].astype('category')\n",
    "df['key'] = df['key'].astype('category')\n",
    "\n",
    "# Perform one-hot encoding\n",
    "df = pd.get_dummies(df, columns=['mode', 'key'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2b27fdef-c390-457d-83ff-79ceef554e34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['instance_id',\n",
       " 'artist_name',\n",
       " 'track_name',\n",
       " 'popularity',\n",
       " 'acousticness',\n",
       " 'danceability',\n",
       " 'duration_ms',\n",
       " 'energy',\n",
       " 'instrumentalness',\n",
       " 'liveness',\n",
       " 'loudness',\n",
       " 'speechiness',\n",
       " 'tempo',\n",
       " 'obtained_date',\n",
       " 'valence',\n",
       " 'music_genre',\n",
       " 'mode_Major',\n",
       " 'key_A',\n",
       " 'key_A#',\n",
       " 'key_B',\n",
       " 'key_C',\n",
       " 'key_C#',\n",
       " 'key_D',\n",
       " 'key_D#',\n",
       " 'key_E',\n",
       " 'key_F',\n",
       " 'key_F#',\n",
       " 'key_G']"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# avoiding dummy variable trap\n",
    "df = df.drop([\"mode_Minor\", \"key_G#\"], axis=1)\n",
    "df.columns.to_list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9ed7ed27-67d6-4fd9-82c1-0663a99046f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instance_id</th>\n",
       "      <th>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>...</th>\n",
       "      <th>key_B</th>\n",
       "      <th>key_C</th>\n",
       "      <th>key_C#</th>\n",
       "      <th>key_D</th>\n",
       "      <th>key_D#</th>\n",
       "      <th>key_E</th>\n",
       "      <th>key_F</th>\n",
       "      <th>key_F#</th>\n",
       "      <th>key_G</th>\n",
       "      <th>duration_sec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32894</td>\n",
       "      <td>Röyksopp</td>\n",
       "      <td>Röyksopp's Night Out</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.00468</td>\n",
       "      <td>0.652</td>\n",
       "      <td>246660.675637</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.79200</td>\n",
       "      <td>0.115</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>246.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>46652</td>\n",
       "      <td>Thievery Corporation</td>\n",
       "      <td>The Shining Path</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.01270</td>\n",
       "      <td>0.622</td>\n",
       "      <td>218293.000000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.95000</td>\n",
       "      <td>0.124</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>218.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30097</td>\n",
       "      <td>Dillon Francis</td>\n",
       "      <td>Hurricane</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00306</td>\n",
       "      <td>0.620</td>\n",
       "      <td>215613.000000</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.01180</td>\n",
       "      <td>0.534</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>62177</td>\n",
       "      <td>Dubloadz</td>\n",
       "      <td>Nitro</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.02540</td>\n",
       "      <td>0.774</td>\n",
       "      <td>166875.000000</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00253</td>\n",
       "      <td>0.157</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24907</td>\n",
       "      <td>What So Not</td>\n",
       "      <td>Divide &amp; Conquer</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.638</td>\n",
       "      <td>222369.000000</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.90900</td>\n",
       "      <td>0.157</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>222.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   instance_id           artist_name            track_name  popularity  \\\n",
       "0        32894              Röyksopp  Röyksopp's Night Out        0.27   \n",
       "1        46652  Thievery Corporation      The Shining Path        0.31   \n",
       "2        30097        Dillon Francis             Hurricane        0.28   \n",
       "3        62177              Dubloadz                 Nitro        0.34   \n",
       "4        24907           What So Not      Divide & Conquer        0.32   \n",
       "\n",
       "   acousticness  danceability    duration_ms  energy  instrumentalness  \\\n",
       "0       0.00468         0.652  246660.675637   0.941           0.79200   \n",
       "1       0.01270         0.622  218293.000000   0.890           0.95000   \n",
       "2       0.00306         0.620  215613.000000   0.755           0.01180   \n",
       "3       0.02540         0.774  166875.000000   0.700           0.00253   \n",
       "4       0.00465         0.638  222369.000000   0.587           0.90900   \n",
       "\n",
       "   liveness  ...  key_B  key_C  key_C# key_D  key_D# key_E  key_F  key_F#  \\\n",
       "0     0.115  ...      0      0       0     0       0     0      0       0   \n",
       "1     0.124  ...      0      0       0     1       0     0      0       0   \n",
       "2     0.534  ...      0      0       0     0       0     0      0       0   \n",
       "3     0.157  ...      0      0       1     0       0     0      0       0   \n",
       "4     0.157  ...      0      0       0     0       0     0      0       1   \n",
       "\n",
       "   key_G  duration_sec  \n",
       "0      0        246.66  \n",
       "1      0        218.29  \n",
       "2      0        215.61  \n",
       "3      0        166.88  \n",
       "4      0        222.37  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['instance_id'] = df['instance_id'].astype(int)\n",
    "df['artist_name'] = df['artist_name'].astype(str)\n",
    "df['track_name'] = df['track_name'].astype(str)\n",
    "\n",
    "df['popularity'] = df['popularity'].astype(float)\n",
    "df['acousticness'] = pd.to_numeric(df['acousticness'], errors='coerce')\n",
    "df['danceability'] = pd.to_numeric(df['danceability'], errors='coerce')\n",
    "df['duration_ms'] = pd.to_numeric(df['duration_ms'], errors='coerce')\n",
    "df['energy'] = pd.to_numeric(df['energy'], errors='coerce')\n",
    "df['instrumentalness'] = pd.to_numeric(df['instrumentalness'], errors='coerce')\n",
    "df['liveness'] = pd.to_numeric(df['liveness'], errors='coerce')\n",
    "df['loudness'] = pd.to_numeric(df['loudness'], errors='coerce')\n",
    "df['speechiness'] = pd.to_numeric(df['speechiness'], errors='coerce')\n",
    "df['tempo'] = pd.to_numeric(df['tempo'], errors='coerce')\n",
    "df['valence'] = pd.to_numeric(df['valence'], errors='coerce')\n",
    "\n",
    "df['mode_Major'] = df['mode_Major'].astype(int)\n",
    "df['key_A'] = df['key_A'].astype(int)\n",
    "df['key_A#'] = df['key_A#'].astype(int)\n",
    "df['key_B'] = df['key_B'].astype(int)\n",
    "df['key_C'] = df['key_C'].astype(int)\n",
    "df['key_C#'] = df['key_C#'].astype(int)\n",
    "df['key_D'] = df['key_D'].astype(int)\n",
    "df['key_D#'] = df['key_D#'].astype(int)\n",
    "df['key_E'] = df['key_E'].astype(int)\n",
    "df['key_F'] = df['key_F'].astype(int)\n",
    "df['key_F#'] = df['key_F#'].astype(int)\n",
    "df['key_G'] = df['key_G'].astype(int)\n",
    "\n",
    "df.dropna()\n",
    "df[\"duration_sec\"] = (df[\"duration_ms\"] / 1000).round(decimals=2)\n",
    "df.reset_index()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18ab60a6-e0f7-42d3-8898-d1588b5446b4",
   "metadata": {},
   "source": [
    "*The category labels of the genres will need to be transformed into numerical labels.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2c9079a4-918c-4687-84eb-13f8dbef90bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'Alternative', 1: 'Anime', 2: 'Blues', 3: 'Classical', 4: 'Country', 5: 'Electronic', 6: 'Hip-Hop', 7: 'Jazz', 8: 'Rap', 9: 'Rock'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "encoder = LabelEncoder()\n",
    "df['genre_label'] = encoder.fit_transform(df['music_genre'])\n",
    "genre_label_mapping = {label: genre for label, genre in zip(encoder.transform(encoder.classes_), encoder.classes_)}\n",
    "\n",
    "print(genre_label_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0bbd0dc2-6d98-46a1-993b-3169e7972c83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['4-Apr' '3-Apr' '5-Apr' '1-Apr' '0/4']\n"
     ]
    }
   ],
   "source": [
    "print(df[\"obtained_date\"].unique())\n",
    "# we dont need the instance_id and obtained_date for the analysis\n",
    "df = df.drop([\"instance_id\"], axis=1)\n",
    "df = df.drop([\"obtained_date\"], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8c17fecc-58df-453a-babd-d8e6fa4fb937",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['artist_name', 'track_name', 'popularity', 'acousticness', 'danceability', 'duration_ms', 'energy', 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo', 'valence', 'music_genre', 'mode_Major', 'key_A', 'key_A#', 'key_B', 'key_C', 'key_C#', 'key_D', 'key_D#', 'key_E', 'key_F', 'key_F#', 'key_G', 'duration_sec', 'genre_label', 'duration_minutes', 'duration_seconds', 'duration_formatted']\n"
     ]
    },
    {
     "data": {
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       "    }\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>artist_name</th>\n",
       "      <th>track_name</th>\n",
       "      <th>popularity</th>\n",
       "      <th>acousticness</th>\n",
       "      <th>danceability</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>energy</th>\n",
       "      <th>instrumentalness</th>\n",
       "      <th>liveness</th>\n",
       "      <th>loudness</th>\n",
       "      <th>...</th>\n",
       "      <th>key_D#</th>\n",
       "      <th>key_E</th>\n",
       "      <th>key_F</th>\n",
       "      <th>key_F#</th>\n",
       "      <th>key_G</th>\n",
       "      <th>duration_sec</th>\n",
       "      <th>genre_label</th>\n",
       "      <th>duration_minutes</th>\n",
       "      <th>duration_seconds</th>\n",
       "      <th>duration_formatted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Röyksopp</td>\n",
       "      <td>Röyksopp's Night Out</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.00468</td>\n",
       "      <td>0.652</td>\n",
       "      <td>246660.675637</td>\n",
       "      <td>0.941</td>\n",
       "      <td>0.79200</td>\n",
       "      <td>0.115</td>\n",
       "      <td>-5.201</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>246.66</td>\n",
       "      <td>5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0m 6.0s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Thievery Corporation</td>\n",
       "      <td>The Shining Path</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.01270</td>\n",
       "      <td>0.622</td>\n",
       "      <td>218293.000000</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.95000</td>\n",
       "      <td>0.124</td>\n",
       "      <td>-7.043</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>218.29</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>3.0m 38.0s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Dillon Francis</td>\n",
       "      <td>Hurricane</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00306</td>\n",
       "      <td>0.620</td>\n",
       "      <td>215613.000000</td>\n",
       "      <td>0.755</td>\n",
       "      <td>0.01180</td>\n",
       "      <td>0.534</td>\n",
       "      <td>-4.617</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>215.61</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>3.0m 35.0s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Dubloadz</td>\n",
       "      <td>Nitro</td>\n",
       "      <td>0.34</td>\n",
       "      <td>0.02540</td>\n",
       "      <td>0.774</td>\n",
       "      <td>166875.000000</td>\n",
       "      <td>0.700</td>\n",
       "      <td>0.00253</td>\n",
       "      <td>0.157</td>\n",
       "      <td>-4.498</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166.88</td>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0m 46.0s</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What So Not</td>\n",
       "      <td>Divide &amp; Conquer</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00465</td>\n",
       "      <td>0.638</td>\n",
       "      <td>222369.000000</td>\n",
       "      <td>0.587</td>\n",
       "      <td>0.90900</td>\n",
       "      <td>0.157</td>\n",
       "      <td>-6.266</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>222.37</td>\n",
       "      <td>5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>3.0m 42.0s</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            artist_name            track_name  popularity  acousticness  \\\n",
       "0              Röyksopp  Röyksopp's Night Out        0.27       0.00468   \n",
       "1  Thievery Corporation      The Shining Path        0.31       0.01270   \n",
       "2        Dillon Francis             Hurricane        0.28       0.00306   \n",
       "3              Dubloadz                 Nitro        0.34       0.02540   \n",
       "4           What So Not      Divide & Conquer        0.32       0.00465   \n",
       "\n",
       "   danceability    duration_ms  energy  instrumentalness  liveness  loudness  \\\n",
       "0         0.652  246660.675637   0.941           0.79200     0.115    -5.201   \n",
       "1         0.622  218293.000000   0.890           0.95000     0.124    -7.043   \n",
       "2         0.620  215613.000000   0.755           0.01180     0.534    -4.617   \n",
       "3         0.774  166875.000000   0.700           0.00253     0.157    -4.498   \n",
       "4         0.638  222369.000000   0.587           0.90900     0.157    -6.266   \n",
       "\n",
       "   ...  key_D#  key_E  key_F key_F#  key_G  duration_sec  genre_label  \\\n",
       "0  ...       0      0      0      0      0        246.66            5   \n",
       "1  ...       0      0      0      0      0        218.29            5   \n",
       "2  ...       0      0      0      0      0        215.61            5   \n",
       "3  ...       0      0      0      0      0        166.88            5   \n",
       "4  ...       0      0      0      1      0        222.37            5   \n",
       "\n",
       "   duration_minutes  duration_seconds  duration_formatted  \n",
       "0               4.0               6.0           4.0m 6.0s  \n",
       "1               3.0              38.0          3.0m 38.0s  \n",
       "2               3.0              35.0          3.0m 35.0s  \n",
       "3               2.0              46.0          2.0m 46.0s  \n",
       "4               3.0              42.0          3.0m 42.0s  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert duration from milliseconds to minutes and seconds\n",
    "df['duration_minutes'] = df['duration_ms'] // 60000  # 1 minute = 60,000 milliseconds\n",
    "df['duration_seconds'] = (df['duration_ms'] % 60000) // 1000  # 1 second = 1,000 milliseconds\n",
    "\n",
    "# combine minutes and seconds into a single column with a formatted string\n",
    "df['duration_formatted'] = df.apply(lambda row: f\"{row['duration_minutes']}m {row['duration_seconds']}s\", axis=1)\n",
    "\n",
    "\n",
    "print(df.columns.to_list())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "eb18406a-4f8e-4477-ae4d-81fcc849e7b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "continous_predictors= ['popularity', 'acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'loudness', 'speechiness', 'tempo', 'valence', 'duration_sec']\n",
    "\n",
    "categorical_predictors = ['mode_Major', 'key_A', 'key_A#', 'key_B', 'key_C', 'key_C#', 'key_D', 'key_D#', 'key_E', 'key_F', 'key_F#', 'key_G']\n",
    "\n",
    "# 'genre_label', 'music_genre'\n",
    "predictors_all = [\"artist_name\", \"track_name\"] + continous_predictors + categorical_predictors + [\"genre_label\", \"music_genre\"]\n",
    "final_df = df[predictors_all]\n",
    "final_df.to_csv('processed_dataset.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "656b5514-c634-481a-8dd6-34abc4278cb2",
   "metadata": {},
   "source": [
    "### Cleaned the dataset and saved it into the dataframe\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a02e5bcb-b2b5-4ab6-a813-38998fb9d481",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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