notebooks/main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center;color:red;\">Wilco</h1>\n",
    "<p style=\"text-align:center;\">Por Maycon Cypriano Batestin</p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About the Dataset\n",
    "\n",
    "The objective of this project is to analyze the lyrics of the band WILCO (or in this case any other band with a long history) throughout their career and be able to predict when, what and how the group's next songs will be. Using machine learning and NLP\n",
    "\n",
    "- **Fonte original:** Spotify\n",
    "- **Libertado por:** Maycon Batestin\n",
    "- **Licença:** Creative Commons Attribution-ShareAlike 4.0 International ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center;color:red;\">Glossary</h1>\n",
    "\n",
    "\n",
    "Fields\t                                                  | Type  \t  |    Description                              |\n",
    "----------------------------------------------------------|:---------:|:-------------------------------------------:|\n",
    "artist                            \t\t\t\t\t\t  |string     | name of artist                               |\n",
    "album                                                     |string     | name of album                                |\n",
    "track\t\t\t\t\t\t\t\t\t\t\t\t\t  |string     | name of a song belong to the album           |\n",
    "year\t\t\t\t\t\t\t\t\t\t\t\t\t  |int        | year of release of the album                 |\n",
    "lyrics\t\t\t\t\t\t\t\t\t\t\t\t\t  |string     | the lyrics about the song                    |\n",
    "duration_ms                                               |int        | Duration each song in ms                     |\n",
    "count_letter                                              |int        | count letter of lyric                        |\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center;color:red;\">Getting the Dataset </h1>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "artist = \"Wilco\"\n",
    "artist = artist.replace(\" \",\"_\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  0%|                                                    | 0/20 [00:00<?, ?it/s]Searching for \"Infinite Surprise\" by wilco...\n",
      "Done.\n",
      "Searching for \"Ten Dead\" by wilco...\n",
      "Done.\n",
      "Searching for \"Levee\" by wilco...\n",
      "Done.\n",
      "Searching for \"Evicted\" by wilco...\n",
      "Done.\n",
      "Searching for \"Sunlight Ends\" by wilco...\n",
      "Done.\n",
      "Searching for \"A Bowl and A Pudding\" by wilco...\n",
      "Done.\n",
      "Searching for \"Cousin\" by wilco...\n",
      "Done.\n",
      "Searching for \"Pittsburgh\" by wilco...\n",
      "Done.\n",
      "Searching for \"Soldier Child\" by wilco...\n",
      "  0%|                                                    | 0/20 [00:24<?, ?it/s]\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 466, in _make_request\n",
      "    six.raise_from(e, None)\n",
      "  File \"<string>\", line 3, in raise_from\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 461, in _make_request\n",
      "    httplib_response = conn.getresponse()\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/http/client.py\", line 1371, in getresponse\n",
      "    response.begin()\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/http/client.py\", line 319, in begin\n",
      "    version, status, reason = self._read_status()\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/http/client.py\", line 280, in _read_status\n",
      "    line = str(self.fp.readline(_MAXLINE + 1), \"iso-8859-1\")\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/socket.py\", line 704, in readinto\n",
      "    return self._sock.recv_into(b)\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/ssl.py\", line 1241, in recv_into\n",
      "    return self.read(nbytes, buffer)\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/ssl.py\", line 1099, in read\n",
      "    return self._sslobj.read(len, buffer)\n",
      "socket.timeout: The read operation timed out\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mayconcyprianobatestin/.local/lib/python3.9/site-packages/requests/adapters.py\", line 486, in send\n",
      "    resp = conn.urlopen(\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 798, in urlopen\n",
      "    retries = retries.increment(\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/urllib3/util/retry.py\", line 550, in increment\n",
      "    raise six.reraise(type(error), error, _stacktrace)\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/urllib3/packages/six.py\", line 770, in reraise\n",
      "    raise value\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 714, in urlopen\n",
      "    httplib_response = self._make_request(\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 468, in _make_request\n",
      "    self._raise_timeout(err=e, url=url, timeout_value=read_timeout)\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/urllib3/connectionpool.py\", line 357, in _raise_timeout\n",
      "    raise ReadTimeoutError(\n",
      "urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='genius.com', port=443): Read timed out. (read timeout=5)\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/lyricsgenius/api/base.py\", line 75, in _make_request\n",
      "    response = self._session.request(method, uri,\n",
      "  File \"/Users/mayconcyprianobatestin/.local/lib/python3.9/site-packages/requests/sessions.py\", line 589, in request\n",
      "    resp = self.send(prep, **send_kwargs)\n",
      "  File \"/Users/mayconcyprianobatestin/.local/lib/python3.9/site-packages/requests/sessions.py\", line 703, in send\n",
      "    r = adapter.send(request, **kwargs)\n",
      "  File \"/Users/mayconcyprianobatestin/.local/lib/python3.9/site-packages/requests/adapters.py\", line 532, in send\n",
      "    raise ReadTimeout(e, request=request)\n",
      "requests.exceptions.ReadTimeout: HTTPSConnectionPool(host='genius.com', port=443): Read timed out. (read timeout=5)\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"/Users/mayconcyprianobatestin/Documents/repositorios/DATA_SCIENCE/MUSIC/scripts/create_dataset.py\", line 93, in <module>\n",
      "    lyrics.append(get_lyrics(artist, track['name']))\n",
      "  File \"/Users/mayconcyprianobatestin/Documents/repositorios/DATA_SCIENCE/MUSIC/scripts/create_dataset.py\", line 57, in get_lyrics\n",
      "    song = genius.search_song(track_name, artist)\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/lyricsgenius/genius.py\", line 436, in search_song\n",
      "    lyrics = self.lyrics(song_url=song_info['url'])\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/lyricsgenius/genius.py\", line 132, in lyrics\n",
      "    self._make_request(path, web=True).replace('<br/>', '\\n'),\n",
      "  File \"/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/lyricsgenius/api/base.py\", line 84, in _make_request\n",
      "    raise Timeout(error)\n",
      "requests.exceptions.Timeout: Request timed out:\n",
      "HTTPSConnectionPool(host='genius.com', port=443): Read timed out. (read timeout=5)\n"
     ]
    }
   ],
   "source": [
    "!clear\n",
    "!python /Users/mayconcyprianobatestin/Documents/repositorios/DATA_SCIENCE/MUSIC/scripts/create_dataset.py $artist\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center;color:red;\">Librarys </h1>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Librarys\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as pyo\n",
    "import plotly.express as px\n",
    "import plotly.subplots as sp\n",
    "from plotly.subplots import make_subplots\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn import linear_model\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.metrics import accuracy_score\n",
    "from wordcloud import WordCloud\n",
    "import nltk \n",
    "from nltk import tokenize, RSLPStemmer\n",
    "import matplotlib.pyplot as plt\n",
    "from string import punctuation\n",
    "import unidecode\n",
    "import re\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center;color:red;\">Threating & Prepare Data </h1>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>album</th>\n",
       "      <th>track</th>\n",
       "      <th>year</th>\n",
       "      <th>lyrics</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Being There</td>\n",
       "      <td>Misunderstood</td>\n",
       "      <td>1996</td>\n",
       "      <td>17 ContributorsMisunderstood Lyrics[Intro]\\nWh...</td>\n",
       "      <td>388267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Being There</td>\n",
       "      <td>Why Would You Wanna Live</td>\n",
       "      <td>1996</td>\n",
       "      <td>6 ContributorsWhy Would You Wanna Live Lyrics[...</td>\n",
       "      <td>256293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Being There</td>\n",
       "      <td>(Was I) In Your Dreams</td>\n",
       "      <td>1996</td>\n",
       "      <td>5 Contributors(Was I) In Your Dreams LyricsWas...</td>\n",
       "      <td>210867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Being There</td>\n",
       "      <td>Kingpin</td>\n",
       "      <td>1996</td>\n",
       "      <td>9 ContributorsKingpin LyricsI want to be your ...</td>\n",
       "      <td>316853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Being There</td>\n",
       "      <td>Someone Else's Song</td>\n",
       "      <td>1996</td>\n",
       "      <td>6 ContributorsSomeone Else’s Song LyricsI can'...</td>\n",
       "      <td>201480</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "         album                     track  year  \\\n",
       "0  Being There             Misunderstood  1996   \n",
       "1  Being There  Why Would You Wanna Live  1996   \n",
       "2  Being There    (Was I) In Your Dreams  1996   \n",
       "3  Being There                   Kingpin  1996   \n",
       "4  Being There       Someone Else's Song  1996   \n",
       "\n",
       "                                              lyrics  duration_ms  \n",
       "0  17 ContributorsMisunderstood Lyrics[Intro]\\nWh...       388267  \n",
       "1  6 ContributorsWhy Would You Wanna Live Lyrics[...       256293  \n",
       "2  5 Contributors(Was I) In Your Dreams LyricsWas...       210867  \n",
       "3  9 ContributorsKingpin LyricsI want to be your ...       316853  \n",
       "4  6 ContributorsSomeone Else’s Song LyricsI can'...       201480  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = f'/Users/mayconcyprianobatestin/Documents/repositorios/DATA_SCIENCE/MUSIC/dataset/dataset_{artist.lower()}.csv'\n",
    "df = pd.read_csv(path)\n",
    "\n",
    "df = df[['album', 'track', 'year', 'lyrics', 'duration_ms']].groupby(\"year\").apply(lambda x: x.sort_values('year')).reset_index(drop=True)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking for NaN values and fixing!.\n"
     ]
    }
   ],
   "source": [
    "#check for NA value\n",
    "\n",
    "def checkNAN(df):\n",
    "    if df.isnull().values.any():\n",
    "        df.dropna(inplace=True) \n",
    "        df.reset_index(drop=True, inplace=True)\n",
    "        print(\"Checking for NaN values and fixing!.\")\n",
    "    else:\n",
    "        print(\"There no NaN values on your dataset\")\n",
    "\n",
    "checkNAN(df)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>album</th>\n",
       "      <th>track</th>\n",
       "      <th>year</th>\n",
       "      <th>lyrics</th>\n",
       "      <th>duration_ms</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Being There</td>\n",
       "      <td>Misunderstood</td>\n",
       "      <td>1996</td>\n",
       "      <td>17 ContributorsMisunderstood Lyrics[Intro]\\nWh...</td>\n",
       "      <td>388267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Being There</td>\n",
       "      <td>Why Would You Wanna Live</td>\n",
       "      <td>1996</td>\n",
       "      <td>6 ContributorsWhy Would You Wanna Live Lyrics[...</td>\n",
       "      <td>256293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Being There</td>\n",
       "      <td>(Was I) In Your Dreams</td>\n",
       "      <td>1996</td>\n",
       "      <td>5 Contributors(Was I) In Your Dreams LyricsWas...</td>\n",
       "      <td>210867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Being There</td>\n",
       "      <td>Kingpin</td>\n",
       "      <td>1996</td>\n",
       "      <td>9 ContributorsKingpin LyricsI want to be your ...</td>\n",
       "      <td>316853</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Being There</td>\n",
       "      <td>Someone Else's Song</td>\n",
       "      <td>1996</td>\n",
       "      <td>6 ContributorsSomeone Else’s Song LyricsI can'...</td>\n",
       "      <td>201480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Cousin</td>\n",
       "      <td>A Bowl and A Pudding</td>\n",
       "      <td>2023</td>\n",
       "      <td>2 ContributorsA Bowl and A Pudding Lyrics[Vers...</td>\n",
       "      <td>243493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>Cousin</td>\n",
       "      <td>Cousin</td>\n",
       "      <td>2023</td>\n",
       "      <td>2 ContributorsCousin Lyrics[Verse 1]\\nI cut in...</td>\n",
       "      <td>250640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>Cousin</td>\n",
       "      <td>Pittsburgh</td>\n",
       "      <td>2023</td>\n",
       "      <td>2 ContributorsPittsburgh Lyrics[Verse 1]\\nOh, ...</td>\n",
       "      <td>313907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>Cousin</td>\n",
       "      <td>Soldier Child</td>\n",
       "      <td>2023</td>\n",
       "      <td>3 ContributorsSoldier Child Lyrics[Verse]\\nSo ...</td>\n",
       "      <td>257347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>Cousin</td>\n",
       "      <td>Meant To Be</td>\n",
       "      <td>2023</td>\n",
       "      <td>2 ContributorsMeant To Be Lyrics[Verse 1]\\nIf ...</td>\n",
       "      <td>235293</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>294 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           album                     track  year  \\\n",
       "0    Being There             Misunderstood  1996   \n",
       "1    Being There  Why Would You Wanna Live  1996   \n",
       "2    Being There    (Was I) In Your Dreams  1996   \n",
       "3    Being There                   Kingpin  1996   \n",
       "4    Being There       Someone Else's Song  1996   \n",
       "..           ...                       ...   ...   \n",
       "289       Cousin      A Bowl and A Pudding  2023   \n",
       "290       Cousin                    Cousin  2023   \n",
       "291       Cousin                Pittsburgh  2023   \n",
       "292       Cousin             Soldier Child  2023   \n",
       "293       Cousin               Meant To Be  2023   \n",
       "\n",
       "                                                lyrics  duration_ms  \n",
       "0    17 ContributorsMisunderstood Lyrics[Intro]\\nWh...       388267  \n",
       "1    6 ContributorsWhy Would You Wanna Live Lyrics[...       256293  \n",
       "2    5 Contributors(Was I) In Your Dreams LyricsWas...       210867  \n",
       "3    9 ContributorsKingpin LyricsI want to be your ...       316853  \n",
       "4    6 ContributorsSomeone Else’s Song LyricsI can'...       201480  \n",
       "..                                                 ...          ...  \n",
       "289  2 ContributorsA Bowl and A Pudding Lyrics[Vers...       243493  \n",
       "290  2 ContributorsCousin Lyrics[Verse 1]\\nI cut in...       250640  \n",
       "291  2 ContributorsPittsburgh Lyrics[Verse 1]\\nOh, ...       313907  \n",
       "292  3 ContributorsSoldier Child Lyrics[Verse]\\nSo ...       257347  \n",
       "293  2 ContributorsMeant To Be Lyrics[Verse 1]\\nIf ...       235293  \n",
       "\n",
       "[294 rows x 5 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for duplicates\n",
    "\n",
    "def remove_duplicates_from_dataframe(df):\n",
    "    duplicates = df[df.duplicated()]\n",
    "    df_no_duplicates = df.drop_duplicates()\n",
    "    df.update(df_no_duplicates)\n",
    "\n",
    "    return df\n",
    "\n",
    "remove_duplicates_from_dataframe(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There no NaN values on your dataset\n"
     ]
    }
   ],
   "source": [
    "#fixing the colun lyrics \n",
    "\n",
    "def clean_lyrics(text):\n",
    "\n",
    "    if text.startswith('Investigation of the Ferguson Police'):\n",
    "        return ''\n",
    "    if text.startswith('Manifesto of the Communist Party In'):\n",
    "        return ''\n",
    "    if text.startswith('FREEDOM! Contents Introduction 1.'):\n",
    "        return ''\n",
    "    cleaned_text = re.sub(r'\\[[^\\]]+\\]', '', text)  \n",
    "    cleaned_text = re.sub(r'\\d+ Contributors', '', cleaned_text)  \n",
    "    cleaned_text = re.sub(r'\\\\n', ' ', cleaned_text)  \n",
    "    cleaned_text = re.sub(r'\\s+', ' ', cleaned_text)\n",
    "    cleaned_text = re.sub(r'^.*Lyrics', ' ', cleaned_text)   \n",
    "    return cleaned_text.strip()\n",
    "\n",
    "df['lyrics'] = df['lyrics'].apply(clean_lyrics)\n",
    "checkNAN(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a new coluna of count word\n",
    "\n",
    "def count_letters(text):\n",
    "    words = text.split()\n",
    "    total_letters = sum(len(word) for word in words)\n",
    "    return total_letters\n",
    "\n",
    "df['count_letter'] = df['lyrics'].apply(count_letters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "#normalize each album\n",
    "df = df.drop([145, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 77, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 245, 246, 247])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align:center;color:red;\">Visualization </h1>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \n",
    "<p> In this visualization, we see Wilco's albums over the years. Starting in 1996 with Being There and ending in 2023 with the album Cousin </p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
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         "mode": "markers",
         "name": "Being There",
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         "showlegend": true,
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         "legendgroup": "Mermaid Avenue",
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   ],
   "source": [
    "def album_years(df):\n",
    "    try:\n",
    "        fig = px.scatter(df, x=\"year\", color=\"album\", symbol=\"album\")\n",
    "        fig.update_traces(marker_size=10)\n",
    "        fig.update_layout(xaxis=dict(type='category'))\n",
    "        fig.update_xaxes(tickangle=45)\n",
    "        fig.update_layout(title_text='Albums over the years')\n",
    "        fig.update_yaxes(tickangle=45, showticklabels=False, title_text='')\n",
    "\n",
    "        return fig.show()\n",
    "    except:\n",
    "        return \"Somenthing wrong with your dataframe\"\n",
    "album_years(df)"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \n",
    "<p> In this visualization, we see the number of music tracks per album, throughout the releases. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
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    "    try: \n",
    "        album_track_counts = df.groupby(['album', 'year']).size().reset_index(name='num_tracks')\n",
    "        line = px.bar(album_track_counts, y='num_tracks', color='album')\n",
    "        line.update_layout(xaxis=dict(type='category'))\n",
    "        line.update_xaxes(tickangle=45, showticklabels=False, title_text=' ')\n",
    "        line.update_layout(title_text='Track from album, over the year')\n",
    "        line.update_yaxes(tickangle=45, showticklabels=False, title_text='')\n",
    "        for i, count in enumerate(album_track_counts['num_tracks']):\n",
    "            line.add_annotation(text=count, x=album_track_counts.index[i], y=count)\n",
    "        line.update_traces(textposition='outside')\n",
    "        line.add_annotation(\n",
    "            text=\"Number of Tracks\",\n",
    "            xref=\"paper\",\n",
    "            yref=\"paper\",\n",
    "            x=0.5,  \n",
    "            y=-0.1, \n",
    "            showarrow=False\n",
    "        )\n",
    "        \n",
    "        line.show()\n",
    "    except Exception as e:\n",
    "        return f\"Something went wrong: {str(e)}\"\n",
    "\n",
    "track_album_years(df[['album', 'track', 'year']])\n",
    "\n"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \n",
    "<p> In this view, we see the longest songs and the shortest songs. The percentage in relation to the total of the whole. </p>"
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   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexing.py:1773: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
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        "title": {
         "text": "Top 10 Shortest Tracks"
        }
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   "source": [
    "def create_pie_charts(df):\n",
    "    def ms_to_min_sec(ms):\n",
    "        minutes, seconds = divmod(ms // 1000, 60)\n",
    "        return f'{minutes}m{seconds}s'\n",
    "\n",
    "    df = df.sort_values(by='duration_ms', ascending=False)\n",
    "    top_10_longest_tracks = df.head(10)\n",
    "    top_10_longest_tracks.loc[:, 'duration_formatted'] = top_10_longest_tracks['duration_ms'].apply(ms_to_min_sec)\n",
    "    df = df.sort_values(by='duration_ms')\n",
    "    top_10_shortest_tracks = df.head(10)\n",
    "    top_10_shortest_tracks.loc[:, 'duration_formatted'] = top_10_shortest_tracks['duration_ms'].apply(ms_to_min_sec)\n",
    "    fig1 = px.pie(top_10_longest_tracks, names='track', values='duration_ms', color='track',\n",
    "                 hover_data=['track', 'duration_formatted'],\n",
    "                 title='Top 10 Longest Tracks')\n",
    "\n",
    "    fig1.update_traces(textinfo='label+percent', pull=[0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
    "    fig2 = px.pie(top_10_shortest_tracks, names='track', values='duration_ms',\n",
    "                 hover_data=['track', 'duration_formatted'],\n",
    "                 title='Top 10 Shortest Tracks')\n",
    "    fig2.update_traces(textinfo='label+percent', pull=[0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
    "\n",
    "    return fig1, fig2\n",
    "fig1, fig2 = create_pie_charts(df)\n",
    "fig1.show()\n",
    "fig2.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \n",
    "<p> In this view, we see the duration of each album in the band. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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         "customdata": [
          [
           "A Ghost Is Born (Deluxe Version)",
           "15m1s"
          ],
          [
           "The Whole Love (Deluxe Version)",
           "12m3s"
          ],
          [
           "A Ghost Is Born (Deluxe Version)",
           "10m42s"
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          [
           "Cruel Country",
           "7m52s"
          ],
          [
           "Yankee Hotel Foxtrot (2022 Remaster)",
           "7m21s"
          ],
          [
           "The Whole Love (Deluxe Version)",
           "7m16s"
          ],
          [
           "Being There",
           "6m51s"
          ],
          [
           "Being There",
           "6m43s"
          ],
          [
           "Being There",
           "6m28s"
          ],
          [
           "Mermaid Avenue Vol. II",
           "6m26s"
          ],
          [
           "Kicking Television, Live in Chicago",
           "6m23s"
          ],
          [
           "A Ghost Is Born (Deluxe Version)",
           "6m12s"
          ],
          [
           "Kicking Television, Live in Chicago",
           "6m8s"
          ],
          [
           "Sky Blue Sky",
           "6m5s"
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          [
           "Kicking Television, Live in Chicago",
           "6m3s"
          ],
          [
           "Kicking Television, Live in Chicago",
           "6m3s"
          ],
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           "A Ghost Is Born (Deluxe Version)",
           "6m3s"
          ],
          [
           "Sky Blue Sky",
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          ],
          [
           "A Ghost Is Born (Deluxe Version)",
           "5m54s"
          ],
          [
           "Ode to Joy",
           "5m49s"
          ],
          [
           "Cousin",
           "5m43s"
          ],
          [
           "Sky Blue Sky",
           "5m40s"
          ],
          [
           "Wilco (The Album)",
           "5m39s"
          ],
          [
           "Summerteeth",
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    "def create_album_duration(df):\n",
    "        def ms_to_min_sec(ms):\n",
    "                minutes, seconds = divmod(ms // 1000, 60)\n",
    "                return f'{minutes}m{seconds}s'\n",
    "\n",
    "        df_album_duration = df.sort_values(by='duration_ms', ascending=False)\n",
    "        df_album_duration.loc[:, 'duration_formatted'] = df_album_duration['duration_ms'].apply(ms_to_min_sec)\n",
    "        fig = px.pie(df_album_duration, names='album', values='duration_ms',\n",
    "                hover_data=['album', 'duration_formatted'],\n",
    "                title='Album Duration')\n",
    "        fig.update_traces(textinfo='label+percent', pull=[0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0])\n",
    "        return fig.show()\n",
    "create_album_duration(df)"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### \n",
    "<p> In this visualization we see the number of lyrics per song grouped by albums. </p>"
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   "cell_type": "code",
   "execution_count": 63,
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    "#count by letter of each album\n",
    "\n",
    "def count_by_letter(df):\n",
    "    name_album =  [ name for name in df['album'].unique() ]\n",
    "    for i in name_album:\n",
    "        album_name = df[df['album'] == f'{i}']\n",
    "        album_name = album_name.sort_values(by='count_letter', ascending=False)\n",
    "        fig = px.funnel(album_name, x='count_letter', y='track', color=\"track\")\n",
    "        fig.update_xaxes(tickangle=45, showticklabels=False, title_text=' ')\n",
    "        fig.update_yaxes(tickangle=45, showticklabels=False, title_text=' ')\n",
    "        fig.update_traces(orientation='h')\n",
    "        fig.add_annotation(\n",
    "                    text=f\"Album - {i}\",\n",
    "                    xref=\"paper\",\n",
    "                    yref=\"paper\",\n",
    "                    x=0.5,  \n",
    "                    y=-0.1, \n",
    "                    showarrow=False\n",
    "                )\n",
    "        fig.show()\n",
    "\n",
    "    return None\n",
    "count_by_letter(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
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     "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",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>duration_ms</th>\n",
       "      <th>count_letter</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>268.000000</td>\n",
       "      <td>268.000000</td>\n",
       "      <td>268.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2009.156716</td>\n",
       "      <td>232383.216418</td>\n",
       "      <td>1012.996269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.387037</td>\n",
       "      <td>87743.499256</td>\n",
       "      <td>4749.449489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1996.000000</td>\n",
       "      <td>22880.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>185586.500000</td>\n",
       "      <td>481.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>218387.000000</td>\n",
       "      <td>634.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>256433.000000</td>\n",
       "      <td>848.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2023.000000</td>\n",
       "      <td>901333.000000</td>\n",
       "      <td>77694.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year    duration_ms  count_letter\n",
       "count   268.000000     268.000000    268.000000\n",
       "mean   2009.156716  232383.216418   1012.996269\n",
       "std       8.387037   87743.499256   4749.449489\n",
       "min    1996.000000   22880.000000     21.000000\n",
       "25%    2002.000000  185586.500000    481.000000\n",
       "50%    2009.000000  218387.000000    634.500000\n",
       "75%    2016.000000  256433.000000    848.000000\n",
       "max    2023.000000  901333.000000  77694.000000"
      ]
     },
     "execution_count": 142,
     "metadata": {},
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    "# accuracy \n",
    "\n",
    "df.head()\n",
    "\n",
    "vetor = CountVectorizer()\n",
    "bag = vetor.fit_transform(df['album'])\n",
    "train, test, class_train, class_test = train_test_split(bag, df['year'])\n",
    "regres_logistc = LogisticRegression()\n",
    "regres_logistc.fit(train, class_train)\n",
    "acuracy = regres_logistc.score(test, class_test)\n",
    "final = {\"acuracy\": acuracy}\n",
    "graph = pd.DataFrame.from_dict(final, orient='index', columns=['Value'])\n",
    "graph.head()\n",
    "\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Indicator(\n",
    "        mode=\"number+gauge+delta\",\n",
    "        value=graph['Value'][0],\n",
    "        title={'text': \"acuracy\"},\n",
    "        domain={'row': 0, 'column': 0}\n",
    "    ))\n",
    "\n",
    "fig.update_layout(\n",
    "        title=\"Contagem de Acuracia\",\n",
    "        height=300,\n",
    "    )\n",
    "fig.update_traces(uirevision=\"top center\")\n",
    "\n",
    "fig.show() \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Com base no album Cousin, Wilco irá lançar o próximo álbum no ano 2022\n"
     ]
    }
   ],
   "source": [
    "# Ordenar os dados por ano em ordem decrescente\n",
    "df['album_id'] = pd.factorize(df['album'])[0]\n",
    "\n",
    "reg = LinearRegression()\n",
    "\n",
    "X = df[['album_id']].values\n",
    "y = df['year'].values\n",
    "\n",
    "reg.fit(X, y)\n",
    "\n",
    "\n",
    "query = df[df['album'] == 'Cousin']\n",
    "number_album = query.iloc[0]['album_id']\n",
    "name = query.iloc[0]['album']\n",
    "album_name = np.array([[number_album]])\n",
    "\n",
    "predicted_year = int(reg.predict(album_name))\n",
    "\n",
    "print(f\"Com base no album {name}, Wilco irá lançar o próximo álbum no ano {predicted_year}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Com base no álbum Cousin, a previsão para o ano 2028\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:578: ValueWarning:\n",
      "\n",
      "An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "\n",
      "/Users/mayconcyprianobatestin/opt/anaconda3/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:578: ValueWarning:\n",
      "\n",
      "An unsupported index was provided and will be ignored when e.g. forecasting.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import date\n",
    "from statsmodels.tsa.arima_model import ARIMA\n",
    "\n",
    "current_year = date.today().year\n",
    "df = df[df['year'] <= current_year]\n",
    "\n",
    "df['album_id'] = pd.factorize(df['album'])[0]\n",
    "\n",
    "model = ARIMA(df['year'], order=(1, 1, 1))\n",
    "model_fit = model.fit(disp=0)\n",
    "\n",
    "album_name = 'Cousin'\n",
    "album_id = df[df['album'] == album_name].iloc[0]['album_id']\n",
    "years_to_predict = range(current_year + 1, current_year + 6)  \n",
    "\n",
    "for year in years_to_predict:\n",
    "    forecast = model_fit.forecast(steps=1)  \n",
    "    predicted_year = int(forecast[0])\n",
    "print(f\"Com base no álbum {album_name}, a previsão para o ano {year}\")\n"
   ]
  }
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