Working_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "[View in Colaboratory](https://colab.research.google.com/github/ucalyptus/Spotify-Recommendation-Engine/blob/master/Spotify_Recommendation_System_.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "LUBdOIn5irHS",
    "outputId": "cf3771a3-0820-470a-d19b-f19a56db8d23"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: spotipy in /usr/local/lib/python3.6/dist-packages (2.4.4)\n",
      "Requirement already satisfied: requests>=1.0 in /usr/local/lib/python3.6/dist-packages (from spotipy) (2.18.4)\n",
      "Requirement already satisfied: idna<2.7,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=1.0->spotipy) (2.6)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=1.0->spotipy) (3.0.4)\n",
      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=1.0->spotipy) (1.22)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=1.0->spotipy) (2018.8.24)\n"
     ]
    }
   ],
   "source": [
    "!pip install spotipy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Uh7PhpFWkcVg"
   },
   "source": [
    "Client ID ffbef2bcb4b84e80b3a0039a2906cb01\n",
    "\n",
    "Client Secret 6c57daa1247f4abe96f38635d38869a0\n",
    "\n",
    "Redirect URI https://developer.spotify.com/dashboard/applications/ffbef2bcb4b84e80b3a0039a2906cb01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CQiyBuLziyEI"
   },
   "outputs": [],
   "source": [
    "import spotipy\n",
    "from spotipy.oauth2 import SpotifyClientCredentials\n",
    "import spotipy.util as util"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xo3x51nrk48s"
   },
   "outputs": [],
   "source": [
    "cid = 'ffbef2bcb4b84e80b3a0039a2906cb01'\n",
    "secret = '6c57daa1247f4abe96f38635d38869a0'\n",
    "username = 'francocasadei'\n",
    "redirect_uri = 'https://developer.spotify.com/dashboard/applications/ffbef2bcb4b84e80b3a0039a2906cb01'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6wqoMOuOlf52"
   },
   "outputs": [],
   "source": [
    "scope = 'user-library-read playlist-modify-public playlist-read-private'\n",
    "\n",
    "client_credentials_manager = SpotifyClientCredentials(client_id = cid, client_secret=secret)\n",
    "\n",
    "sp = spotipy.Spotify(client_credentials_manager= client_credentials_manager)\n",
    "\n",
    "token = util.prompt_for_user_token(username,scope,cid,secret,redirect_uri)\n",
    "\n",
    "if token:\n",
    "  sp = spotipy.Spotify(auth=token)\n",
    "  \n",
    "else:\n",
    "  print(\"Can't get token for\", username)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WxaTn9mLmcqZ"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#https://open.spotify.com/user/francocasadei/playlist/6aWL6tZgIycyz98WgigfsG\n",
    "sourcePlaylistID = '6aWL6tZgIycyz98WgigfsG'\n",
    "sourcePlaylist = sp.user_playlist(username,sourcePlaylistID);\n",
    "tracks = sourcePlaylist[\"tracks\"];\n",
    "songs = tracks[\"items\"];\n",
    "\n",
    "track_ids = []\n",
    "track_names = []\n",
    "\n",
    "\n",
    "for i in range(0, len(songs)):\n",
    "  if songs[i]['track']['id']!=None:\n",
    "    track_ids.append(songs[i]['track']['id'])\n",
    "    track_names.append(songs[i]['track']['name'])\n",
    "    \n",
    "features = []\n",
    "\n",
    "\n",
    "for i in range(0,len(track_ids)):\n",
    "  audio_features  = sp.audio_features(track_ids[i])\n",
    "  for track in audio_features:\n",
    "    features.append(track)\n",
    "    \n",
    "    \n",
    "playlist_df = pd.DataFrame(features,index = track_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MooiGYAppQ91"
   },
   "outputs": [],
   "source": [
    "#francocasadei@yahoo.it:astrolabio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mA3jsda2sk2n"
   },
   "outputs": [],
   "source": [
    "playlist_df = playlist_df[[\"id\", \"acousticness\", \"danceability\", \"duration_ms\", \"energy\", \"instrumentalness\",  \"key\", \"liveness\", \"loudness\", \"mode\", \"speechiness\", \"tempo\", \"valence\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "oHA0fckEtZ9u",
    "outputId": "a54775ec-418f-4e34-ee12-065ee275c86d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 13)"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cJBLzEg_twFM"
   },
   "outputs": [],
   "source": [
    "playlist_df['ratings']=[10, 9, 9, 10, 8, 6, 8, 4, 3, 5, 7, 5, 5, 8, 8,10,4,6,8,2,4,5,6,9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1017
    },
    "colab_type": "code",
    "id": "ZO9-WVjnuR-F",
    "outputId": "12570c48-ebd2-4933-b4fc-05dd135b701b"
   },
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</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",
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       "      <th>speechiness</th>\n",
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       "      <th>valence</th>\n",
       "      <th>ratings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Losers</th>\n",
       "      <td>1bAvtmTl0F9loyz9AwPwp7</td>\n",
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       "    <tr>\n",
       "      <th>Tell Your Friends</th>\n",
       "      <td>1mhVXWduD8ReDwusfaHNwU</td>\n",
       "      <td>0.26500</td>\n",
       "      <td>0.565</td>\n",
       "      <td>334333</td>\n",
       "      <td>0.454</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>-11.091</td>\n",
       "      <td>0</td>\n",
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       "      <td>74.809</td>\n",
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       "    <tr>\n",
       "      <th>Often</th>\n",
       "      <td>4PhsKqMdgMEUSstTDAmMpg</td>\n",
       "      <td>0.21800</td>\n",
       "      <td>0.569</td>\n",
       "      <td>249040</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
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       "      <td>-9.837</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>134.035</td>\n",
       "      <td>0.0726</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Hills</th>\n",
       "      <td>7fBv7CLKzipRk6EC6TWHOB</td>\n",
       "      <td>0.06710</td>\n",
       "      <td>0.580</td>\n",
       "      <td>242253</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1350</td>\n",
       "      <td>-7.065</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0488</td>\n",
       "      <td>112.986</td>\n",
       "      <td>0.1380</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Acquainted</th>\n",
       "      <td>7uJH0YdSZL6psjxI6Xy08b</td>\n",
       "      <td>0.48400</td>\n",
       "      <td>0.372</td>\n",
       "      <td>348853</td>\n",
       "      <td>0.477</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0844</td>\n",
       "      <td>-9.838</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0497</td>\n",
       "      <td>101.097</td>\n",
       "      <td>0.2540</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Can't Feel My Face</th>\n",
       "      <td>22VdIZQfgXJea34mQxlt81</td>\n",
       "      <td>0.11200</td>\n",
       "      <td>0.705</td>\n",
       "      <td>213520</td>\n",
       "      <td>0.769</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>0.1050</td>\n",
       "      <td>-5.526</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0426</td>\n",
       "      <td>107.939</td>\n",
       "      <td>0.5900</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shameless</th>\n",
       "      <td>6C6fHTuFUxVtJpBOOaMvJr</td>\n",
       "      <td>0.14600</td>\n",
       "      <td>0.603</td>\n",
       "      <td>253507</td>\n",
       "      <td>0.562</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1090</td>\n",
       "      <td>-8.311</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0267</td>\n",
       "      <td>100.038</td>\n",
       "      <td>0.2440</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Earned It (Fifty Shades Of Grey)</th>\n",
       "      <td>4frLb7nWtsz2ymBE6k2GRP</td>\n",
       "      <td>0.40800</td>\n",
       "      <td>0.647</td>\n",
       "      <td>277680</td>\n",
       "      <td>0.291</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0771</td>\n",
       "      <td>-8.275</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>119.839</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>In The Night</th>\n",
       "      <td>25KybV9BOUlvcnv7nN3Pyo</td>\n",
       "      <td>0.07960</td>\n",
       "      <td>0.505</td>\n",
       "      <td>235653</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0465</td>\n",
       "      <td>-4.990</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0713</td>\n",
       "      <td>168.021</td>\n",
       "      <td>0.5390</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>As You Are</th>\n",
       "      <td>3H3qBumaYj67PT3aMEBvAP</td>\n",
       "      <td>0.13100</td>\n",
       "      <td>0.371</td>\n",
       "      <td>340493</td>\n",
       "      <td>0.329</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2790</td>\n",
       "      <td>-9.667</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0574</td>\n",
       "      <td>173.661</td>\n",
       "      <td>0.0539</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dark Times</th>\n",
       "      <td>6J7PIkhtHwmFjPLq0SyuPj</td>\n",
       "      <td>0.11400</td>\n",
       "      <td>0.501</td>\n",
       "      <td>260640</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>9</td>\n",
       "      <td>0.1240</td>\n",
       "      <td>-9.607</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0752</td>\n",
       "      <td>132.608</td>\n",
       "      <td>0.2700</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prisoner</th>\n",
       "      <td>1gZADNt16Oh23jWyMYRk4p</td>\n",
       "      <td>0.44100</td>\n",
       "      <td>0.550</td>\n",
       "      <td>274960</td>\n",
       "      <td>0.404</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1100</td>\n",
       "      <td>-12.755</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0398</td>\n",
       "      <td>135.016</td>\n",
       "      <td>0.3290</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angel</th>\n",
       "      <td>0wzPHTgOtFIDhk2rD3Au4x</td>\n",
       "      <td>0.10500</td>\n",
       "      <td>0.626</td>\n",
       "      <td>377347</td>\n",
       "      <td>0.583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1150</td>\n",
       "      <td>-7.574</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0435</td>\n",
       "      <td>127.956</td>\n",
       "      <td>0.3350</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Switch</th>\n",
       "      <td>29ZZAShKhwRUuKULSNtxX6</td>\n",
       "      <td>0.04270</td>\n",
       "      <td>0.664</td>\n",
       "      <td>207071</td>\n",
       "      <td>0.664</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>-6.104</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0393</td>\n",
       "      <td>176.678</td>\n",
       "      <td>0.1900</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTW</th>\n",
       "      <td>6Hgh47WXVKtXN5zGOu0hjI</td>\n",
       "      <td>0.18300</td>\n",
       "      <td>0.652</td>\n",
       "      <td>263014</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>2</td>\n",
       "      <td>0.1060</td>\n",
       "      <td>-6.183</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0541</td>\n",
       "      <td>72.989</td>\n",
       "      <td>0.2800</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Real Life</th>\n",
       "      <td>03j354P848KtNU2FVSwkDG</td>\n",
       "      <td>0.43800</td>\n",
       "      <td>0.581</td>\n",
       "      <td>223373</td>\n",
       "      <td>0.653</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.3510</td>\n",
       "      <td>-5.040</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0506</td>\n",
       "      <td>90.726</td>\n",
       "      <td>0.3460</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pretty</th>\n",
       "      <td>0ei3uNcwJOiUocuks1vWcb</td>\n",
       "      <td>0.03490</td>\n",
       "      <td>0.581</td>\n",
       "      <td>375400</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.000057</td>\n",
       "      <td>11</td>\n",
       "      <td>0.3290</td>\n",
       "      <td>-6.832</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0382</td>\n",
       "      <td>125.979</td>\n",
       "      <td>0.3310</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hotline Bling</th>\n",
       "      <td>0wwPcA6wtMf6HUMpIRdeP7</td>\n",
       "      <td>0.00302</td>\n",
       "      <td>0.903</td>\n",
       "      <td>267067</td>\n",
       "      <td>0.621</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0504</td>\n",
       "      <td>-8.132</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0578</td>\n",
       "      <td>134.969</td>\n",
       "      <td>0.5530</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The Morning</th>\n",
       "      <td>6u0dQik0aif7FQlrhycG1L</td>\n",
       "      <td>0.31700</td>\n",
       "      <td>0.652</td>\n",
       "      <td>312427</td>\n",
       "      <td>0.549</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>-7.555</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0390</td>\n",
       "      <td>120.073</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>High For This</th>\n",
       "      <td>5fohLPNqO6TqwrZ7BoUWUT</td>\n",
       "      <td>0.74400</td>\n",
       "      <td>0.465</td>\n",
       "      <td>247227</td>\n",
       "      <td>0.403</td>\n",
       "      <td>0.000667</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0777</td>\n",
       "      <td>-8.895</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0755</td>\n",
       "      <td>74.950</td>\n",
       "      <td>0.0687</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wicked Games</th>\n",
       "      <td>6VwBbL8CzPiC4QV66ay7oR</td>\n",
       "      <td>0.00959</td>\n",
       "      <td>0.658</td>\n",
       "      <td>323747</td>\n",
       "      <td>0.591</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>9</td>\n",
       "      <td>0.3250</td>\n",
       "      <td>-5.197</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0309</td>\n",
       "      <td>114.078</td>\n",
       "      <td>0.3260</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kiss Land</th>\n",
       "      <td>5idmfDT06TjCQ1SPDsa5qh</td>\n",
       "      <td>0.04910</td>\n",
       "      <td>0.326</td>\n",
       "      <td>455333</td>\n",
       "      <td>0.862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0654</td>\n",
       "      <td>-6.671</td>\n",
       "      <td>0</td>\n",
       "      <td>0.1190</td>\n",
       "      <td>122.037</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Desire - Gryffin Remix</th>\n",
       "      <td>6JzCO4ZzxAXhY86vTcqM1Q</td>\n",
       "      <td>0.06060</td>\n",
       "      <td>0.543</td>\n",
       "      <td>268840</td>\n",
       "      <td>0.876</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0.1570</td>\n",
       "      <td>-5.069</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0512</td>\n",
       "      <td>122.058</td>\n",
       "      <td>0.4150</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>King</th>\n",
       "      <td>5Z3zRrMIJk7CVpfcLsM9wD</td>\n",
       "      <td>0.05940</td>\n",
       "      <td>0.560</td>\n",
       "      <td>215360</td>\n",
       "      <td>0.857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>0.3810</td>\n",
       "      <td>-4.145</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0384</td>\n",
       "      <td>119.975</td>\n",
       "      <td>0.4320</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      id  acousticness  \\\n",
       "Losers                            1bAvtmTl0F9loyz9AwPwp7       0.03550   \n",
       "Tell Your Friends                 1mhVXWduD8ReDwusfaHNwU       0.26500   \n",
       "Often                             4PhsKqMdgMEUSstTDAmMpg       0.21800   \n",
       "The Hills                         7fBv7CLKzipRk6EC6TWHOB       0.06710   \n",
       "Acquainted                        7uJH0YdSZL6psjxI6Xy08b       0.48400   \n",
       "Can't Feel My Face                22VdIZQfgXJea34mQxlt81       0.11200   \n",
       "Shameless                         6C6fHTuFUxVtJpBOOaMvJr       0.14600   \n",
       "Earned It (Fifty Shades Of Grey)  4frLb7nWtsz2ymBE6k2GRP       0.40800   \n",
       "In The Night                      25KybV9BOUlvcnv7nN3Pyo       0.07960   \n",
       "As You Are                        3H3qBumaYj67PT3aMEBvAP       0.13100   \n",
       "Dark Times                        6J7PIkhtHwmFjPLq0SyuPj       0.11400   \n",
       "Prisoner                          1gZADNt16Oh23jWyMYRk4p       0.44100   \n",
       "Angel                             0wzPHTgOtFIDhk2rD3Au4x       0.10500   \n",
       "Switch                            29ZZAShKhwRUuKULSNtxX6       0.04270   \n",
       "OTW                               6Hgh47WXVKtXN5zGOu0hjI       0.18300   \n",
       "Real Life                         03j354P848KtNU2FVSwkDG       0.43800   \n",
       "Pretty                            0ei3uNcwJOiUocuks1vWcb       0.03490   \n",
       "Hotline Bling                     0wwPcA6wtMf6HUMpIRdeP7       0.00302   \n",
       "The Morning                       6u0dQik0aif7FQlrhycG1L       0.31700   \n",
       "High For This                     5fohLPNqO6TqwrZ7BoUWUT       0.74400   \n",
       "Wicked Games                      6VwBbL8CzPiC4QV66ay7oR       0.00959   \n",
       "Kiss Land                         5idmfDT06TjCQ1SPDsa5qh       0.04910   \n",
       "Desire - Gryffin Remix            6JzCO4ZzxAXhY86vTcqM1Q       0.06060   \n",
       "King                              5Z3zRrMIJk7CVpfcLsM9wD       0.05940   \n",
       "\n",
       "                                  danceability  duration_ms  energy  \\\n",
       "Losers                                   0.666       281147   0.485   \n",
       "Tell Your Friends                        0.565       334333   0.454   \n",
       "Often                                    0.569       249040   0.629   \n",
       "The Hills                                0.580       242253   0.565   \n",
       "Acquainted                               0.372       348853   0.477   \n",
       "Can't Feel My Face                       0.705       213520   0.769   \n",
       "Shameless                                0.603       253507   0.562   \n",
       "Earned It (Fifty Shades Of Grey)         0.647       277680   0.291   \n",
       "In The Night                             0.505       235653   0.680   \n",
       "As You Are                               0.371       340493   0.329   \n",
       "Dark Times                               0.501       260640   0.403   \n",
       "Prisoner                                 0.550       274960   0.404   \n",
       "Angel                                    0.626       377347   0.583   \n",
       "Switch                                   0.664       207071   0.664   \n",
       "OTW                                      0.652       263014   0.678   \n",
       "Real Life                                0.581       223373   0.653   \n",
       "Pretty                                   0.581       375400   0.691   \n",
       "Hotline Bling                            0.903       267067   0.621   \n",
       "The Morning                              0.652       312427   0.549   \n",
       "High For This                            0.465       247227   0.403   \n",
       "Wicked Games                             0.658       323747   0.591   \n",
       "Kiss Land                                0.326       455333   0.862   \n",
       "Desire - Gryffin Remix                   0.543       268840   0.876   \n",
       "King                                     0.560       215360   0.857   \n",
       "\n",
       "                                  instrumentalness  key  liveness  loudness  \\\n",
       "Losers                                    0.000603    0    0.5040    -9.850   \n",
       "Tell Your Friends                         0.000000    1    0.1810   -11.091   \n",
       "Often                                     0.000000    7    0.1350    -9.837   \n",
       "The Hills                                 0.000000    0    0.1350    -7.065   \n",
       "Acquainted                                0.000000    7    0.0844    -9.838   \n",
       "Can't Feel My Face                        0.000000    9    0.1050    -5.526   \n",
       "Shameless                                 0.000000    6    0.1090    -8.311   \n",
       "Earned It (Fifty Shades Of Grey)          0.000000    2    0.0771    -8.275   \n",
       "In The Night                              0.000000    7    0.0465    -4.990   \n",
       "As You Are                                0.006250    0    0.2790    -9.667   \n",
       "Dark Times                                0.000011    9    0.1240    -9.607   \n",
       "Prisoner                                  0.000220    0    0.1100   -12.755   \n",
       "Angel                                     0.000000   10    0.1150    -7.574   \n",
       "Switch                                    0.004890    7    0.1040    -6.104   \n",
       "OTW                                       0.000033    2    0.1060    -6.183   \n",
       "Real Life                                 0.000000   11    0.3510    -5.040   \n",
       "Pretty                                    0.000057   11    0.3290    -6.832   \n",
       "Hotline Bling                             0.000186    2    0.0504    -8.132   \n",
       "The Morning                               0.000000    6    0.1040    -7.555   \n",
       "High For This                             0.000667   11    0.0777    -8.895   \n",
       "Wicked Games                              0.000005    9    0.3250    -5.197   \n",
       "Kiss Land                                 0.000000   11    0.0654    -6.671   \n",
       "Desire - Gryffin Remix                    0.000000   10    0.1570    -5.069   \n",
       "King                                      0.000000    4    0.3810    -4.145   \n",
       "\n",
       "                                  mode  speechiness    tempo  valence  ratings  \n",
       "Losers                               0       0.0527   91.009   0.2880       10  \n",
       "Tell Your Friends                    0       0.0461   74.809   0.3120        9  \n",
       "Often                                0       0.0485  134.035   0.0726        9  \n",
       "The Hills                            0       0.0488  112.986   0.1380       10  \n",
       "Acquainted                           0       0.0497  101.097   0.2540        8  \n",
       "Can't Feel My Face                   0       0.0426  107.939   0.5900        6  \n",
       "Shameless                            1       0.0267  100.038   0.2440        8  \n",
       "Earned It (Fifty Shades Of Grey)     0       0.0363  119.839   0.4000        4  \n",
       "In The Night                         1       0.0713  168.021   0.5390        3  \n",
       "As You Are                           0       0.0574  173.661   0.0539        5  \n",
       "Dark Times                           0       0.0752  132.608   0.2700        7  \n",
       "Prisoner                             0       0.0398  135.016   0.3290        5  \n",
       "Angel                                0       0.0435  127.956   0.3350        5  \n",
       "Switch                               1       0.0393  176.678   0.1900        8  \n",
       "OTW                                  1       0.0541   72.989   0.2800        8  \n",
       "Real Life                            0       0.0506   90.726   0.3460       10  \n",
       "Pretty                               0       0.0382  125.979   0.3310        4  \n",
       "Hotline Bling                        1       0.0578  134.969   0.5530        6  \n",
       "The Morning                          0       0.0390  120.073   0.1860        8  \n",
       "High For This                        0       0.0755   74.950   0.0687        2  \n",
       "Wicked Games                         1       0.0309  114.078   0.3260        4  \n",
       "Kiss Land                            0       0.1190  122.037   0.4000        5  \n",
       "Desire - Gryffin Remix               0       0.0512  122.058   0.4150        6  \n",
       "King                                 0       0.0384  119.975   0.4320        9  "
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lQ-PbjSzuUrD"
   },
   "outputs": [],
   "source": [
    "X_train = playlist_df.drop(['id', 'ratings'], axis=1)\n",
    "y_train = playlist_df['ratings']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "OG_UdNebud6H",
    "outputId": "6b16b6c9-2860-48e1-c082-7d93bb5154b1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mT5+uDh06+CghAAAAGhOKMgAA4HMnT57UwoULNWfOHB08eNDjvODgYI0bN04J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\n9lsKCwtZuXJlcW2HHXZg4sSJ1KmT1AJHSZIkSZKkLZdIoyyE0AX4c4x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N5Z/cgY49vnnG9cbfuM6murk+ecXwrRxhhnVW+rHjnGeNGcc+Nk+t9bfdvGTq3NjDF+tPr/Vs/h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\n5koAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb184fd3710>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 425,
       "width": 613
      },
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import decomposition\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set(style='white')\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "\n",
    "X_scaled = StandardScaler().fit_transform(X_train)\n",
    "\n",
    "pca = decomposition.PCA().fit(X_scaled)\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "plt.plot(np.cumsum(pca.explained_variance_ratio_),color='k',lw=2)\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Total explained variance')\n",
    "plt.xlim(0,12)\n",
    "plt.yticks(np.arange(0,1.1,0.1))\n",
    "plt.axvline(8,c='b')\n",
    "plt.axhline(0.95,c='r')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Te0mkPNLv8O7"
   },
   "outputs": [],
   "source": [
    "# Fit your dataset to the optimal pca\n",
    "pca = decomposition.PCA(n_components=8)\n",
    "X_pca = pca.fit_transform(X_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Scla3DOowqsA",
    "outputId": "7a54aa1d-aa2b-45eb-9f46-867261bf7ba4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24, 90)"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "v = TfidfVectorizer(sublinear_tf = True, ngram_range=(1,6),\n",
    "                   max_features=10000)\n",
    "X_names_sparse=v.fit_transform(track_names)\n",
    "X_names_sparse.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xX3LjfChxDWn"
   },
   "outputs": [],
   "source": [
    "from scipy.sparse import csr_matrix, hstack\n",
    "\n",
    "X_train_last = csr_matrix(hstack([X_pca, X_names_sparse]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yb5E1q6UxKFx"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import StratifiedKFold, GridSearchCV\n",
    "\n",
    "skf = StratifiedKFold(n_splits=2,shuffle=True,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 734
    },
    "colab_type": "code",
    "id": "Pej5Ou65Ca4I",
    "outputId": "63acc604-8638-4492-a418-28e9b74fe1fd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 9 candidates, totalling 18 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=2.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/parallel.py:547: UserWarning: Multiprocessing-backed parallel loops cannot be nested, setting n_jobs=1\n",
      "  **self._backend_args)\n",
      "[Parallel(n_jobs=-1)]: Done  18 out of  18 | elapsed:    0.2s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "({'n_neighbors': 1}, 0.25)"
      ]
     },
     "execution_count": 17,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_params = {'n_neighbors':range(1,10)}\n",
    "knn = KNeighborsClassifier(n_jobs=-1)\n",
    "\n",
    "knn_grid= GridSearchCV(knn,knn_params,cv=skf,n_jobs =-1,verbose=True)\n",
    "knn_grid.fit(X_train_last,y_train)\n",
    "knn_grid.best_params_, knn_grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "colab_type": "code",
    "id": "mYx2CCdtDO03",
    "outputId": "87296fcb-8388-4238-c543-32f046fb1d10"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 48 candidates, totalling 96 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=2.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "[Parallel(n_jobs=-1)]: Done  46 tasks      | elapsed:   11.0s\n",
      "[Parallel(n_jobs=-1)]: Done  96 out of  96 | elapsed:   22.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "             max_depth=8, max_features=10, max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=-1,\n",
       "             oob_score=True, random_state=42, verbose=0, warm_start=False),\n",
       " 0.375)"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "parameters = {'max_features':[4,7,8,10],'min_samples_leaf':[1,3,5,8],'max_depth':[3,5,8]}\n",
    "rfc = RandomForestClassifier(n_estimators=100,random_state=42,n_jobs=-1,oob_score=True)\n",
    "forest_grid = GridSearchCV(rfc,parameters,n_jobs=-1,cv=skf,verbose=1)\n",
    "forest_grid.fit(X_train_last,y_train)\n",
    "forest_grid.best_estimator_ , forest_grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 207
    },
    "colab_type": "code",
    "id": "aQChN_fzLon5",
    "outputId": "c6829f66-ce7b-4844-a493-db3d46fb5db9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 2 folds for each of 150 candidates, totalling 300 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_split.py:605: Warning: The least populated class in y has only 1 members, which is too few. The minimum number of members in any class cannot be less than n_splits=2.\n",
      "  % (min_groups, self.n_splits)), Warning)\n",
      "[Parallel(n_jobs=-1)]: Done 300 out of 300 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=7,\n",
       "             max_features=17, max_leaf_nodes=None,\n",
       "             min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "             min_samples_leaf=1, min_samples_split=2,\n",
       "             min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "             splitter='best'), 0.4583333333333333)"
      ]
     },
     "execution_count": 19,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "tree= DecisionTreeClassifier()\n",
    "\n",
    "tree_params = {'max_depth':range(1,11),'max_features':range(4,19)}\n",
    "tree_grid = GridSearchCV(tree,tree_params,cv=skf,n_jobs=-1,verbose=True)\n",
    "tree_grid.fit(X_train_last,y_train)\n",
    "tree_grid.best_estimator_ , tree_grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "G72vFULDMobr",
    "outputId": "8f20e1fe-3149-4f43-9601-51ca19538027"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "retrying ...1secs\n",
      "retrying ...1secs\n",
      "retrying ...7secs\n",
      "retrying ...1secs\n",
      "retrying ...7secs\n",
      "retrying ...1secs\n"
     ]
    }
   ],
   "source": [
    "rec_tracks=[]\n",
    "\n",
    "for i in playlist_df['id'].values.tolist():\n",
    "  rec_tracks += sp.recommendations(seed_tracks=[i],limit = int(len(playlist_df)/2))['tracks'];\n",
    "  \n",
    "\n",
    "rec_track_ids=[]\n",
    "rec_track_names = []\n",
    "for i in rec_tracks:\n",
    "  rec_track_ids.append(i['id'])\n",
    "  rec_track_names.append(i['name'])\n",
    "  \n",
    "\n",
    "rec_features=[]\n",
    "\n",
    "for i in range(0,len(rec_track_ids)):\n",
    "  rec_audio_features = sp.audio_features(rec_track_ids[i])\n",
    "  for track in rec_audio_features:\n",
    "    rec_features.append(track)\n",
    "    \n",
    "    \n",
    "rec_playlist_df= pd.DataFrame(rec_features,index = rec_track_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nb6EFMlUTuZ1"
   },
   "outputs": [],
   "source": [
    "rec_playlist_df = rec_playlist_df[[\"acousticness\", \"danceability\", \"duration_ms\", \"energy\", \"instrumentalness\",  \"key\", \"liveness\", \"loudness\", \"mode\", \"speechiness\", \"tempo\", \"valence\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fsL5Vw46UVT0"
   },
   "outputs": [],
   "source": [
    "tree_grid.best_estimator_.fit(X_train_last,y_train)\n",
    "rec_playlist_df_scaled = StandardScaler().fit_transform(rec_playlist_df)\n",
    "X_test_pca  = pca.transform(rec_playlist_df_scaled)\n",
    "X_test_names = v.transform(rec_track_names)\n",
    "X_test_last = csr_matrix(hstack([X_test_pca,X_test_names]))\n",
    "y_pred_class = tree_grid.best_estimator_.predict(X_test_last)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "N4TAkd8WWPUa"
   },
   "outputs": [],
   "source": [
    "rec_playlist_df['ratings']=y_pred_class\n",
    "rec_playlist_df = rec_playlist_df.sort_values('ratings',ascending=False)\n",
    "rec_playlist_df = rec_playlist_df.reset_index()\n",
    "\n",
    "recs_to_add = rec_playlist_df[rec_playlist_df['ratings']>=9]['index'].values.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LaUCBoe8XOS_"
   },
   "outputs": [],
   "source": [
    "playlist_recs =sp.user_playlist_create(username,name='PCA + tf-idf + DT - Recommended Songs for Playlist - {}'.format(sourcePlaylist['name']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 459
    },
    "colab_type": "code",
    "id": "eauEBKtPdiJw",
    "outputId": "be5c6081-501b-4121-e1ae-073cc129e772"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'collaborative': False,\n",
       " 'description': None,\n",
       " 'external_urls': {'spotify': 'https://open.spotify.com/playlist/4D8p3hVYwgngTmf1FHol0Y'},\n",
       " 'followers': {'href': None, 'total': 0},\n",
       " 'href': 'https://api.spotify.com/v1/playlists/4D8p3hVYwgngTmf1FHol0Y',\n",
       " 'id': '4D8p3hVYwgngTmf1FHol0Y',\n",
       " 'images': [],\n",
       " 'name': 'PCA + tf-idf + DT - Recommended Songs for Playlist - bantai',\n",
       " 'owner': {'display_name': 'francocasadei',\n",
       "  'external_urls': {'spotify': 'https://open.spotify.com/user/francocasadei'},\n",
       "  'href': 'https://api.spotify.com/v1/users/francocasadei',\n",
       "  'id': 'francocasadei',\n",
       "  'type': 'user',\n",
       "  'uri': 'spotify:user:francocasadei'},\n",
       " 'primary_color': None,\n",
       " 'public': True,\n",
       " 'snapshot_id': 'MSxlNTJiYzUxNmUyNTU4ODMyYThkYjMwMWVmMjVjODg5OTYyMWE1N2Qz',\n",
       " 'tracks': {'href': 'https://api.spotify.com/v1/playlists/4D8p3hVYwgngTmf1FHol0Y/tracks',\n",
       "  'items': [],\n",
       "  'limit': 100,\n",
       "  'next': None,\n",
       "  'offset': 0,\n",
       "  'previous': None,\n",
       "  'total': 0},\n",
       " 'type': 'playlist',\n",
       " 'uri': 'spotify:user:francocasadei:playlist:4D8p3hVYwgngTmf1FHol0Y'}"
      ]
     },
     "execution_count": 25,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "playlist_recs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Pjp8b-vWXibE"
   },
   "outputs": [],
   "source": [
    "sp.user_playlist_add_tracks(username,playlist_recs['id'],recs_to_add);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "07ieGpc5ZTai"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "include_colab_link": true,
   "name": "Spotify Recommendation System .ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
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   "file_extension": ".py",
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   "name": "python",
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   "pygments_lexer": "ipython3",
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