Beginner_Level__Task_3__Music_Recommendation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": " Beginner Level _Task 3 _Music Recommendation.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Devikasarwade/LGM_DATA-SCIENCE/blob/main/Beginner_Level__Task_3__Music_Recommendation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-1hNtzJDQD-S"
      },
      "source": [
        "\n",
        "# **LetsGrowMore- Virtual Internship Program (VIP)- Data Science**\n",
        "\n",
        "**Beginner Level Task**\n",
        "\n",
        "Name:- Devika Hanumant Sarwade\n",
        "\n",
        "Task 3 :- Music Recommendation"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "E7CroEhhZq5L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VE18Dr6wgguL"
      },
      "source": [
        "**Import the required libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4sqo-tE6R7RR"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxqY6xRag43S"
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cy4vC-SvhGAw"
      },
      "source": [
        "**Load the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w3qbJ7RFhNbw"
      },
      "source": [
        "song_data = pd.read_csv('/content/songs.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "REnxm1hLhtpq",
        "outputId": "a481bdc6-0f5d-4301-fdad-6c8cc5e3071f"
      },
      "source": [
        "song_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>song_id</th>\n",
              "      <th>title</th>\n",
              "      <th>release</th>\n",
              "      <th>artist_name</th>\n",
              "      <th>year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SOQMMHC12AB0180CB8</td>\n",
              "      <td>Silent Night</td>\n",
              "      <td>Monster Ballads X-Mas</td>\n",
              "      <td>Faster Pussy cat</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>SOVFVAK12A8C1350D9</td>\n",
              "      <td>Tanssi vaan</td>\n",
              "      <td>Karkuteillä</td>\n",
              "      <td>Karkkiautomaatti</td>\n",
              "      <td>1995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>SOGTUKN12AB017F4F1</td>\n",
              "      <td>No One Could Ever</td>\n",
              "      <td>Butter</td>\n",
              "      <td>Hudson Mohawke</td>\n",
              "      <td>2006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>SOBNYVR12A8C13558C</td>\n",
              "      <td>Si Vos Querés</td>\n",
              "      <td>De Culo</td>\n",
              "      <td>Yerba Brava</td>\n",
              "      <td>2003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>SOHSBXH12A8C13B0DF</td>\n",
              "      <td>Tangle Of Aspens</td>\n",
              "      <td>Rene Ablaze Presents Winter Sessions</td>\n",
              "      <td>Der Mystic</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              song_id              title  ...       artist_name  year\n",
              "0  SOQMMHC12AB0180CB8       Silent Night  ...  Faster Pussy cat  2003\n",
              "1  SOVFVAK12A8C1350D9        Tanssi vaan  ...  Karkkiautomaatti  1995\n",
              "2  SOGTUKN12AB017F4F1  No One Could Ever  ...    Hudson Mohawke  2006\n",
              "3  SOBNYVR12A8C13558C      Si Vos Querés  ...       Yerba Brava  2003\n",
              "4  SOHSBXH12A8C13B0DF   Tangle Of Aspens  ...        Der Mystic     0\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRZwPHwqhzsq"
      },
      "source": [
        "user_data = pd.read_csv('/content/users.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "UMvJBRz1h7Tw",
        "outputId": "cc8d8247-9c5a-44be-86c8-819569b9c6dd"
      },
      "source": [
        "user_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>user_id</th>\n",
              "      <th>song_id</th>\n",
              "      <th>listen_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOAKIMP12A8C130995</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBBMDR12A8C13253B</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBXHDL12A81C204C0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBYHAJ12A6701BF1D</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SODACBL12A8C13C273</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                    user_id             song_id  listen_count\n",
              "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOAKIMP12A8C130995             1\n",
              "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBBMDR12A8C13253B             2\n",
              "2  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBXHDL12A81C204C0             1\n",
              "3  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SOBYHAJ12A6701BF1D             1\n",
              "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  SODACBL12A8C13C273             1"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u4_H1r2ZiRMl"
      },
      "source": [
        "**Pre-process the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cuzVwivRNUY"
      },
      "source": [
        "song_data[\"year\"] = song_data[\"year\"].astype('Int64')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3iRW843mNcX"
      },
      "source": [
        "song_data.rename(columns={\"song_id\":\"SongId\",\"title\":\"Title\",\"release\":\"Album\",\"artist_name\":\"Artist\",\"year\":\"Year\"},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FLhAnlKfjxO2"
      },
      "source": [
        "user_data[\"listen_count\"] = user_data[\"listen_count\"].astype('Int64')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-k4RLH0Im0gU"
      },
      "source": [
        "user_data.rename(columns={\"user_id\":\"UserId\",\"song_id\":\"SongId\",\"listen_count\":\"ListenCount\"},inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SbtTW6OtoS7H"
      },
      "source": [
        "**Merge the datasets**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "wT1CqFD5oWsv",
        "outputId": "39f7bfd2-6efe-432b-d306-adde1d2c4b0f"
      },
      "source": [
        "final_data = pd.merge(user_data, song_data.drop_duplicates([\"SongId\"]), on='SongId', how='left')\n",
        "final_data['Song'] = final_data['Title'] + ' by ' + final_data['Artist']\n",
        "final_data = final_data.drop(['Title'],axis=1)\n",
        "final_data = final_data.head(50000)\n",
        "final_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>UserId</th>\n",
              "      <th>SongId</th>\n",
              "      <th>ListenCount</th>\n",
              "      <th>Album</th>\n",
              "      <th>Artist</th>\n",
              "      <th>Year</th>\n",
              "      <th>Song</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOAKIMP12A8C130995</td>\n",
              "      <td>1</td>\n",
              "      <td>Thicker Than Water</td>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>0</td>\n",
              "      <td>The Cove by Jack Johnson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBBMDR12A8C13253B</td>\n",
              "      <td>2</td>\n",
              "      <td>Flamenco Para Niños</td>\n",
              "      <td>Paco De Lucia</td>\n",
              "      <td>1976</td>\n",
              "      <td>Entre Dos Aguas by Paco De Lucia</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBXHDL12A81C204C0</td>\n",
              "      <td>1</td>\n",
              "      <td>Graduation</td>\n",
              "      <td>Kanye West</td>\n",
              "      <td>2007</td>\n",
              "      <td>Stronger by Kanye West</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SOBYHAJ12A6701BF1D</td>\n",
              "      <td>1</td>\n",
              "      <td>In Between Dreams</td>\n",
              "      <td>Jack Johnson</td>\n",
              "      <td>2005</td>\n",
              "      <td>Constellations by Jack Johnson</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>b80344d063b5ccb3212f76538f3d9e43d87dca9e</td>\n",
              "      <td>SODACBL12A8C13C273</td>\n",
              "      <td>1</td>\n",
              "      <td>There Is Nothing Left To Lose</td>\n",
              "      <td>Foo Fighters</td>\n",
              "      <td>1999</td>\n",
              "      <td>Learn To Fly by Foo Fighters</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                     UserId  ...                              Song\n",
              "0  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...          The Cove by Jack Johnson\n",
              "1  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...  Entre Dos Aguas by Paco De Lucia\n",
              "2  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...            Stronger by Kanye West\n",
              "3  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...    Constellations by Jack Johnson\n",
              "4  b80344d063b5ccb3212f76538f3d9e43d87dca9e  ...      Learn To Fly by Foo Fighters\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zqq4pJidrAe-"
      },
      "source": [
        "**Visualize the data**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "id": "wmL8UMA2rEVL",
        "outputId": "bd17da11-49b4-498c-cf3a-141368c06045"
      },
      "source": [
        "#Using countplot to see the number of songs per year\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.countplot(x='Year', data=final_data[-(final_data['Year']==0)])\n",
        "plt.xticks(rotation=90)\n",
        "plt.title(\"No. of songs per year\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "Iiy_VUs9tOJX",
        "outputId": "a6daaaa7-5ea7-4fd0-ec1b-8290354d548f"
      },
      "source": [
        "#Using barplot to see the number of songs per artist\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.barplot(final_data['Artist'].value_counts()[:10].index,final_data['Artist'].value_counts()[:10].values)\n",
        "plt.title(\"No. of songs per artist\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "id": "IAHdNrHluJI8",
        "outputId": "c893a627-a9c3-4597-acc5-5403489609c4"
      },
      "source": [
        "#Using barplot to see the most popular songs\n",
        "plt.figure(figsize=(20,10))\n",
        "sns.barplot(final_data['Song'].value_counts()[:10].values,final_data['Song'].value_counts()[:10].index)\n",
        "plt.title(\"Most popular songs\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PlViwAW4vAri"
      },
      "source": [
        "**Build the recommendation system**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UusMS2kFvFOT"
      },
      "source": [
        "class Recommendation():\n",
        "    def __init__(self, data, user_id, song):\n",
        "        self.data = data\n",
        "        self.user_id = user_id\n",
        "        self.song = song\n",
        "        self.glcm = None\n",
        "        \n",
        "    def song_history(self, user):\n",
        "        user_data = self.data[self.data[self.user_id] == user]\n",
        "        return list(user_data[self.song].unique())\n",
        "        \n",
        "    def users(self, item):\n",
        "        item_data = self.data[self.data[self.song] == item]\n",
        "        return set(item_data[self.user_id].unique())\n",
        "        \n",
        "    def all_songs(self):\n",
        "        return list(self.data[self.song].unique())\n",
        "        \n",
        "    def get_glcm(self, user_songs, all_songs):\n",
        "        users = []        \n",
        "        for p in range(0, len(user_songs)):\n",
        "            users.append(self.users(user_songs[p]))   \n",
        "        glcm = np.matrix(np.zeros(shape=(len(user_songs), len(all_songs))), float)\n",
        "\n",
        "        for p in range(0,len(all_songs)):\n",
        "            songs_p_data = self.data[self.data[self.song] == all_songs[p]]\n",
        "            users_p = set(songs_p_data[self.user_id].unique())\n",
        "            \n",
        "            for q in range(0,len(user_songs)):           \n",
        "                users_q = users[q]\n",
        "                users_intersection = users_p.intersection(users_q)\n",
        "               \n",
        "                users_union = users_p.union(users_q)\n",
        "                glcm[q,p] = float(len(users_intersection))/float(len(users_union))\n",
        "\n",
        "        return glcm\n",
        "\n",
        "    def generate(self, user, glcm, all_songs, user_songs):\n",
        "        scores = glcm.sum(axis=0)/float(glcm.shape[0])\n",
        "        scores = np.array(scores)[0].tolist()\n",
        "        sort_index = sorted(((e,p) for p,e in enumerate(list(scores))), reverse=True)\n",
        "        columns = ['UserID', 'Song', 'Score', 'Rank']\n",
        "        final_data = pd.DataFrame(columns=columns)\n",
        "         \n",
        "        rank = 1 \n",
        "        for i in range(0,len(sort_index)):\n",
        "            if ~np.isnan(sort_index[i][0]) and all_songs[sort_index[i][1]] not in user_songs and rank <= 10:\n",
        "                final_data.loc[len(final_data)]=[user,all_songs[sort_index[i][1]],sort_index[i][0],rank]\n",
        "                rank = rank+1\n",
        "        \n",
        "        print(\"Music Recommendations: \\n\")\n",
        "        return final_data.drop(['UserID'], axis=1)\n",
        "\n",
        "    def get_recommendations(self, user):\n",
        "        user_songs = self.song_history(user)    \n",
        "        all_songs = self.all_songs()\n",
        "        glcm = self.get_glcm(user_songs, all_songs)\n",
        "        return self.generate(user, glcm, all_songs, user_songs)\n",
        "\n",
        "    def get_similar_songs(self, item_list):\n",
        "        user_songs = item_list\n",
        "        all_songs = self.all_songs()\n",
        "        glcm = self.get_glcm(user_songs, all_songs)\n",
        "        return self.generate(\"\", glcm, all_songs, user_songs)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KLz_muyAxQ8L"
      },
      "source": [
        "**Get song history for a user**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTGmZnUmxWbi"
      },
      "source": [
        "r = Recommendation(final_data,'UserId','Song')\n",
        "history=r.song_history(final_data['UserId'][6])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2LDQQxuVx4yY",
        "outputId": "f3898b0e-9327-4bd1-ea9c-9359d369d88b"
      },
      "source": [
        "print(\"Song history of the user:\\n\")\n",
        "for song in history:\n",
        "  print(song)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Song history of the user:\n",
            "\n",
            "The Cove by Jack Johnson\n",
            "Entre Dos Aguas by Paco De Lucia\n",
            "Stronger by Kanye West\n",
            "Constellations by Jack Johnson\n",
            "Learn To Fly by Foo Fighters\n",
            "Apuesta Por El Rock 'N' Roll by Héroes del Silencio\n",
            "Paper Gangsta by Lady GaGa\n",
            "Stacked Actors by Foo Fighters\n",
            "Sehr kosmisch by Harmonia\n",
            "Heaven's gonna burn your eyes by Thievery Corporation feat. Emiliana Torrini\n",
            "Let It Be Sung by Jack Johnson / Matt Costa / Zach Gill / Dan Lebowitz / Steve Adams\n",
            "I'll Be Missing You (Featuring Faith Evans & 112)(Album Version) by Puff Daddy\n",
            "Love Shack by The B-52's\n",
            "Clarity by John Mayer\n",
            "I?'m A Steady Rollin? Man by Robert Johnson\n",
            "The Old Saloon by The Lonely Island\n",
            "Behind The Sea [Live In Chicago] by Panic At The Disco\n",
            "Champion by Kanye West\n",
            "Breakout by Foo Fighters\n",
            "Ragged Wood by Fleet Foxes\n",
            "Mykonos by Fleet Foxes\n",
            "Country Road by Jack Johnson / Paula Fuga\n",
            "Oh No by Andrew Bird\n",
            "Love Song For No One by John Mayer\n",
            "Jewels And Gold by Angus & Julia Stone\n",
            "Warning by Incubus\n",
            "83 by John Mayer\n",
            "Neon by John Mayer\n",
            "The Middle by Jimmy Eat World\n",
            "High and dry by Jorge Drexler\n",
            "All That We Perceive by Thievery Corporation\n",
            "The Christmas Song  (LP Version) by King Curtis\n",
            "Our Swords (Soundtrack Version) by Band Of Horses\n",
            "Are You In? by Incubus\n",
            "Drive by Incubus\n",
            "Generator by Foo Fighters\n",
            "Come Back To Bed by John Mayer\n",
            "He Doesn't Know Why by Fleet Foxes\n",
            "Trani by Kings Of Leon\n",
            "Bigger Isn't Better by The String Cheese Incident\n",
            "Sun Giant by Fleet Foxes\n",
            "City Love by John Mayer\n",
            "Right Back by Sublime\n",
            "Moonshine by Jack Johnson\n",
            "Holes To Heaven by Jack Johnson\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HLLYfdCnybOR"
      },
      "source": [
        "**Get recommendations**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "id": "JyUIKZNdyfGa",
        "outputId": "ed686c4c-45e3-4b0a-e5da-c372ed54baf1"
      },
      "source": [
        "r.get_recommendations(final_data['UserId'][6])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Music Recommendations: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Song</th>\n",
              "      <th>Score</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Questions by Jack Johnson</td>\n",
              "      <td>0.037157</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Great Indoors by John Mayer</td>\n",
              "      <td>0.036953</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Wrong Turn by Jack Johnson</td>\n",
              "      <td>0.036740</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Ghost Dream by Hymie's Basement</td>\n",
              "      <td>0.036463</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Baby I Want You by Amos Lee</td>\n",
              "      <td>0.036244</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Better That We Break by Maroon 5</td>\n",
              "      <td>0.035016</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>The Sharing Song by Jack Johnson</td>\n",
              "      <td>0.034193</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Better Days by Amos Lee</td>\n",
              "      <td>0.034193</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Dreamin' by Amos Lee</td>\n",
              "      <td>0.034193</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Mr.Curiosity  (Album Version) by Jason Mraz</td>\n",
              "      <td>0.034193</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                          Song     Score Rank\n",
              "0                    Questions by Jack Johnson  0.037157    1\n",
              "1                  Great Indoors by John Mayer  0.036953    2\n",
              "2                   Wrong Turn by Jack Johnson  0.036740    3\n",
              "3              Ghost Dream by Hymie's Basement  0.036463    4\n",
              "4                  Baby I Want You by Amos Lee  0.036244    5\n",
              "5             Better That We Break by Maroon 5  0.035016    6\n",
              "6             The Sharing Song by Jack Johnson  0.034193    7\n",
              "7                      Better Days by Amos Lee  0.034193    8\n",
              "8                         Dreamin' by Amos Lee  0.034193    9\n",
              "9  Mr.Curiosity  (Album Version) by Jason Mraz  0.034193   10"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOKre2sr0Q2r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 393
        },
        "outputId": "d5f55b09-193e-45fe-ce0a-fbdfa2ed78a8"
      },
      "source": [
        "r.get_similar_songs(['The Cove by Jack Johnson'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Music Recommendations: \n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "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>Song</th>\n",
              "      <th>Score</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Moonshine by Jack Johnson</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Baby I Want You by Amos Lee</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Country Road by Jack Johnson / Paula Fuga</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Questions by Jack Johnson</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Wrong Turn by Jack Johnson</td>\n",
              "      <td>0.285714</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Holes To Heaven by Jack Johnson</td>\n",
              "      <td>0.266667</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>What's Been Going On by Amos Lee</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ghost Dream by Hymie's Basement</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Painting by Chagall by The Weepies</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Mrs Thompson by The Kooks</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Song     Score Rank\n",
              "0                  Moonshine by Jack Johnson  0.400000    1\n",
              "1                Baby I Want You by Amos Lee  0.333333    2\n",
              "2  Country Road by Jack Johnson / Paula Fuga  0.333333    3\n",
              "3                  Questions by Jack Johnson  0.285714    4\n",
              "4                 Wrong Turn by Jack Johnson  0.285714    5\n",
              "5            Holes To Heaven by Jack Johnson  0.266667    6\n",
              "6           What's Been Going On by Amos Lee  0.250000    7\n",
              "7            Ghost Dream by Hymie's Basement  0.250000    8\n",
              "8         Painting by Chagall by The Weepies  0.250000    9\n",
              "9                  Mrs Thompson by The Kooks  0.250000   10"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    }
  ]
}