spotify-recommendation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 170653 entries, 0 to 170652\n",
      "Data columns (total 19 columns):\n",
      " #   Column            Non-Null Count   Dtype  \n",
      "---  ------            --------------   -----  \n",
      " 0   valence           170653 non-null  float64\n",
      " 1   year              170653 non-null  int64  \n",
      " 2   acousticness      170653 non-null  float64\n",
      " 3   artists           170653 non-null  object \n",
      " 4   danceability      170653 non-null  float64\n",
      " 5   duration_ms       170653 non-null  int64  \n",
      " 6   energy            170653 non-null  float64\n",
      " 7   explicit          170653 non-null  int64  \n",
      " 8   id                170653 non-null  object \n",
      " 9   instrumentalness  170653 non-null  float64\n",
      " 10  key               170653 non-null  int64  \n",
      " 11  liveness          170653 non-null  float64\n",
      " 12  loudness          170653 non-null  float64\n",
      " 13  mode              170653 non-null  int64  \n",
      " 14  name              170653 non-null  object \n",
      " 15  popularity        170653 non-null  int64  \n",
      " 16  release_date      170653 non-null  object \n",
      " 17  speechiness       170653 non-null  float64\n",
      " 18  tempo             170653 non-null  float64\n",
      "dtypes: float64(9), int64(6), object(4)\n",
      "memory usage: 24.7+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2973 entries, 0 to 2972\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   mode              2973 non-null   int64  \n",
      " 1   genres            2973 non-null   object \n",
      " 2   acousticness      2973 non-null   float64\n",
      " 3   danceability      2973 non-null   float64\n",
      " 4   duration_ms       2973 non-null   float64\n",
      " 5   energy            2973 non-null   float64\n",
      " 6   instrumentalness  2973 non-null   float64\n",
      " 7   liveness          2973 non-null   float64\n",
      " 8   loudness          2973 non-null   float64\n",
      " 9   speechiness       2973 non-null   float64\n",
      " 10  tempo             2973 non-null   float64\n",
      " 11  valence           2973 non-null   float64\n",
      " 12  popularity        2973 non-null   float64\n",
      " 13  key               2973 non-null   int64  \n",
      "dtypes: float64(11), int64(2), object(1)\n",
      "memory usage: 325.3+ KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(genre_data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 14 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   mode              100 non-null    int64  \n",
      " 1   year              100 non-null    int64  \n",
      " 2   acousticness      100 non-null    float64\n",
      " 3   danceability      100 non-null    float64\n",
      " 4   duration_ms       100 non-null    float64\n",
      " 5   energy            100 non-null    float64\n",
      " 6   instrumentalness  100 non-null    float64\n",
      " 7   liveness          100 non-null    float64\n",
      " 8   loudness          100 non-null    float64\n",
      " 9   speechiness       100 non-null    float64\n",
      " 10  tempo             100 non-null    float64\n",
      " 11  valence           100 non-null    float64\n",
      " 12  popularity        100 non-null    float64\n",
      " 13  key               100 non-null    int64  \n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 11.1 KB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(year_data.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Features correlation with dependent variable'}, xlabel='Pearson Correlation'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from yellowbrick.target import FeatureCorrelation\n",
    "\n",
    "feature_names = ['acousticness', 'danceability', 'energy', 'instrumentalness',\n",
    "       'liveness', 'loudness', 'speechiness', 'tempo', 'valence','duration_ms','explicit','key','mode','year']\n",
    "\n",
    "X, y = data[feature_names], data['popularity']\n",
    "\n",
    "# Create a list of the feature names\n",
    "features = np.array(feature_names)\n",
    "\n",
    "# Instantiate the visualizer\n",
    "visualizer = FeatureCorrelation(labels=features)\n",
    "\n",
    "plt.rcParams['figure.figsize']=(20,20)\n",
    "visualizer.fit(X, y)     # Fit the data to the visualizer\n",
    "visualizer.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='decade', ylabel='count'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 792x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_decade(year):\n",
    "    period_start = int(year/10) * 10\n",
    "    decade = '{}s'.format(period_start)\n",
    "    return decade\n",
    "\n",
    "data['decade'] = data['year'].apply(get_decade)\n",
    "\n",
    "sns.set(rc={'figure.figsize':(11 ,6)})\n",
    "sns.countplot(data['decade'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "variable=acousticness<br>year=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "acousticness",
         "line": {
          "color": "#636efa",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "acousticness",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
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         "yaxis": "y"
        },
        {
         "hovertemplate": "variable=danceability<br>year=%{x}<br>value=%{y}<extra></extra>",
         "legendgroup": "danceability",
         "line": {
          "color": "#EF553B",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "danceability",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
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   "source": [
    "sound_features = ['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'valence']\n",
    "fig = px.line(year_data, x='year', y=sound_features)\n",
    "fig.show()"
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   "metadata": {},
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   "source": [
    "fig = px.line(year_data, x='year', y=sound_features)\n"
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