FinalProject_group050.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "  - Brianna Lehane\n",
    "       - Written Descriptions, Data Analysis\n",
    "  - Alexa Hurtado\n",
    "       - Data Cleaning, Data Analysis\n",
    "  - Karam Singh\n",
    "       - EDA, Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "This study aims to prove and analyze the powerful effect music has on emotion. There are a myriad of musical genres that express different variations of emotions. Some of these genres’ names directly reflect the kind of emotion they look to evoke or express in their music, such as Blues or Soul. Since 1894, music of all different genres have been ranked on the Billboard Top 100 charts based on its popularity; this ranking takes into account sales and how widely listened to the song is (Molanphy, 2013). But what components of a song make this song popular? What emotions are people most widely looking to evoke when listening to music? Using a Spotify data set, we set to investigate the relationship between genre, bpm, danceability, loudness and valence in a song. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Background\n",
    "\n",
    "Music and cognition have a historically important relationship. According to an Oxford University paper titled Music and emotion: theory and research, “It is an ancient, and very pervasive, idea that music expresses emotion” (Scherer and Zentner, 361). Music is powerful for many reasons. Music can affect a person’s productivity, memory, and stress levels (Harvard, 2011). One of music’s most impactful abilities is its effect on human emotion. The sound waves music produces sends vibrations through the ear, eardrum, and all the way through to the hair cells, which, in turn, release chemical neurotransmitters that activate the auditory nerve, sending miniature electric currents to the auditory cortex in the temporal lobe of the brain (Harvard, 2011). With more ways to listen to music today than ever, music listening is becoming more popular. In fact, the music industry gained more than $8 billion dollars in 2017 alone (Christman, 2018).\n",
    "\n",
    "There are hundreds of different genres of music, some specifically targeting certain emotions, such as Blues or Soul. Of all these emotion-evoking genres, what makes music popular? There are a number of patterns in recent popular songs, such as having a female artist, being up-tempo, and having a well-known artist (Interiano et al, 2018). In fact, the general framework of contemporary popular music has such a common pattern that there has been a term created for these catchy tunes; earworms. \n",
    "\n",
    "As we have stated, data has shown that popular contemporary music follows some kind of pattern. But is there a pattern in a specific mood or emotion that can be tracked in popular music? By studying the most popular moods music listeners are seeking, we hope our data analysis can provide insight to musicians and producers looking to understand listener habits as they try to find success in this saturated industry. We also hope our date can inform music listeners about what emotional components of a song makes it so darn catchy!\n",
    "\n",
    "References:\n",
    "Harvard, 2011: https://www.health.harvard.edu/staying-healthy/music-and-health\n",
    "Interiano et al, 2018: https://royalsocietypublishing.org/doi/full/10.1098/rsos.171274\n",
    "Zentner, Scherer, 2008: https://www.ncbi.nlm.nih.gov/pubmed/18729581\n",
    "Christman, 2018: https://www.billboard.com/articles/business/8257558/us-music-industry-2017-highest-revenue-in-decade-fueled-paid-subscriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question\n",
    "\n",
    "Based on our data set, we aim to analyze if the Spotify music values our research team has characterized as ‘fun’ correlate with the popularity of a song. Using the parameters valence, bpm, danceability and how does the perceived ‘fun-ness’ of a song reflect how popular it is between 2008 and 2019? Can ‘fun-ness’ predict a hit song? In other words, can we predict the popularity of a song based on these ‘fun’ features?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n",
    "\n",
    "We hypothesize that an increase across five parameters we have deemed to quantify fun (bpm, loudness, danceability, valence, and energy) will increase the popularity of the song. We will be finding this by also hypothesizing that when running a multiple linear regression model on these parameters we will be able to successfully predict the popularity of that song. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset\n",
    "\n",
    "We used a Spotify API dataset. \n",
    "\n",
    "In this set, Spotify created quantifiable parameters to measure descriptive characteristics. These measures include valence, “the musical positiveness conveyed in a track,” danceability, “how suitable a track is for dancing based on a combination of musical elements including tempo, rhythm stability, beat strength, and overall regularity”, and energy, which “...represents a perceptual measure of intensity and activity” (Spotify for Developers). Seeing as songs can be so many things, our study, using Spotify’s data characteristics, aims to study the relationship between how ‘fun’ (eliciting happiness, joy, excitement) is and the song’s popularity. \n",
    "\n",
    "Dataset:\n",
    "https://www.kaggle.com/leonardopena/top-spotify-songs-from-20102019-by-year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by importing all the necessary libraries and packages needed to complete our data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from sklearn import linear_model\n",
    "import patsy\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#We begin by importing the file into a pandas Dataframe\n",
    "Main_FilePath = \"Downloads/top-spotify-songs-from-20102019-by-year/top10s.csv\"\n",
    "Main_Music_Dataset = pd.read_csv(Main_FilePath, encoding = \"ISO-8859-1\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>artist</th>\n",
       "      <th>top genre</th>\n",
       "      <th>year</th>\n",
       "      <th>bpm</th>\n",
       "      <th>nrgy</th>\n",
       "      <th>dnce</th>\n",
       "      <th>dB</th>\n",
       "      <th>live</th>\n",
       "      <th>val</th>\n",
       "      <th>dur</th>\n",
       "      <th>acous</th>\n",
       "      <th>spch</th>\n",
       "      <th>pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Hey, Soul Sister</td>\n",
       "      <td>Train</td>\n",
       "      <td>neo mellow</td>\n",
       "      <td>2010</td>\n",
       "      <td>97</td>\n",
       "      <td>89</td>\n",
       "      <td>67</td>\n",
       "      <td>-4</td>\n",
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       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Love The Way You Lie</td>\n",
       "      <td>Eminem</td>\n",
       "      <td>detroit hip hop</td>\n",
       "      <td>2010</td>\n",
       "      <td>87</td>\n",
       "      <td>93</td>\n",
       "      <td>75</td>\n",
       "      <td>-5</td>\n",
       "      <td>52</td>\n",
       "      <td>64</td>\n",
       "      <td>263</td>\n",
       "      <td>24</td>\n",
       "      <td>23</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>TiK ToK</td>\n",
       "      <td>Kesha</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>120</td>\n",
       "      <td>84</td>\n",
       "      <td>76</td>\n",
       "      <td>-3</td>\n",
       "      <td>29</td>\n",
       "      <td>71</td>\n",
       "      <td>200</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bad Romance</td>\n",
       "      <td>Lady Gaga</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>119</td>\n",
       "      <td>92</td>\n",
       "      <td>70</td>\n",
       "      <td>-4</td>\n",
       "      <td>8</td>\n",
       "      <td>71</td>\n",
       "      <td>295</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Just the Way You Are</td>\n",
       "      <td>Bruno Mars</td>\n",
       "      <td>pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>109</td>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "      <td>-5</td>\n",
       "      <td>9</td>\n",
       "      <td>43</td>\n",
       "      <td>221</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>78</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0                 title      artist        top genre  year  bpm  \\\n",
       "0           1      Hey, Soul Sister       Train       neo mellow  2010   97   \n",
       "1           2  Love The Way You Lie      Eminem  detroit hip hop  2010   87   \n",
       "2           3               TiK ToK       Kesha        dance pop  2010  120   \n",
       "3           4           Bad Romance   Lady Gaga        dance pop  2010  119   \n",
       "4           5  Just the Way You Are  Bruno Mars              pop  2010  109   \n",
       "\n",
       "   nrgy  dnce  dB  live  val  dur  acous  spch  pop  \n",
       "0    89    67  -4     8   80  217     19     4   83  \n",
       "1    93    75  -5    52   64  263     24    23   82  \n",
       "2    84    76  -3    29   71  200     10    14   80  \n",
       "3    92    70  -4     8   71  295      0     4   79  \n",
       "4    84    64  -5     9   43  221      2     4   78  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Main_Music_Dataset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning\n",
    "\n",
    "For the cleaning process, we start by removing all columns in our dataframe that were not applicable to determining the ‘funness’ of the song. We kept data columns; bpm, loudness, danceability, valence. We also eliminated any initial outliers, including genres with fewer than 40 songs. Since we are using a Spotify dataset, we decided it would only make sense to analyze the popularity of songs that have been released after the application launched in 2008. The timespan of analysis is 2010-2019. The data will be ordered by year of release. To set up our data, we ordered data by year. We separated and created sub-data sets based on the year. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Initialize a new Dataframe where we will clean and wrangle data\n",
    "#Here we are dropping all columns we will not be using in our analysis\n",
    "\n",
    "Cleaned_Dataset = Main_Music_Dataset.drop(labels = [\"live\", \"dur\", \"acous\", \"spch\"], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#renaming columns so there's more clarity in what they mean\n",
    "\n",
    "Cleaned_Dataset.columns = [\"Index\", \"Title\", \"Artist\", \"Genre\", \"Year\", \"BPM\", \"Energy\", \"Dance\", \"Loudness\", \"Valence\", \"Popularity\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Hey, Soul Sister</td>\n",
       "      <td>Train</td>\n",
       "      <td>neo mellow</td>\n",
       "      <td>2010</td>\n",
       "      <td>97</td>\n",
       "      <td>89</td>\n",
       "      <td>67</td>\n",
       "      <td>-4</td>\n",
       "      <td>80</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Love The Way You Lie</td>\n",
       "      <td>Eminem</td>\n",
       "      <td>detroit hip hop</td>\n",
       "      <td>2010</td>\n",
       "      <td>87</td>\n",
       "      <td>93</td>\n",
       "      <td>75</td>\n",
       "      <td>-5</td>\n",
       "      <td>64</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>TiK ToK</td>\n",
       "      <td>Kesha</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>120</td>\n",
       "      <td>84</td>\n",
       "      <td>76</td>\n",
       "      <td>-3</td>\n",
       "      <td>71</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Bad Romance</td>\n",
       "      <td>Lady Gaga</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>119</td>\n",
       "      <td>92</td>\n",
       "      <td>70</td>\n",
       "      <td>-4</td>\n",
       "      <td>71</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Just the Way You Are</td>\n",
       "      <td>Bruno Mars</td>\n",
       "      <td>pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>109</td>\n",
       "      <td>84</td>\n",
       "      <td>64</td>\n",
       "      <td>-5</td>\n",
       "      <td>43</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Index                 Title      Artist            Genre  Year  BPM  \\\n",
       "0      1      Hey, Soul Sister       Train       neo mellow  2010   97   \n",
       "1      2  Love The Way You Lie      Eminem  detroit hip hop  2010   87   \n",
       "2      3               TiK ToK       Kesha        dance pop  2010  120   \n",
       "3      4           Bad Romance   Lady Gaga        dance pop  2010  119   \n",
       "4      5  Just the Way You Are  Bruno Mars              pop  2010  109   \n",
       "\n",
       "   Energy  Dance  Loudness  Valence  Popularity  \n",
       "0      89     67        -4       80          83  \n",
       "1      93     75        -5       64          82  \n",
       "2      84     76        -3       71          80  \n",
       "3      92     70        -4       71          79  \n",
       "4      84     64        -5       43          78  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cleaned_Dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sorting by year of songs so we can start to organize this \n",
    "\n",
    "Cleaned_Dataset = Cleaned_Dataset.sort_values(by = [\"Year\"], ascending = False).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>603</td>\n",
       "      <td>Kills You Slowly</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "      <td>electropop</td>\n",
       "      <td>2019</td>\n",
       "      <td>150</td>\n",
       "      <td>44</td>\n",
       "      <td>70</td>\n",
       "      <td>-9</td>\n",
       "      <td>23</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>588</td>\n",
       "      <td>I Don't Care (with Justin Bieber)</td>\n",
       "      <td>Ed Sheeran</td>\n",
       "      <td>pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>102</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>-5</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>574</td>\n",
       "      <td>Lose You To Love Me</td>\n",
       "      <td>Selena Gomez</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>102</td>\n",
       "      <td>34</td>\n",
       "      <td>51</td>\n",
       "      <td>-9</td>\n",
       "      <td>9</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>575</td>\n",
       "      <td>Someone You Loved</td>\n",
       "      <td>Lewis Capaldi</td>\n",
       "      <td>pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>110</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>-6</td>\n",
       "      <td>45</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>576</td>\n",
       "      <td>Señorita</td>\n",
       "      <td>Shawn Mendes</td>\n",
       "      <td>canadian pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>117</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "      <td>-6</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                              Title            Artist         Genre  \\\n",
       "0    603                   Kills You Slowly  The Chainsmokers    electropop   \n",
       "1    588  I Don't Care (with Justin Bieber)        Ed Sheeran           pop   \n",
       "2    574                Lose You To Love Me      Selena Gomez     dance pop   \n",
       "3    575                  Someone You Loved     Lewis Capaldi           pop   \n",
       "4    576                           Señorita      Shawn Mendes  canadian pop   \n",
       "\n",
       "   Year  BPM  Energy  Dance  Loudness  Valence  Popularity  \n",
       "0  2019  150      44     70        -9       23          67  \n",
       "1  2019  102      68     80        -5       84          85  \n",
       "2  2019  102      34     51        -9        9          97  \n",
       "3  2019  110      41     50        -6       45          96  \n",
       "4  2019  117      54     76        -6       75          95  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cleaned_Dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010    51\n",
       "2011    53\n",
       "2012    35\n",
       "2013    71\n",
       "2014    58\n",
       "2015    95\n",
       "2016    80\n",
       "2017    65\n",
       "2018    64\n",
       "2019    31\n",
       "Name: Year, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cleaned_Dataset[\"Year\"].value_counts(sort = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Separating Datasets into different individual years so we can do linear regression on each \n",
    "#individually then compare\n",
    "\n",
    "Dataset_2019 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2019]).reset_index(drop = True)\n",
    "Dataset_2018 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2018]).reset_index(drop = True)\n",
    "Dataset_2017 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2017]).reset_index(drop = True)\n",
    "Dataset_2016 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2016]).reset_index(drop = True)\n",
    "Dataset_2015 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2015]).reset_index(drop = True)\n",
    "Dataset_2014 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2014]).reset_index(drop = True)\n",
    "Dataset_2013 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2013]).reset_index(drop = True)\n",
    "Dataset_2012 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2012]).reset_index(drop = True)\n",
    "Dataset_2011 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2011]).reset_index(drop = True)\n",
    "Dataset_2010 = pd.DataFrame(Cleaned_Dataset[Cleaned_Dataset[\"Year\"] == 2010]).reset_index(drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>603</td>\n",
       "      <td>Kills You Slowly</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "      <td>electropop</td>\n",
       "      <td>2019</td>\n",
       "      <td>150</td>\n",
       "      <td>44</td>\n",
       "      <td>70</td>\n",
       "      <td>-9</td>\n",
       "      <td>23</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>588</td>\n",
       "      <td>I Don't Care (with Justin Bieber)</td>\n",
       "      <td>Ed Sheeran</td>\n",
       "      <td>pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>102</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>-5</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>574</td>\n",
       "      <td>Lose You To Love Me</td>\n",
       "      <td>Selena Gomez</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>102</td>\n",
       "      <td>34</td>\n",
       "      <td>51</td>\n",
       "      <td>-9</td>\n",
       "      <td>9</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>575</td>\n",
       "      <td>Someone You Loved</td>\n",
       "      <td>Lewis Capaldi</td>\n",
       "      <td>pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>110</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>-6</td>\n",
       "      <td>45</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>576</td>\n",
       "      <td>Señorita</td>\n",
       "      <td>Shawn Mendes</td>\n",
       "      <td>canadian pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>117</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "      <td>-6</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                              Title            Artist         Genre  \\\n",
       "0    603                   Kills You Slowly  The Chainsmokers    electropop   \n",
       "1    588  I Don't Care (with Justin Bieber)        Ed Sheeran           pop   \n",
       "2    574                Lose You To Love Me      Selena Gomez     dance pop   \n",
       "3    575                  Someone You Loved     Lewis Capaldi           pop   \n",
       "4    576                           Señorita      Shawn Mendes  canadian pop   \n",
       "\n",
       "   Year  BPM  Energy  Dance  Loudness  Valence  Popularity  \n",
       "0  2019  150      44     70        -9       23          67  \n",
       "1  2019  102      68     80        -5       84          85  \n",
       "2  2019  102      34     51        -9        9          97  \n",
       "3  2019  110      41     50        -6       45          96  \n",
       "4  2019  117      54     76        -6       75          95  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We have now separated into different years and datasets\n",
    "Dataset_2019.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Descriptive and Exploratory Data Analysis\n",
    "\n",
    "Now we want to analyze the data and find the stats for each feature. Here we are going to run some tests on our data. We will start by checking the distributions. We will start by seeing if the data is normally distributed. To run a normality test we will be using the normaltest function to test for normality of the distribution. However before doing this we should pull out the necessary data which we will be running the normality tests on. We will be running the normality tests on each column for 'fun'. \n",
    "\n",
    "This means that we will have to pull out the columns for each dataset. Below we are using bpm as an example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.767531702257407\n",
      "0.09220270124978908\n"
     ]
    }
   ],
   "source": [
    "# here we are running the normaltest on the bpm column for the 2019 Dataframe \n",
    "bpm = Dataset_2019['BPM'].values\n",
    "s_bpm = stats.normaltest(bpm)[0]\n",
    "p_bpm = stats.normaltest(bpm)[1]\n",
    "print(s_bpm)\n",
    "print(p_bpm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here because we see that the p value here is greater than the alpha value (p>0.01)\n",
    "# We accept the null hypothesis. \n",
    "# This means that the values for BPM in this dataset follows a normal distribution. We will then do the same thing for the rest\n",
    "# of the columns in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.51151802840859\n",
      "0.10479397342825336\n"
     ]
    }
   ],
   "source": [
    "Energy = Dataset_2019['Energy'].values\n",
    "Energy = Dataset_2019['Energy'].values\n",
    "s_Energy = stats.normaltest(Energy)[0]\n",
    "p_Energy = stats.normaltest(Energy)[1]\n",
    "print(s_Energy)\n",
    "print(p_Energy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#For the energy values in this Dataframe for 2019 we see that because p>0.01 we accept the null hypothesis. \n",
    "#the Energy data for 2019 does follow a normal distribution. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.54629753330108\n",
      "0.4615574411208211\n"
     ]
    }
   ],
   "source": [
    "Dance = Dataset_2019['Dance'].values\n",
    "s_Dance = stats.normaltest(Dance)[0]\n",
    "p_Dance = stats.normaltest(Dance)[1]\n",
    "print(s_Dance)\n",
    "print(p_Dance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we see that because p>0.01, the Dance values are following a normal distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6143060361135761\n",
      "0.7355380416938431\n"
     ]
    }
   ],
   "source": [
    "Loudness = Dataset_2019['Loudness'].values\n",
    "s_Loudness = stats.normaltest(Loudness)[0]\n",
    "p_Loudness = stats.normaltest(Loudness)[1]\n",
    "print(s_Loudness)\n",
    "print(p_Loudness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The loudness follows a normal distribution because the p-value is greater than 0.01 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3664824258194452\n",
      "0.8325673055319263\n"
     ]
    }
   ],
   "source": [
    "Valence = Dataset_2019['Valence'].values\n",
    "s_Valence = stats.normaltest(Valence)[0]\n",
    "p_Valence = stats.normaltest(Valence)[1]\n",
    "print(s_Valence)\n",
    "print(p_Valence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The valence follows a normal distribution as well "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7711395793869441\n",
      "0.6800630328818376\n"
     ]
    }
   ],
   "source": [
    "Popularity = Dataset_2019['Popularity'].values\n",
    "s_Popularity = stats.normaltest(Popularity)[0]\n",
    "p_Popularity = stats.normaltest(Popularity)[1]\n",
    "print(s_Popularity)\n",
    "print(p_Popularity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Here we realize that because we aren't going to be using t-tests to compare the\n",
    "#However despite this the normality tests were still a good exercise in statistics however it did not prove to be entirely \n",
    "#useful due to the fact that it doesn't make much sense to compare the variables for the different years against each \n",
    "# other. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#In the next few sections we are attempting to visualize the mean values for the variables over the last decade."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>26.0</td>\n",
       "      <td>122.058824</td>\n",
       "      <td>77.901961</td>\n",
       "      <td>64.529412</td>\n",
       "      <td>-4.901961</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>64.254902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>78.0</td>\n",
       "      <td>119.075472</td>\n",
       "      <td>74.886792</td>\n",
       "      <td>63.641509</td>\n",
       "      <td>-5.018868</td>\n",
       "      <td>53.698113</td>\n",
       "      <td>61.867925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>122.0</td>\n",
       "      <td>121.085714</td>\n",
       "      <td>75.485714</td>\n",
       "      <td>65.714286</td>\n",
       "      <td>-4.857143</td>\n",
       "      <td>64.171429</td>\n",
       "      <td>67.771429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>175.0</td>\n",
       "      <td>121.676056</td>\n",
       "      <td>73.873239</td>\n",
       "      <td>62.042254</td>\n",
       "      <td>-5.140845</td>\n",
       "      <td>53.183099</td>\n",
       "      <td>63.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>239.5</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>67.775862</td>\n",
       "      <td>62.534483</td>\n",
       "      <td>-5.775862</td>\n",
       "      <td>52.086207</td>\n",
       "      <td>62.706897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>316.0</td>\n",
       "      <td>119.768421</td>\n",
       "      <td>70.336842</td>\n",
       "      <td>63.663158</td>\n",
       "      <td>-5.621053</td>\n",
       "      <td>52.526316</td>\n",
       "      <td>64.568421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>403.5</td>\n",
       "      <td>114.325000</td>\n",
       "      <td>67.237500</td>\n",
       "      <td>63.325000</td>\n",
       "      <td>-6.712500</td>\n",
       "      <td>45.150000</td>\n",
       "      <td>64.162500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>476.0</td>\n",
       "      <td>116.800000</td>\n",
       "      <td>69.169231</td>\n",
       "      <td>65.369231</td>\n",
       "      <td>-5.615385</td>\n",
       "      <td>52.276923</td>\n",
       "      <td>69.015385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>540.5</td>\n",
       "      <td>114.593750</td>\n",
       "      <td>65.468750</td>\n",
       "      <td>67.203125</td>\n",
       "      <td>-5.671875</td>\n",
       "      <td>48.765625</td>\n",
       "      <td>72.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>588.0</td>\n",
       "      <td>112.451613</td>\n",
       "      <td>64.741935</td>\n",
       "      <td>69.709677</td>\n",
       "      <td>-5.774194</td>\n",
       "      <td>50.806452</td>\n",
       "      <td>84.354839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Index         BPM     Energy      Dance  Loudness    Valence  Popularity\n",
       "Year                                                                          \n",
       "2010   26.0  122.058824  77.901961  64.529412 -4.901961  57.000000   64.254902\n",
       "2011   78.0  119.075472  74.886792  63.641509 -5.018868  53.698113   61.867925\n",
       "2012  122.0  121.085714  75.485714  65.714286 -4.857143  64.171429   67.771429\n",
       "2013  175.0  121.676056  73.873239  62.042254 -5.140845  53.183099   63.985915\n",
       "2014  239.5  123.000000  67.775862  62.534483 -5.775862  52.086207   62.706897\n",
       "2015  316.0  119.768421  70.336842  63.663158 -5.621053  52.526316   64.568421\n",
       "2016  403.5  114.325000  67.237500  63.325000 -6.712500  45.150000   64.162500\n",
       "2017  476.0  116.800000  69.169231  65.369231 -5.615385  52.276923   69.015385\n",
       "2018  540.5  114.593750  65.468750  67.203125 -5.671875  48.765625   72.437500\n",
       "2019  588.0  112.451613  64.741935  69.709677 -5.774194  50.806452   84.354839"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Cleaned_Dataset.groupby('Year').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .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>Index</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>26.0</td>\n",
       "      <td>122.058824</td>\n",
       "      <td>77.901961</td>\n",
       "      <td>64.529412</td>\n",
       "      <td>-4.901961</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>64.254902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>78.0</td>\n",
       "      <td>119.075472</td>\n",
       "      <td>74.886792</td>\n",
       "      <td>63.641509</td>\n",
       "      <td>-5.018868</td>\n",
       "      <td>53.698113</td>\n",
       "      <td>61.867925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>122.0</td>\n",
       "      <td>121.085714</td>\n",
       "      <td>75.485714</td>\n",
       "      <td>65.714286</td>\n",
       "      <td>-4.857143</td>\n",
       "      <td>64.171429</td>\n",
       "      <td>67.771429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>175.0</td>\n",
       "      <td>121.676056</td>\n",
       "      <td>73.873239</td>\n",
       "      <td>62.042254</td>\n",
       "      <td>-5.140845</td>\n",
       "      <td>53.183099</td>\n",
       "      <td>63.985915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>239.5</td>\n",
       "      <td>123.000000</td>\n",
       "      <td>67.775862</td>\n",
       "      <td>62.534483</td>\n",
       "      <td>-5.775862</td>\n",
       "      <td>52.086207</td>\n",
       "      <td>62.706897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>316.0</td>\n",
       "      <td>119.768421</td>\n",
       "      <td>70.336842</td>\n",
       "      <td>63.663158</td>\n",
       "      <td>-5.621053</td>\n",
       "      <td>52.526316</td>\n",
       "      <td>64.568421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>403.5</td>\n",
       "      <td>114.325000</td>\n",
       "      <td>67.237500</td>\n",
       "      <td>63.325000</td>\n",
       "      <td>-6.712500</td>\n",
       "      <td>45.150000</td>\n",
       "      <td>64.162500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>476.0</td>\n",
       "      <td>116.800000</td>\n",
       "      <td>69.169231</td>\n",
       "      <td>65.369231</td>\n",
       "      <td>-5.615385</td>\n",
       "      <td>52.276923</td>\n",
       "      <td>69.015385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>540.5</td>\n",
       "      <td>114.593750</td>\n",
       "      <td>65.468750</td>\n",
       "      <td>67.203125</td>\n",
       "      <td>-5.671875</td>\n",
       "      <td>48.765625</td>\n",
       "      <td>72.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>588.0</td>\n",
       "      <td>112.451613</td>\n",
       "      <td>64.741935</td>\n",
       "      <td>69.709677</td>\n",
       "      <td>-5.774194</td>\n",
       "      <td>50.806452</td>\n",
       "      <td>84.354839</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Index         BPM     Energy      Dance  Loudness    Valence  Popularity\n",
       "Year                                                                          \n",
       "2010   26.0  122.058824  77.901961  64.529412 -4.901961  57.000000   64.254902\n",
       "2011   78.0  119.075472  74.886792  63.641509 -5.018868  53.698113   61.867925\n",
       "2012  122.0  121.085714  75.485714  65.714286 -4.857143  64.171429   67.771429\n",
       "2013  175.0  121.676056  73.873239  62.042254 -5.140845  53.183099   63.985915\n",
       "2014  239.5  123.000000  67.775862  62.534483 -5.775862  52.086207   62.706897\n",
       "2015  316.0  119.768421  70.336842  63.663158 -5.621053  52.526316   64.568421\n",
       "2016  403.5  114.325000  67.237500  63.325000 -6.712500  45.150000   64.162500\n",
       "2017  476.0  116.800000  69.169231  65.369231 -5.615385  52.276923   69.015385\n",
       "2018  540.5  114.593750  65.468750  67.203125 -5.671875  48.765625   72.437500\n",
       "2019  588.0  112.451613  64.741935  69.709677 -5.774194  50.806452   84.354839"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here we acquire a data set that gives all the mean values for all of the years by variable. So we acquire a data set\n",
    "#that gives all of the mean values for all of the variables that has been grouped by year. \n",
    "Cleaned_Dataset = Cleaned_Dataset.groupby('Year').mean()\n",
    "Cleaned_Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Here we are plotting all of the mean variables over the last decade. \n",
    "ax = Cleaned_Dataset.plot.line()\n",
    "ax.legend(loc=0)\n",
    "ax.set_title('Trends over the 2010\\'s')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding Linear Relationships Between Features\n",
    "\n",
    "Now we want to see if there is a linear relationship between variables and popularity before doing linear regression. We will  be looking at relationships between BPM and Popularity as well as Dance and Popularity for each year to analyze linear relationships between these factors in order to predict how well our multiple linear regression will function later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5yZoIHgCOTh/fAbxf0hDwx8BPexmYmZnlI2vT0KeB30wf/wXwNeAPgWeBVT2My8zMcpL1OoKxpse3S1pOcoawNSK2t/s5MzMrr6xNQ3uIiF0RcXuWJCBpvqQfSvpy+vwISbdKelDSFyUt7PQaZmbWO92sUPbX3b5YRPxJF7udRzLctLGa2cXApyJiXNJngbOBy7t9TzMzm51umob+dZev1XYJywZJS4G3AWuAD0sScBLw7nSXdSQznDoRmJnlpJsVyno50dylwAVMLm6zGHgqXfQGYBtwWA/fz8zMOlBExwP53ryR9HbgrRFxrqQTgY8AZwH/HBFHpvscDtwQEXudhUgaBUYBlixZMjw+Pj7t+01MTDA4ONjbX6IPqhInVCfWqsQJ1Ym1KnGCY53OypUrN0XEiqnlWecamra/oEMfwQnAqZLeCuxH0kdwKbBI0oL0rGAp8Gib114LrAVYsWJFnHjiidPGunHjRjrtUwZViROqE2tV4oTqxFqVOMGxzkTW6wimHqnvQzJ8dAFw+3Q/GBEXARcBNM4IImJE0peAdwHjJNciXJcxJjMzm4Ws1xHs1V8gaT/gCuA7M4zho8C4pL8Cfpi+lpmZ5WRG6xE0i4hfSVoDfB34bJc/sxHYmD7+CXDcbOMwM7OZmdUFZU1eClSjd8bMzPaQtbP4w1OLgEOBEeCGXgVlZmb5ydo09MEpz18EngCuAv5nTyIyM7NcZe0sPqJfgZiZWTFm3EcgaVCS+wXMzCoucyKQ9CFJW4GngaclPSLp/HTeIDMzq5isncWfIJnm4RLgn9Pi1wF/RtJpfEFPozMzs77L2ln8PuB9EXFNU9m3JP0I+D84EZiZVc5M+gjualPWq2sSzOpnbAyWL4d585L7sbFOP2HWM1n/ef8tyUL1U50DfH724ZjV0NgYjI7Cli0QkdyPjjoZWG6yNg3tC7xb0h8At6RlrwVeDow1z07a5WplZrZ6NezatWfZrl1J+chIMTFZrWRNBEczOcvoUHr/WHr77ab98lnkwGwu2Lo1W7lZj8169lEzm6Vly5LmoFblZjmYUQevpP0kHSPpX6XTUJvZTK1ZAwMDe5YNDCTlZjnIlAgk7SPpEuBJ4E7gbuBJSZ+QtE8/AjSb80ZGYO1aGBoCKblfu9b9A5abrH0EFwN/CLwf+G5a9nqSCefmkaxDbGZZjYz4H78VJmsieDfw3ohonnL6x5KeAD6HE4GZWeVk7SN4CfDjFuU/BhbNPhwzM8tb1kRwJ9Dq+oDzgDtmH46ZmeUta9PQBcANkt5EMulckEw693LgLT2OzczMcpDpjCAibgZeCVxNskbxgcCXgFdFxHen+1kzMyunrs4IJA2QTD39DmAf4CbgP0XE9j7GZmZmOej2jOBjwHuArwDjwBuBy/sUk5mZ5ajbPoJ3AmdHxDiApPXA9yTNj4gX+hadmZn1XbdnBIcD32k8iYjvA7tJOonNzKzCuk0E84HnppTtJvuoIzMzK5lu/5ELWC/p2aay/YD/K+nXE6lHxKm9DM7MzPqv20SwrkXZ+l4GYmZmxegqEUTEWf0OxMzMiuEF583Mas6JwMys5pwIzMxqzonAzKzmnAjMzGrOicDMrOacCMzMas6JwMys5pwIzMxqzonAzKzmcksEkg6XtEHS/ZLulXReWn6wpBslPZjeH5RXTGZmlu8ZwW7gP0fEbwPHA38s6dXAhcBNEXEUyRKYF+YYk5lZ7eWWCCLiZxFxe/r4GeB+4DDgNCZnN11Hsi6ymZnlRBGR/5tKy4GbgWOArRGxqGnbkxGxV/OQpFFgFGDJkiXD4+Pj077HxMQEg4ODPYy6P6oSJ1Qn1qrECdWJtSpxgmOdzsqVKzdFxIq9NkRErjdgENgEvDN9/tSU7U92eo3h4eHoZMOGDR33KYOqxBlRnVirEmdEdWKtSpwRjnU6wG3R4n9qrqOGJO0D/D0wFhHXpsU/l3Rouv1Q4PE8YzIzq7s8Rw0JuAK4PyL+d9Om64FV6eNVwHV5xWRmZvkuPn8CcCZwt6Q70rL/AnwcuFrS2cBW4PQcYzIzq73cEkFEfBdQm80n5xWHmZntyVcWm5nVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdVcKRKBpFMk/UjSZkkXFh2PWSWMjcHy5TBvXnI/NlZ0RFZRC4oOQNJ84DPAm4BtwA8kXR8R9xUbmVmJjY3B6Cjs2pU837IleQ4wMlJcXFZJZTgjOA7YHBE/iYjngHHgtIJjMiu31asnk0DDrl1JuVlGiohiA5DeBZwSEe9Ln58JvDYiPjBlv1FgFGDJkiXD4+Pj077uxMQEg4OD/Qm6h6oSJ1Qn1qrECbOIddOm9tuGh2ceUBu1qNMC5B3rypUrN0XEir02REShN+B04HNNz88E/ma6nxkeHo5ONmzY0HGfMqhKnBHVibUqcUbMItahoQjY+zY01MPoJtWiTguQd6zAbdHif2oZmoa2AYc3PV8KPFpQLGbVsGYNDAzsWTYwkJSbZVSGRPAD4ChJR0haCJwBXF9wTGblNjICa9fC0BBIyf3ate4othkpfNRQROyW9AHg68B84MqIuLfgsMzKb2TE//itJwpPBAARcQNwQ9FxmJnVURmahszMrEBOBGZmNedEYGZWc04EZmY1V/iVxTMh6QlgS4fdDgG25xDObFUlTqhOrFWJE6oTa1XiBMc6naGIeOnUwkomgm5Iui1aXUpdMlWJE6oTa1XihOrEWpU4wbHOhJuGzMxqzonAzKzm5nIiWFt0AF2qSpxQnVirEidUJ9aqxAmONbM520dgZmbdmctnBGZm1gUnAjOzmpsziUDSfEk/lPTl9PkRkm6V9KCkL6ZTXBdO0iJJ10h6QNL9kl4n6WBJN6ax3ijpoBLEeb6keyXdI+kLkvYrS51KulLS45LuaSprWYdK/LWkzZLukvSaEsR6Sfr53yXpHyQtatp2URrrjyT9QZFxNm37iKSQdEj6vHR1mpZ/MK23eyV9oqm8NHUq6VhJt0i6Q9Jtko5Lywut08JXKOvVDfgw8HfAl9PnVwNnpI8/C5xTdIxpLOuA96WPFwKLgE8AF6ZlFwIXFxzjYcBDwP5NdfmestQp8AbgNcA9TWUt6xB4K/BVQMDxwK0liPXNwIL08cVNsb4auBPYFzgC+DEwv6g40/LDSaaI3wIcUuI6XQl8E9g3ff6yMtYp8A3gLU31uLEMdTonzggkLQXeBnwufS7gJOCadJd1wDuKiW6SpANJvhxXAETEcxHxFHAaSYxQklhJpijfX9ICYAD4GSWp04i4Gdg5pbhdHZ4G/G0kbgEWSTo0n0hbxxoR34iI3enTW0hW5WvEOh4Rz0bEQ8Bm4Lii4kx9CrgAaB5VUro6Bc4BPh4Rz6b7PN4Ua5nqNIAD08cvYXI1xkLrdE4kAuBSki/ri+nzxcBTTX9s20iOcov2CuAJ4Kq0Getzkg4AlkTEzwDS+5cVGWRE/BT4JLCVJAE8DWyinHXa0K4ODwMeadqvbHG/l+RIEEoWq6RTgZ9GxJ1TNpUqztQrgdenTZfflvS7aXnZYv0QcImkR0j+xi5KywuNs/KJQNLbgccjYlNzcYtdyzBOdgHJqeLlEfFvgV+SNGOUStq+fhrJqfTLgQOAt7TYtQx12klZvwtIWg3sBsYaRS12KyRWSQPAauDPWm1uUVZ0nS4ADiJpVvlT4Oq0ZaBssZ4DnB8RhwPnk7YOUHCclU8EwAnAqZIeBsZJmi8uJTm1aqzAtpTJU7AibQO2RcSt6fNrSBLDzxungen9421+Pi9vBB6KiCci4nngWuD3KGedNrSrw20k7dwNpYhb0irg7cBIpI3ElCvW3yI5ELgz/dtaCtwu6TcpV5wN24Br06aV75O0DhxC+WJdRfL3BPAlJpupCo2z8okgIi6KiKURsZxk4ftvRcQIsAF4V7rbKuC6gkL8tYh4DHhE0qvSopOB+4DrSWKEcsS6FThe0kB6VNWIs3R12qRdHV4P/FE6KuN44OlGE1JRJJ0CfBQ4NSJ2NW26HjhD0r6SjgCOAr5fRIwRcXdEvCwilqd/W9uA16Tf4dLVKfCPJAeBSHolyUCM7ZSoTlOPAr+fPj4JeDB9XGyd5tkz3e8bcCKTo4ZeQfKBbybJvPsWHV8a17HAbcBdJF/eg0j6NG4i+VLcBBxcgjg/BjwA3AN8nmTURSnqFPgCSd/F8yT/oM5uV4ckp9yfIRktcjewogSxbiZpD74jvX22af/Vaaw/Ih1dUlScU7Y/zOSooTLW6UJgffp9vR04qYx1Cvw7kv62O4FbgeEy1KmnmDAzq7nKNw2ZmdnsOBGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBWQuS/l869XLjtl3SlyUd3bRP8/Zn0mmF39m0/c/Tbd9s8frnptv2mvbZLG9OBGbtfRM4NL29Gdgf+Icp+/zHdPvvklwk9CVJr2va/hjJZGjLp/zce0mu4DYrnBOBWXvPRsRj6e12kimZj5a0f9M+T6XbHwDeD/wKOLVp+w7gK8BZjQJJvwMczeSU3maFciIw64Kk3wD+PXB3RPxLq30imaBvN7DPlE1XAKskNf7eziZZ5OeZPoVrlokTgVl7p0iakDQB/IJksrB3t9oxndTsv5IsOnLTlM1fI0kOJ0vaFxgBruxf2GbZOBGYtXczySSBxwKvBb4FfENS83TBn08TxS6S5VI/EhFfbX6RiHiBZNW095KsnLYjIr6bQ/xmXVnQeRez2toVEZsbTyRtIlmtbRT4b2nxn5Ic8f8iJpdHbOVKkhlnl+OzASsZJwKz7gXJgicDTWWPNSeLtj8YsVnSD0gW+Hlnp/3N8uREYNbevumKXJCsG/EBYBD4pxm+3ltI1nB4shfBmfWKE4FZe28kWVgEkhE+DwCnR8TGmbxYJKuR7eq4o1nOvDCNmVnNedSQmVnNORGYmdWcE4GZWc05EZiZ1ZwTgZlZzTkRmJnVnBOBmVnNORGYmdXc/wcDeKzwZYw1zgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's start with the year 2010\n",
    "#We will look at the relationships between BPM and Popularity\n",
    "\n",
    "plt.scatter(Dataset_2010[\"BPM\"], Dataset_2010[\"Popularity\"], color = \"red\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "#Now we can look at relationship between Dance and Popularity\n",
    "\n",
    "plt.scatter(Dataset_2010[\"Dance\"], Dataset_2010[\"Popularity\"], color = \"red\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Let's analyze the same for the rest of the years\n",
    "#Year 2011\n",
    "\n",
    "plt.scatter(Dataset_2011[\"BPM\"], Dataset_2011[\"Popularity\"], color = \"green\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2011[\"Dance\"], Dataset_2011[\"Popularity\"], color = \"green\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2012\n",
    "\n",
    "plt.scatter(Dataset_2012[\"BPM\"], Dataset_2012[\"Popularity\"], color = \"blue\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2012[\"Dance\"], Dataset_2012[\"Popularity\"], color = \"blue\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2013\n",
    "\n",
    "plt.scatter(Dataset_2013[\"BPM\"], Dataset_2013[\"Popularity\"], color = \"brown\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2013[\"Dance\"], Dataset_2013[\"Popularity\"], color = \"brown\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2014\n",
    "\n",
    "plt.scatter(Dataset_2014[\"BPM\"], Dataset_2014[\"Popularity\"], color = \"orange\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2014[\"Dance\"], Dataset_2014[\"Popularity\"], color = \"orange\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2015\n",
    "\n",
    "plt.scatter(Dataset_2015[\"BPM\"], Dataset_2015[\"Popularity\"], color = \"black\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2015[\"Dance\"], Dataset_2015[\"Popularity\"], color = \"black\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2016\n",
    "\n",
    "plt.scatter(Dataset_2016[\"BPM\"], Dataset_2016[\"Popularity\"], color = \"pink\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2016[\"Dance\"], Dataset_2016[\"Popularity\"], color = \"pink\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2017\n",
    "\n",
    "plt.scatter(Dataset_2017[\"BPM\"], Dataset_2017[\"Popularity\"], color = \"purple\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2017[\"Dance\"], Dataset_2017[\"Popularity\"], color = \"purple\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2018\n",
    "\n",
    "plt.scatter(Dataset_2018[\"BPM\"], Dataset_2018[\"Popularity\"], color = \"grey\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2018[\"Dance\"], Dataset_2018[\"Popularity\"], color = \"grey\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2019\n",
    "\n",
    "plt.scatter(Dataset_2019[\"BPM\"], Dataset_2019[\"Popularity\"], color = \"hotpink\")\n",
    "plt.xlabel(\"BPM\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2019[\"Dance\"], Dataset_2019[\"Popularity\"], color = \"hotpink\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Popularity\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression to Create Predictor Model\n",
    "\n",
    "Now we have shown the relationships between at least two variables, testing to see if there even are strong linear \n",
    "relationships between features and popularity which would make good models for popularity predictions. As we can see\n",
    "though, there aren't many strong linear relationships in any of the years that correlate strongly with popularity. \n",
    "We will still create predictor models though for each year to see if any year does in fact have a strong predictor for \n",
    "popularity based on the features of BPM, Energy, Dance, Valence, and Loudness, which we categorized as \"Fun\" features. \n",
    "We will be using **multiple linear regression** using sci-kit learn and statsmodels for each year's dataset to analyze this. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>Telephone</td>\n",
       "      <td>Lady Gaga</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>122</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>-6</td>\n",
       "      <td>71</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>All The Right Moves</td>\n",
       "      <td>OneRepublic</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>146</td>\n",
       "      <td>95</td>\n",
       "      <td>53</td>\n",
       "      <td>-4</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>Misery</td>\n",
       "      <td>Maroon 5</td>\n",
       "      <td>pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>103</td>\n",
       "      <td>81</td>\n",
       "      <td>70</td>\n",
       "      <td>-5</td>\n",
       "      <td>73</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>Take It Off</td>\n",
       "      <td>Kesha</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>125</td>\n",
       "      <td>68</td>\n",
       "      <td>73</td>\n",
       "      <td>-5</td>\n",
       "      <td>74</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>Whataya Want from Me</td>\n",
       "      <td>Adam Lambert</td>\n",
       "      <td>australian pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>186</td>\n",
       "      <td>68</td>\n",
       "      <td>44</td>\n",
       "      <td>-5</td>\n",
       "      <td>45</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                 Title        Artist           Genre  Year  BPM  \\\n",
       "0     14             Telephone     Lady Gaga       dance pop  2010  122   \n",
       "1     25   All The Right Moves   OneRepublic       dance pop  2010  146   \n",
       "2     24                Misery      Maroon 5             pop  2010  103   \n",
       "3     23           Take It Off         Kesha       dance pop  2010  125   \n",
       "4     22  Whataya Want from Me  Adam Lambert  australian pop  2010  186   \n",
       "\n",
       "   Energy  Dance  Loudness  Valence  Popularity  \n",
       "0      83     83        -6       71          73  \n",
       "1      95     53        -4       65          65  \n",
       "2      81     70        -5       73          65  \n",
       "3      68     73        -5       74          66  \n",
       "4      68     44        -5       45          66  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's start with 2010\n",
    "\n",
    "Dataset_2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 48.46488126581923\n",
      "Coefficients: \n",
      " [ 0.00882285 -0.18146673  0.10974871 -2.52858497  0.16443306]\n",
      "Predicted Popularity: \n",
      " [70.43493001]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.128\n",
      "Model:                            OLS   Adj. R-squared:                  0.032\n",
      "Method:                 Least Squares   F-statistic:                     1.326\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):              0.270\n",
      "Time:                        13:31:40   Log-Likelihood:                -200.05\n",
      "No. Observations:                  51   AIC:                             412.1\n",
      "Df Residuals:                      45   BIC:                             423.7\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         48.4649     20.080      2.414      0.020       8.022      88.908\n",
      "BPM            0.0088      0.075      0.117      0.907      -0.143       0.161\n",
      "Energy        -0.1815      0.155     -1.168      0.249      -0.494       0.131\n",
      "Dance          0.1097      0.147      0.745      0.460      -0.187       0.406\n",
      "Loudness      -2.5286      1.544     -1.637      0.109      -5.639       0.582\n",
      "Valence        0.1644      0.114      1.443      0.156      -0.065       0.394\n",
      "==============================================================================\n",
      "Omnibus:                       32.786   Durbin-Watson:                   0.920\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              100.234\n",
      "Skew:                          -1.676   Prob(JB):                     1.72e-22\n",
      "Kurtosis:                       8.994   Cond. No.                     1.88e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.88e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "#Year 2010\n",
    "\n",
    "#Setting the X variables that will be predictors for Y\n",
    "X = Dataset_2010[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2010[\"Popularity\"]\n",
    "\n",
    "#using sklearn\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2010 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 73 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 122\n",
    "New_Energy = 83\n",
    "New_Dance = 83\n",
    "New_Loudness = -6\n",
    "New_Valence = 71\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "#used statsmodels here as well \n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted probablity is 70 compared to the 73 we should have gotten\n",
    "#Somewhat close to expected value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>71</td>\n",
       "      <td>Just Cant Get Enough</td>\n",
       "      <td>The Black Eyed Peas</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2011</td>\n",
       "      <td>94</td>\n",
       "      <td>63</td>\n",
       "      <td>66</td>\n",
       "      <td>-9</td>\n",
       "      <td>26</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>66</td>\n",
       "      <td>Best Thing I Never Had</td>\n",
       "      <td>Beyoncé</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2011</td>\n",
       "      <td>99</td>\n",
       "      <td>65</td>\n",
       "      <td>55</td>\n",
       "      <td>-4</td>\n",
       "      <td>30</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>67</td>\n",
       "      <td>Party Rock Anthem</td>\n",
       "      <td>LMFAO</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2011</td>\n",
       "      <td>130</td>\n",
       "      <td>74</td>\n",
       "      <td>75</td>\n",
       "      <td>-4</td>\n",
       "      <td>35</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>68</td>\n",
       "      <td>We R Who We R</td>\n",
       "      <td>Kesha</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2011</td>\n",
       "      <td>120</td>\n",
       "      <td>82</td>\n",
       "      <td>74</td>\n",
       "      <td>-5</td>\n",
       "      <td>65</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69</td>\n",
       "      <td>Price Tag</td>\n",
       "      <td>Jessie J</td>\n",
       "      <td>australian pop</td>\n",
       "      <td>2011</td>\n",
       "      <td>175</td>\n",
       "      <td>83</td>\n",
       "      <td>64</td>\n",
       "      <td>-4</td>\n",
       "      <td>68</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                   Title               Artist           Genre  Year  \\\n",
       "0     71   Just Cant Get Enough  The Black Eyed Peas       dance pop  2011   \n",
       "1     66  Best Thing I Never Had              Beyoncé       dance pop  2011   \n",
       "2     67       Party Rock Anthem                LMFAO       dance pop  2011   \n",
       "3     68           We R Who We R                Kesha       dance pop  2011   \n",
       "4     69               Price Tag             Jessie J  australian pop  2011   \n",
       "\n",
       "   BPM  Energy  Dance  Loudness  Valence  Popularity  \n",
       "0   94      63     66        -9       26          71  \n",
       "1   99      65     55        -4       30          73  \n",
       "2  130      74     75        -4       35          72  \n",
       "3  120      82     74        -5       65          72  \n",
       "4  175      83     64        -4       68          72  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2011 predictor \n",
    "\n",
    "Dataset_2011.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 56.76860681902299\n",
      "Coefficients: \n",
      " [ 0.11767085 -0.09398451  0.08569877  0.49351828 -0.09034381]\n",
      "Predicted Popularity: \n",
      " [60.77415802]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.037\n",
      "Model:                            OLS   Adj. R-squared:                 -0.065\n",
      "Method:                 Least Squares   F-statistic:                    0.3617\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):              0.872\n",
      "Time:                        13:31:40   Log-Likelihood:                -220.84\n",
      "No. Observations:                  53   AIC:                             453.7\n",
      "Df Residuals:                      47   BIC:                             465.5\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         56.7686     28.528      1.990      0.052      -0.622     114.159\n",
      "BPM            0.1177      0.115      1.022      0.312      -0.114       0.349\n",
      "Energy        -0.0940      0.244     -0.386      0.701      -0.584       0.396\n",
      "Dance          0.0857      0.260      0.330      0.743      -0.437       0.608\n",
      "Loudness       0.4935      2.313      0.213      0.832      -4.160       5.147\n",
      "Valence       -0.0903      0.147     -0.616      0.541      -0.385       0.205\n",
      "==============================================================================\n",
      "Omnibus:                       21.249   Durbin-Watson:                   0.615\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               28.397\n",
      "Skew:                          -1.513   Prob(JB):                     6.82e-07\n",
      "Kurtosis:                       4.923   Cond. No.                     2.08e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.08e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2011[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2011[\"Popularity\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2011 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 71 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 94\n",
    "New_Energy = 63\n",
    "New_Dance = 66\n",
    "New_Loudness = -9\n",
    "New_Valence = 26\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted popularity is 60 when it should have been 71\n",
    "#Value is significantly farther from expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>126</td>\n",
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       "      <th>3</th>\n",
       "      <td>118</td>\n",
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       "      <td>pop</td>\n",
       "      <td>2012</td>\n",
       "      <td>93</td>\n",
       "      <td>82</td>\n",
       "      <td>72</td>\n",
       "      <td>-3</td>\n",
       "      <td>62</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>117</td>\n",
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       "      <td>dance pop</td>\n",
       "      <td>2012</td>\n",
       "      <td>125</td>\n",
       "      <td>72</td>\n",
       "      <td>75</td>\n",
       "      <td>-2</td>\n",
       "      <td>75</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                 Title       Artist         Genre  Year  BPM  Energy  \\\n",
       "0    120         Glad You Came   The Wanted      boy band  2012  127      85   \n",
       "1    113  Set Fire to the Rain        Adele  british soul  2012  108      68   \n",
       "2    119             Good Time     Owl City           pop  2012  126      87   \n",
       "3    118        One More Night     Maroon 5           pop  2012   93      82   \n",
       "4    117             Starships  Nicki Minaj     dance pop  2012  125      72   \n",
       "\n",
       "   Dance  Loudness  Valence  Popularity  \n",
       "0     72        -4       45          72  \n",
       "1     61        -4       47          76  \n",
       "2     56        -4       68          73  \n",
       "3     72        -3       62          73  \n",
       "4     75        -2       75          73  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2012\n",
    "\n",
    "Dataset_2012.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 83.56260634882551\n",
      "Coefficients: \n",
      " [ 0.0663748  -0.45293145  0.41721186  1.08585645 -0.18358612]\n",
      "Predicted Popularity: \n",
      " [70.92748569]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.202\n",
      "Model:                            OLS   Adj. R-squared:                  0.065\n",
      "Method:                 Least Squares   F-statistic:                     1.472\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):              0.229\n",
      "Time:                        13:31:41   Log-Likelihood:                -138.86\n",
      "No. Observations:                  35   AIC:                             289.7\n",
      "Df Residuals:                      29   BIC:                             299.1\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         83.5626     35.482      2.355      0.025      10.994     156.131\n",
      "BPM            0.0664      0.127      0.523      0.605      -0.193       0.326\n",
      "Energy        -0.4529      0.228     -1.985      0.057      -0.920       0.014\n",
      "Dance          0.4172      0.289      1.443      0.160      -0.174       1.009\n",
      "Loudness       1.0859      2.043      0.531      0.599      -3.094       5.265\n",
      "Valence       -0.1836      0.151     -1.215      0.234      -0.493       0.126\n",
      "==============================================================================\n",
      "Omnibus:                       42.282   Durbin-Watson:                   0.930\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              169.438\n",
      "Skew:                          -2.661   Prob(JB):                     1.61e-37\n",
      "Kurtosis:                      12.374   Cond. No.                     2.55e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.55e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2012[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2012[\"Popularity\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2012 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 72 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 127\n",
    "New_Energy = 85\n",
    "New_Dance = 72\n",
    "New_Loudness = -4\n",
    "New_Valence = 45\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted popularity is 71 and actual is 72 \n",
    "#Relatively good predictor for this year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>158</td>\n",
       "      <td>Everybody Talks</td>\n",
       "      <td>Neon Trees</td>\n",
       "      <td>indie pop</td>\n",
       "      <td>2013</td>\n",
       "      <td>155</td>\n",
       "      <td>92</td>\n",
       "      <td>47</td>\n",
       "      <td>-4</td>\n",
       "      <td>73</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>164</td>\n",
       "      <td>Work Bitch</td>\n",
       "      <td>Britney Spears</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2013</td>\n",
       "      <td>128</td>\n",
       "      <td>82</td>\n",
       "      <td>63</td>\n",
       "      <td>-7</td>\n",
       "      <td>85</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>159</td>\n",
       "      <td>Hold On, We're Going Home</td>\n",
       "      <td>Drake</td>\n",
       "      <td>canadian hip hop</td>\n",
       "      <td>2013</td>\n",
       "      <td>100</td>\n",
       "      <td>41</td>\n",
       "      <td>77</td>\n",
       "      <td>-7</td>\n",
       "      <td>29</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>160</td>\n",
       "      <td>Best Song Ever</td>\n",
       "      <td>One Direction</td>\n",
       "      <td>boy band</td>\n",
       "      <td>2013</td>\n",
       "      <td>118</td>\n",
       "      <td>88</td>\n",
       "      <td>65</td>\n",
       "      <td>-3</td>\n",
       "      <td>49</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>161</td>\n",
       "      <td>Kiss You</td>\n",
       "      <td>One Direction</td>\n",
       "      <td>boy band</td>\n",
       "      <td>2013</td>\n",
       "      <td>90</td>\n",
       "      <td>93</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>91</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                      Title          Artist             Genre  Year  \\\n",
       "0    158            Everybody Talks      Neon Trees         indie pop  2013   \n",
       "1    164                 Work Bitch  Britney Spears         dance pop  2013   \n",
       "2    159  Hold On, We're Going Home           Drake  canadian hip hop  2013   \n",
       "3    160             Best Song Ever   One Direction          boy band  2013   \n",
       "4    161                   Kiss You   One Direction          boy band  2013   \n",
       "\n",
       "   BPM  Energy  Dance  Loudness  Valence  Popularity  \n",
       "0  155      92     47        -4       73          74  \n",
       "1  128      82     63        -7       85          71  \n",
       "2  100      41     77        -7       29          73  \n",
       "3  118      88     65        -3       49          73  \n",
       "4   90      93     63        -3       91          73  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2013\n",
    "\n",
    "Dataset_2013.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 66.28760606975807\n",
      "Coefficients: \n",
      " [ 0.02382258 -0.05000192 -0.07094011  0.08474761  0.06262205]\n",
      "Predicted Popularity: \n",
      " [66.27816298]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.011\n",
      "Model:                            OLS   Adj. R-squared:                 -0.065\n",
      "Method:                 Least Squares   F-statistic:                    0.1479\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):              0.980\n",
      "Time:                        13:31:41   Log-Likelihood:                -281.21\n",
      "No. Observations:                  71   AIC:                             574.4\n",
      "Df Residuals:                      65   BIC:                             588.0\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         66.2876     22.104      2.999      0.004      22.142     110.433\n",
      "BPM            0.0238      0.066      0.360      0.720      -0.108       0.156\n",
      "Energy        -0.0500      0.161     -0.310      0.757      -0.372       0.272\n",
      "Dance         -0.0709      0.164     -0.432      0.667      -0.399       0.257\n",
      "Loudness       0.0847      1.382      0.061      0.951      -2.675       2.845\n",
      "Valence        0.0626      0.095      0.661      0.511      -0.127       0.252\n",
      "==============================================================================\n",
      "Omnibus:                        5.027   Durbin-Watson:                   0.201\n",
      "Prob(Omnibus):                  0.081   Jarque-Bera (JB):                5.042\n",
      "Skew:                          -0.637   Prob(JB):                       0.0804\n",
      "Kurtosis:                       2.715   Cond. No.                     2.33e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.33e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2013[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2013[\"Popularity\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2013 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 74 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 155\n",
    "New_Energy = 92\n",
    "New_Dance = 47\n",
    "New_Loudness = -4\n",
    "New_Valence = 73\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted popularity is 66 when should have been 74\n",
    "#Relatively poor predictor for what value should have been"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
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       "      <th>Dance</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230</td>\n",
       "      <td>She Looks So Perfect</td>\n",
       "      <td>5 Seconds of Summer</td>\n",
       "      <td>boy band</td>\n",
       "      <td>2014</td>\n",
       "      <td>160</td>\n",
       "      <td>95</td>\n",
       "      <td>49</td>\n",
       "      <td>-4</td>\n",
       "      <td>44</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>225</td>\n",
       "      <td>Love Me Again</td>\n",
       "      <td>John Newman</td>\n",
       "      <td>pop</td>\n",
       "      <td>2014</td>\n",
       "      <td>126</td>\n",
       "      <td>89</td>\n",
       "      <td>50</td>\n",
       "      <td>-5</td>\n",
       "      <td>21</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>226</td>\n",
       "      <td>Latch</td>\n",
       "      <td>Disclosure</td>\n",
       "      <td>house</td>\n",
       "      <td>2014</td>\n",
       "      <td>122</td>\n",
       "      <td>73</td>\n",
       "      <td>50</td>\n",
       "      <td>-5</td>\n",
       "      <td>52</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>227</td>\n",
       "      <td>Adore You</td>\n",
       "      <td>Miley Cyrus</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2014</td>\n",
       "      <td>120</td>\n",
       "      <td>66</td>\n",
       "      <td>58</td>\n",
       "      <td>-5</td>\n",
       "      <td>20</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>228</td>\n",
       "      <td>Love Never Felt So Good</td>\n",
       "      <td>Michael Jackson</td>\n",
       "      <td>pop</td>\n",
       "      <td>2014</td>\n",
       "      <td>118</td>\n",
       "      <td>72</td>\n",
       "      <td>78</td>\n",
       "      <td>-6</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                    Title               Artist      Genre  Year  BPM  \\\n",
       "0    230     She Looks So Perfect  5 Seconds of Summer   boy band  2014  160   \n",
       "1    225            Love Me Again          John Newman        pop  2014  126   \n",
       "2    226                    Latch           Disclosure      house  2014  122   \n",
       "3    227                Adore You          Miley Cyrus  dance pop  2014  120   \n",
       "4    228  Love Never Felt So Good      Michael Jackson        pop  2014  118   \n",
       "\n",
       "   Energy  Dance  Loudness  Valence  Popularity  \n",
       "0      95     49        -4       44          71  \n",
       "1      89     50        -5       21          73  \n",
       "2      73     50        -5       52          72  \n",
       "3      66     58        -5       20          72  \n",
       "4      72     78        -6       71          71  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2014\n",
    "\n",
    "Dataset_2014.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 87.51192043527968\n",
      "Coefficients: \n",
      " [ 0.08395913 -0.22530767 -0.14470518  2.92697087  0.11698428]\n",
      "Predicted Popularity: \n",
      " [65.89002472]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.091\n",
      "Model:                            OLS   Adj. R-squared:                  0.004\n",
      "Method:                 Least Squares   F-statistic:                     1.040\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):              0.404\n",
      "Time:                        13:31:42   Log-Likelihood:                -238.17\n",
      "No. Observations:                  58   AIC:                             488.3\n",
      "Df Residuals:                      52   BIC:                             500.7\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         87.5119     23.502      3.724      0.000      40.352     134.672\n",
      "BPM            0.0840      0.086      0.975      0.334      -0.089       0.257\n",
      "Energy        -0.2253      0.191     -1.182      0.243      -0.608       0.157\n",
      "Dance         -0.1447      0.202     -0.718      0.476      -0.549       0.260\n",
      "Loudness       2.9270      1.801      1.626      0.110      -0.686       6.540\n",
      "Valence        0.1170      0.120      0.978      0.332      -0.123       0.357\n",
      "==============================================================================\n",
      "Omnibus:                       22.199   Durbin-Watson:                   0.653\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               33.610\n",
      "Skew:                          -1.363   Prob(JB):                     5.03e-08\n",
      "Kurtosis:                       5.545   Cond. No.                     1.90e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.9e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2014[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2014[\"Popularity\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2014 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 71 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 160\n",
    "New_Energy = 95\n",
    "New_Dance = 49\n",
    "New_Loudness = -4\n",
    "New_Valence = 44\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted popularity is 65 when it should be 71\n",
    "#Not the best predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>321</td>\n",
       "      <td>I Really Like You</td>\n",
       "      <td>Carly Rae Jepsen</td>\n",
       "      <td>canadian pop</td>\n",
       "      <td>2015</td>\n",
       "      <td>122</td>\n",
       "      <td>81</td>\n",
       "      <td>62</td>\n",
       "      <td>-5</td>\n",
       "      <td>60</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>322</td>\n",
       "      <td>BO$$</td>\n",
       "      <td>Fifth Harmony</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2015</td>\n",
       "      <td>103</td>\n",
       "      <td>87</td>\n",
       "      <td>81</td>\n",
       "      <td>-5</td>\n",
       "      <td>74</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>323</td>\n",
       "      <td>Sugar</td>\n",
       "      <td>Maroon 5</td>\n",
       "      <td>pop</td>\n",
       "      <td>2015</td>\n",
       "      <td>120</td>\n",
       "      <td>79</td>\n",
       "      <td>75</td>\n",
       "      <td>-7</td>\n",
       "      <td>88</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>324</td>\n",
       "      <td>Focus</td>\n",
       "      <td>Ariana Grande</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2015</td>\n",
       "      <td>100</td>\n",
       "      <td>88</td>\n",
       "      <td>67</td>\n",
       "      <td>-6</td>\n",
       "      <td>79</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>325</td>\n",
       "      <td>All About That Bass</td>\n",
       "      <td>Meghan Trainor</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2015</td>\n",
       "      <td>134</td>\n",
       "      <td>88</td>\n",
       "      <td>81</td>\n",
       "      <td>-4</td>\n",
       "      <td>96</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                Title            Artist         Genre  Year  BPM  \\\n",
       "0    321    I Really Like You  Carly Rae Jepsen  canadian pop  2015  122   \n",
       "1    322                 BO$$     Fifth Harmony     dance pop  2015  103   \n",
       "2    323                Sugar          Maroon 5           pop  2015  120   \n",
       "3    324                Focus     Ariana Grande     dance pop  2015  100   \n",
       "4    325  All About That Bass    Meghan Trainor     dance pop  2015  134   \n",
       "\n",
       "   Energy  Dance  Loudness  Valence  Popularity  \n",
       "0      81     62        -5       60          66  \n",
       "1      87     81        -5       74          66  \n",
       "2      79     75        -7       88          66  \n",
       "3      88     67        -6       79          66  \n",
       "4      88     81        -4       96          65  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2015\n",
    "\n",
    "Dataset_2015.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 68.43398160100823\n",
      "Coefficients: \n",
      " [-0.02942686 -0.04576655 -0.02571096 -0.20286017  0.06424352]\n",
      "Predicted Popularity: \n",
      " [64.41164703]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.013\n",
      "Model:                            OLS   Adj. R-squared:                 -0.043\n",
      "Method:                 Least Squares   F-statistic:                    0.2287\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):              0.949\n",
      "Time:                        13:31:43   Log-Likelihood:                -386.76\n",
      "No. Observations:                  95   AIC:                             785.5\n",
      "Df Residuals:                      89   BIC:                             800.8\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         68.4340     18.054      3.790      0.000      32.560     104.307\n",
      "BPM           -0.0294      0.059     -0.501      0.618      -0.146       0.087\n",
      "Energy        -0.0458      0.130     -0.351      0.726      -0.305       0.213\n",
      "Dance         -0.0257      0.141     -0.183      0.855      -0.305       0.253\n",
      "Loudness      -0.2029      1.213     -0.167      0.868      -2.612       2.207\n",
      "Valence        0.0642      0.087      0.739      0.462      -0.108       0.237\n",
      "==============================================================================\n",
      "Omnibus:                       38.774   Durbin-Watson:                   0.351\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               88.209\n",
      "Skew:                          -1.529   Prob(JB):                     7.01e-20\n",
      "Kurtosis:                       6.597   Cond. No.                     1.96e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.96e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "X = Dataset_2015[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2015[\"Popularity\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2015 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 66 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 122\n",
    "New_Energy = 81\n",
    "New_Dance = 62\n",
    "New_Loudness = -5\n",
    "New_Valence = 60\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted popularity is 64 and actual is 66, not bad predictor for this year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>386</td>\n",
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       "      <td>Demi Lovato</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2016</td>\n",
       "      <td>130</td>\n",
       "      <td>75</td>\n",
       "      <td>59</td>\n",
       "      <td>-6</td>\n",
       "      <td>34</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>393</td>\n",
       "      <td>Close</td>\n",
       "      <td>Nick Jonas</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2016</td>\n",
       "      <td>124</td>\n",
       "      <td>62</td>\n",
       "      <td>65</td>\n",
       "      <td>-5</td>\n",
       "      <td>40</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>385</td>\n",
       "      <td>Electric Love</td>\n",
       "      <td>BØRNS</td>\n",
       "      <td>electropop</td>\n",
       "      <td>2016</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>-8</td>\n",
       "      <td>52</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>387</td>\n",
       "      <td>Too Good</td>\n",
       "      <td>Drake</td>\n",
       "      <td>canadian hip hop</td>\n",
       "      <td>2016</td>\n",
       "      <td>118</td>\n",
       "      <td>65</td>\n",
       "      <td>79</td>\n",
       "      <td>-8</td>\n",
       "      <td>39</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>388</td>\n",
       "      <td>Roses</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "      <td>electropop</td>\n",
       "      <td>2016</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>71</td>\n",
       "      <td>-7</td>\n",
       "      <td>34</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index          Title            Artist             Genre  Year  BPM  \\\n",
       "0    386      Confident       Demi Lovato         dance pop  2016  130   \n",
       "1    393          Close        Nick Jonas         dance pop  2016  124   \n",
       "2    385  Electric Love             BØRNS        electropop  2016  120   \n",
       "3    387       Too Good             Drake  canadian hip hop  2016  118   \n",
       "4    388          Roses  The Chainsmokers        electropop  2016  100   \n",
       "\n",
       "   Energy  Dance  Loudness  Valence  Popularity  \n",
       "0      75     59        -6       34          75  \n",
       "1      62     65        -5       40          72  \n",
       "2      80     61        -8       52          76  \n",
       "3      65     79        -8       39          74  \n",
       "4      80     71        -7       34          74  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2016\n",
    "\n",
    "Dataset_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 78.05272532332924\n",
      "Coefficients: \n",
      " [-0.01780832 -0.0583745   0.07444689  1.30649132 -0.08579978]\n",
      "Predicted Popularity: \n",
      " [64.99578232]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.222\n",
      "Model:                            OLS   Adj. R-squared:                  0.170\n",
      "Method:                 Least Squares   F-statistic:                     4.231\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):            0.00194\n",
      "Time:                        13:31:43   Log-Likelihood:                -325.94\n",
      "No. Observations:                  80   AIC:                             663.9\n",
      "Df Residuals:                      74   BIC:                             678.2\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         78.0527     19.752      3.952      0.000      38.697     117.409\n",
      "BPM           -0.0178      0.086     -0.207      0.836      -0.189       0.153\n",
      "Energy        -0.0584      0.119     -0.490      0.625      -0.296       0.179\n",
      "Dance          0.0744      0.169      0.440      0.661      -0.263       0.412\n",
      "Loudness       1.3065      0.438      2.985      0.004       0.435       2.178\n",
      "Valence       -0.0858      0.106     -0.809      0.421      -0.297       0.125\n",
      "==============================================================================\n",
      "Omnibus:                       10.753   Durbin-Watson:                   0.321\n",
      "Prob(Omnibus):                  0.005   Jarque-Bera (JB):               11.375\n",
      "Skew:                          -0.917   Prob(JB):                      0.00339\n",
      "Kurtosis:                       3.223   Cond. No.                     1.87e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.87e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2016[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2016[\"Popularity\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2016 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 75 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 130\n",
    "New_Energy = 75\n",
    "New_Dance = 59\n",
    "New_Loudness = -6\n",
    "New_Valence = 34\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted popularity is 65 when it should be 75\n",
    "#Features in this year were not the best predictors for popularity of songs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>468</td>\n",
       "      <td>Slide (feat. Frank Ocean &amp; Migos)</td>\n",
       "      <td>Calvin Harris</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2017</td>\n",
       "      <td>104</td>\n",
       "      <td>80</td>\n",
       "      <td>74</td>\n",
       "      <td>-3</td>\n",
       "      <td>51</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>462</td>\n",
       "      <td>Despacito - Remix</td>\n",
       "      <td>Luis Fonsi</td>\n",
       "      <td>latin</td>\n",
       "      <td>2017</td>\n",
       "      <td>178</td>\n",
       "      <td>82</td>\n",
       "      <td>65</td>\n",
       "      <td>-4</td>\n",
       "      <td>82</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>463</td>\n",
       "      <td>The Greatest</td>\n",
       "      <td>Sia</td>\n",
       "      <td>australian dance</td>\n",
       "      <td>2017</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>67</td>\n",
       "      <td>-6</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>464</td>\n",
       "      <td>There for You</td>\n",
       "      <td>Martin Garrix</td>\n",
       "      <td>big room</td>\n",
       "      <td>2017</td>\n",
       "      <td>106</td>\n",
       "      <td>64</td>\n",
       "      <td>61</td>\n",
       "      <td>-8</td>\n",
       "      <td>13</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>465</td>\n",
       "      <td>Paris</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "      <td>electropop</td>\n",
       "      <td>2017</td>\n",
       "      <td>100</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>-7</td>\n",
       "      <td>25</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                              Title            Artist  \\\n",
       "0    468  Slide (feat. Frank Ocean & Migos)     Calvin Harris   \n",
       "1    462                  Despacito - Remix        Luis Fonsi   \n",
       "2    463                       The Greatest               Sia   \n",
       "3    464                      There for You     Martin Garrix   \n",
       "4    465                              Paris  The Chainsmokers   \n",
       "\n",
       "              Genre  Year  BPM  Energy  Dance  Loudness  Valence  Popularity  \n",
       "0         dance pop  2017  104      80     74        -3       51          75  \n",
       "1             latin  2017  178      82     65        -4       82          76  \n",
       "2  australian dance  2017  192      73     67        -6       73          76  \n",
       "3          big room  2017  106      64     61        -8       13          76  \n",
       "4        electropop  2017  100      64     65        -7       25          76  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2017\n",
    "\n",
    "Dataset_2017.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 76.17779403033025\n",
      "Coefficients: \n",
      " [ 3.38167509e-02 -1.93245644e-01  1.42245550e-01  1.24964006e+00\n",
      " -5.13152776e-04]\n",
      "Predicted Popularity: \n",
      " [70.98616439]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.084\n",
      "Model:                            OLS   Adj. R-squared:                  0.007\n",
      "Method:                 Least Squares   F-statistic:                     1.088\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):              0.377\n",
      "Time:                        13:31:48   Log-Likelihood:                -244.62\n",
      "No. Observations:                  65   AIC:                             501.2\n",
      "Df Residuals:                      59   BIC:                             514.3\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         76.1778     14.707      5.180      0.000      46.749     105.607\n",
      "BPM            0.0338      0.051      0.662      0.510      -0.068       0.136\n",
      "Energy        -0.1932      0.110     -1.752      0.085      -0.414       0.027\n",
      "Dance          0.1422      0.107      1.331      0.188      -0.072       0.356\n",
      "Loudness       1.2496      0.995      1.256      0.214      -0.742       3.241\n",
      "Valence       -0.0005      0.076     -0.007      0.995      -0.154       0.153\n",
      "==============================================================================\n",
      "Omnibus:                        4.353   Durbin-Watson:                   0.424\n",
      "Prob(Omnibus):                  0.113   Jarque-Bera (JB):                4.300\n",
      "Skew:                          -0.599   Prob(JB):                        0.116\n",
      "Kurtosis:                       2.611   Cond. No.                     1.75e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.75e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2017[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2017[\"Popularity\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2017 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 75 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 104\n",
    "New_Energy = 80\n",
    "New_Dance = 74\n",
    "New_Loudness = -3\n",
    "New_Valence = 51\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted popularity is 71 when it should be 75\n",
    "#Not the best but not the worst for this year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>531</td>\n",
       "      <td>Him &amp; I (with Halsey)</td>\n",
       "      <td>G-Eazy</td>\n",
       "      <td>hip hop</td>\n",
       "      <td>2018</td>\n",
       "      <td>88</td>\n",
       "      <td>73</td>\n",
       "      <td>59</td>\n",
       "      <td>-6</td>\n",
       "      <td>19</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>526</td>\n",
       "      <td>These Days</td>\n",
       "      <td>Rudimental</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2018</td>\n",
       "      <td>92</td>\n",
       "      <td>81</td>\n",
       "      <td>65</td>\n",
       "      <td>-4</td>\n",
       "      <td>55</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>527</td>\n",
       "      <td>What Lovers Do (feat. SZA)</td>\n",
       "      <td>Maroon 5</td>\n",
       "      <td>pop</td>\n",
       "      <td>2018</td>\n",
       "      <td>110</td>\n",
       "      <td>60</td>\n",
       "      <td>80</td>\n",
       "      <td>-5</td>\n",
       "      <td>42</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>528</td>\n",
       "      <td>Finesse - Remix; feat. Cardi B</td>\n",
       "      <td>Bruno Mars</td>\n",
       "      <td>pop</td>\n",
       "      <td>2018</td>\n",
       "      <td>105</td>\n",
       "      <td>86</td>\n",
       "      <td>70</td>\n",
       "      <td>-5</td>\n",
       "      <td>93</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>529</td>\n",
       "      <td>Perfect Duet (Ed Sheeran &amp; Beyoncé)</td>\n",
       "      <td>Ed Sheeran</td>\n",
       "      <td>pop</td>\n",
       "      <td>2018</td>\n",
       "      <td>95</td>\n",
       "      <td>30</td>\n",
       "      <td>59</td>\n",
       "      <td>-7</td>\n",
       "      <td>36</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                                Title      Artist      Genre  Year  \\\n",
       "0    531                Him & I (with Halsey)      G-Eazy    hip hop  2018   \n",
       "1    526                           These Days  Rudimental  dance pop  2018   \n",
       "2    527           What Lovers Do (feat. SZA)    Maroon 5        pop  2018   \n",
       "3    528       Finesse - Remix; feat. Cardi B  Bruno Mars        pop  2018   \n",
       "4    529  Perfect Duet (Ed Sheeran & Beyoncé)  Ed Sheeran        pop  2018   \n",
       "\n",
       "   BPM  Energy  Dance  Loudness  Valence  Popularity  \n",
       "0   88      73     59        -6       19          78  \n",
       "1   92      81     65        -4       55          80  \n",
       "2  110      60     80        -5       42          79  \n",
       "3  105      86     70        -5       93          78  \n",
       "4   95      30     59        -7       36          78  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2018\n",
    "\n",
    "Dataset_2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 92.43770744292375\n",
      "Coefficients: \n",
      " [ 0.04185673 -0.19015447 -0.03406762  2.3948199   0.07228491]\n",
      "Predicted Popularity: \n",
      " [67.2343272]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.108\n",
      "Model:                            OLS   Adj. R-squared:                  0.031\n",
      "Method:                 Least Squares   F-statistic:                     1.409\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):              0.234\n",
      "Time:                        13:31:49   Log-Likelihood:                -233.17\n",
      "No. Observations:                  64   AIC:                             478.3\n",
      "Df Residuals:                      58   BIC:                             491.3\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const         92.4377     15.118      6.114      0.000      62.176     122.700\n",
      "BPM            0.0419      0.056      0.743      0.460      -0.071       0.155\n",
      "Energy        -0.1902      0.120     -1.588      0.118      -0.430       0.049\n",
      "Dance         -0.0341      0.102     -0.334      0.740      -0.238       0.170\n",
      "Loudness       2.3948      1.047      2.286      0.026       0.298       4.491\n",
      "Valence        0.0723      0.064      1.122      0.267      -0.057       0.201\n",
      "==============================================================================\n",
      "Omnibus:                        9.195   Durbin-Watson:                   0.658\n",
      "Prob(Omnibus):                  0.010   Jarque-Bera (JB):                8.820\n",
      "Skew:                          -0.870   Prob(JB):                       0.0122\n",
      "Kurtosis:                       3.529   Cond. No.                     1.96e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.96e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2018[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2018[\"Popularity\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2018 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 78 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 88\n",
    "New_Energy = 73\n",
    "New_Dance = 59\n",
    "New_Loudness = -6\n",
    "New_Valence = 19\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted popularity is 67 when it should be 78\n",
    "#Features were not the best predictors for this year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
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       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
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       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>603</td>\n",
       "      <td>Kills You Slowly</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "      <td>electropop</td>\n",
       "      <td>2019</td>\n",
       "      <td>150</td>\n",
       "      <td>44</td>\n",
       "      <td>70</td>\n",
       "      <td>-9</td>\n",
       "      <td>23</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>588</td>\n",
       "      <td>I Don't Care (with Justin Bieber)</td>\n",
       "      <td>Ed Sheeran</td>\n",
       "      <td>pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>102</td>\n",
       "      <td>68</td>\n",
       "      <td>80</td>\n",
       "      <td>-5</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>574</td>\n",
       "      <td>Lose You To Love Me</td>\n",
       "      <td>Selena Gomez</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>102</td>\n",
       "      <td>34</td>\n",
       "      <td>51</td>\n",
       "      <td>-9</td>\n",
       "      <td>9</td>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>575</td>\n",
       "      <td>Someone You Loved</td>\n",
       "      <td>Lewis Capaldi</td>\n",
       "      <td>pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>110</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>-6</td>\n",
       "      <td>45</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>576</td>\n",
       "      <td>Señorita</td>\n",
       "      <td>Shawn Mendes</td>\n",
       "      <td>canadian pop</td>\n",
       "      <td>2019</td>\n",
       "      <td>117</td>\n",
       "      <td>54</td>\n",
       "      <td>76</td>\n",
       "      <td>-6</td>\n",
       "      <td>75</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                              Title            Artist         Genre  \\\n",
       "0    603                   Kills You Slowly  The Chainsmokers    electropop   \n",
       "1    588  I Don't Care (with Justin Bieber)        Ed Sheeran           pop   \n",
       "2    574                Lose You To Love Me      Selena Gomez     dance pop   \n",
       "3    575                  Someone You Loved     Lewis Capaldi           pop   \n",
       "4    576                           Señorita      Shawn Mendes  canadian pop   \n",
       "\n",
       "   Year  BPM  Energy  Dance  Loudness  Valence  Popularity  \n",
       "0  2019  150      44     70        -9       23          67  \n",
       "1  2019  102      68     80        -5       84          85  \n",
       "2  2019  102      34     51        -9        9          97  \n",
       "3  2019  110      41     50        -6       45          96  \n",
       "4  2019  117      54     76        -6       75          95  "
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2019\n",
    "Dataset_2019.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " 145.6732046826455\n",
      "Coefficients: \n",
      " [-0.11181934 -0.45153094 -0.07030163  3.11901555  0.06690861]\n",
      "Predicted Popularity: \n",
      " [77.57958672]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:             Popularity   R-squared:                       0.477\n",
      "Model:                            OLS   Adj. R-squared:                  0.373\n",
      "Method:                 Least Squares   F-statistic:                     4.569\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):            0.00427\n",
      "Time:                        13:31:50   Log-Likelihood:                -98.995\n",
      "No. Observations:                  31   AIC:                             210.0\n",
      "Df Residuals:                      25   BIC:                             218.6\n",
      "Df Model:                           5                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        145.6732     15.354      9.488      0.000     114.051     177.296\n",
      "BPM           -0.1118      0.062     -1.799      0.084      -0.240       0.016\n",
      "Energy        -0.4515      0.104     -4.359      0.000      -0.665      -0.238\n",
      "Dance         -0.0703      0.119     -0.591      0.560      -0.315       0.175\n",
      "Loudness       3.1190      1.039      3.003      0.006       0.980       5.258\n",
      "Valence        0.0669      0.065      1.026      0.315      -0.067       0.201\n",
      "==============================================================================\n",
      "Omnibus:                        3.974   Durbin-Watson:                   1.556\n",
      "Prob(Omnibus):                  0.137   Jarque-Bera (JB):                1.981\n",
      "Skew:                          -0.322   Prob(JB):                        0.371\n",
      "Kurtosis:                       1.943   Cond. No.                     2.05e+03\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 2.05e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "X = Dataset_2019[[\"BPM\",\"Energy\", \"Dance\", \"Loudness\", \"Valence\"]]\n",
    "Y = Dataset_2019[\"Popularity\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2019 dataset to see how accurate popularity predictor is\n",
    "#Predicted popularity should be close to 67 (the actual popularity value of this song) if there's strong linear relationship\n",
    "New_BPM = 150\n",
    "New_Energy = 44\n",
    "New_Dance = 70\n",
    "New_Loudness = -9\n",
    "New_Valence = 23\n",
    "\n",
    "print(\"Predicted Popularity: \\n\", regression.predict([[New_BPM, New_Energy, New_Dance, New_Loudness, New_Valence]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "popularity_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "#The predicted popularity was 77 when the actual was 67\n",
    "#Features for this year were not the best predicors for popularity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results for Multiple Linear Regression on Predicting Popularity\n",
    "\n",
    "Features in all years were not as good predictors as we would have expected for popularity.\n",
    "Maybe this is because we did not see much linearity when doing scatter plots to test for \n",
    "correlation between features and popularity.\n",
    "\n",
    "However the years that did show strongest relationships\n",
    "for popularity predictions from music features were **2010**, **2012**, and **2015**. In general though, the scatter plots showed\n",
    "there was not a strong linear relationship between popularity and other features to begin with. Our multiple linear regression\n",
    "and prediction models further emphasize this fault. \n",
    "\n",
    "\n",
    "# Finding Linearity between factors and Valence\n",
    "\n",
    "While we were playing around with the data, however, we noticed there was more of a linear relationship between other \n",
    "features and valence, meaning with these datasets we are perhaps better able to predict the valence of songs. Scatter plots are \n",
    "shown below to demonstrate these linear relationships "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>Telephone</td>\n",
       "      <td>Lady Gaga</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>122</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>-6</td>\n",
       "      <td>71</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>All The Right Moves</td>\n",
       "      <td>OneRepublic</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>146</td>\n",
       "      <td>95</td>\n",
       "      <td>53</td>\n",
       "      <td>-4</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>Misery</td>\n",
       "      <td>Maroon 5</td>\n",
       "      <td>pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>103</td>\n",
       "      <td>81</td>\n",
       "      <td>70</td>\n",
       "      <td>-5</td>\n",
       "      <td>73</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>Take It Off</td>\n",
       "      <td>Kesha</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>125</td>\n",
       "      <td>68</td>\n",
       "      <td>73</td>\n",
       "      <td>-5</td>\n",
       "      <td>74</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>Whataya Want from Me</td>\n",
       "      <td>Adam Lambert</td>\n",
       "      <td>australian pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>186</td>\n",
       "      <td>68</td>\n",
       "      <td>44</td>\n",
       "      <td>-5</td>\n",
       "      <td>45</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                 Title        Artist           Genre  Year  BPM  \\\n",
       "0     14             Telephone     Lady Gaga       dance pop  2010  122   \n",
       "1     25   All The Right Moves   OneRepublic       dance pop  2010  146   \n",
       "2     24                Misery      Maroon 5             pop  2010  103   \n",
       "3     23           Take It Off         Kesha       dance pop  2010  125   \n",
       "4     22  Whataya Want from Me  Adam Lambert  australian pop  2010  186   \n",
       "\n",
       "   Energy  Dance  Loudness  Valence  Popularity  \n",
       "0      83     83        -6       71          73  \n",
       "1      95     53        -4       65          65  \n",
       "2      81     70        -5       73          65  \n",
       "3      68     73        -5       74          66  \n",
       "4      68     44        -5       45          66  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset_2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Testing BPM versus Valence for 2010\n",
    "\n",
    "plt.scatter(Dataset_2010[\"Dance\"], Dataset_2010[\"Valence\"], color = \"red\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2010[\"Energy\"], Dataset_2010[\"Valence\"], color = \"red\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "#Testing Energy Versus Valence "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2011\n",
    "\n",
    "\n",
    "plt.scatter(Dataset_2011[\"Dance\"], Dataset_2011[\"Valence\"], color = \"green\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2011[\"Energy\"], Dataset_2011[\"Valence\"], color = \"green\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2012\n",
    "\n",
    "plt.scatter(Dataset_2012[\"Dance\"], Dataset_2012[\"Valence\"], color = \"blue\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2012[\"Energy\"], Dataset_2012[\"Valence\"], color = \"blue\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2013\n",
    "\n",
    "plt.scatter(Dataset_2013[\"Dance\"], Dataset_2013[\"Valence\"], color = \"brown\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2013[\"Energy\"], Dataset_2013[\"Valence\"], color = \"brown\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2014\n",
    "\n",
    "plt.scatter(Dataset_2014[\"Dance\"], Dataset_2014[\"Valence\"], color = \"orange\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2014[\"Energy\"], Dataset_2014[\"Valence\"], color = \"orange\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2015\n",
    "\n",
    "plt.scatter(Dataset_2015[\"Dance\"], Dataset_2015[\"Valence\"], color = \"black\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2015[\"Energy\"], Dataset_2015[\"Valence\"], color = \"black\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2016\n",
    "\n",
    "plt.scatter(Dataset_2016[\"Dance\"], Dataset_2016[\"Valence\"], color = \"pink\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2016[\"Energy\"], Dataset_2016[\"Valence\"], color = \"pink\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2017\n",
    "\n",
    "plt.scatter(Dataset_2017[\"Dance\"], Dataset_2017[\"Valence\"], color = \"purple\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2017[\"Energy\"], Dataset_2017[\"Valence\"], color = \"purple\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2018\n",
    "\n",
    "plt.scatter(Dataset_2018[\"Dance\"], Dataset_2018[\"Valence\"], color = \"grey\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2018[\"Energy\"], Dataset_2018[\"Valence\"], color = \"grey\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Year 2019\n",
    "\n",
    "plt.scatter(Dataset_2019[\"Dance\"], Dataset_2019[\"Valence\"], color = \"hotpink\")\n",
    "plt.xlabel(\"Dance\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()\n",
    "\n",
    "plt.scatter(Dataset_2019[\"Energy\"], Dataset_2019[\"Valence\"], color = \"hotpink\")\n",
    "plt.xlabel(\"Energy\", fontsize = 14)\n",
    "plt.ylabel(\"Valence\", fontsize = 14)\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression to predict Valence from Features\n",
    "\n",
    "The years that show the most linearity between Dance and Energy and Valence are 2010, 2014, and 2016. So we will be using multiple linear regression on those years to see if we can use Dance and Energy values of a song to predict the Valence of that song. \n",
    "\n",
    "(The valence number of a song is representative of the positive tone a song carries.) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>Telephone</td>\n",
       "      <td>Lady Gaga</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>122</td>\n",
       "      <td>83</td>\n",
       "      <td>83</td>\n",
       "      <td>-6</td>\n",
       "      <td>71</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25</td>\n",
       "      <td>All The Right Moves</td>\n",
       "      <td>OneRepublic</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>146</td>\n",
       "      <td>95</td>\n",
       "      <td>53</td>\n",
       "      <td>-4</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24</td>\n",
       "      <td>Misery</td>\n",
       "      <td>Maroon 5</td>\n",
       "      <td>pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>103</td>\n",
       "      <td>81</td>\n",
       "      <td>70</td>\n",
       "      <td>-5</td>\n",
       "      <td>73</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23</td>\n",
       "      <td>Take It Off</td>\n",
       "      <td>Kesha</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>125</td>\n",
       "      <td>68</td>\n",
       "      <td>73</td>\n",
       "      <td>-5</td>\n",
       "      <td>74</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>Whataya Want from Me</td>\n",
       "      <td>Adam Lambert</td>\n",
       "      <td>australian pop</td>\n",
       "      <td>2010</td>\n",
       "      <td>186</td>\n",
       "      <td>68</td>\n",
       "      <td>44</td>\n",
       "      <td>-5</td>\n",
       "      <td>45</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                 Title        Artist           Genre  Year  BPM  \\\n",
       "0     14             Telephone     Lady Gaga       dance pop  2010  122   \n",
       "1     25   All The Right Moves   OneRepublic       dance pop  2010  146   \n",
       "2     24                Misery      Maroon 5             pop  2010  103   \n",
       "3     23           Take It Off         Kesha       dance pop  2010  125   \n",
       "4     22  Whataya Want from Me  Adam Lambert  australian pop  2010  186   \n",
       "\n",
       "   Energy  Dance  Loudness  Valence  Popularity  \n",
       "0      83     83        -6       71          73  \n",
       "1      95     53        -4       65          65  \n",
       "2      81     70        -5       73          65  \n",
       "3      68     73        -5       74          66  \n",
       "4      68     44        -5       45          66  "
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2010 Analysis\n",
    "\n",
    "Dataset_2010.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -32.03674355658168\n",
      "Coefficients: \n",
      " [0.70576115 0.52776812]\n",
      "Predicted Valence: \n",
      " [70.34618559]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Valence   R-squared:                       0.438\n",
      "Model:                            OLS   Adj. R-squared:                  0.414\n",
      "Method:                 Least Squares   F-statistic:                     18.68\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):           1.00e-06\n",
      "Time:                        13:32:19   Log-Likelihood:                -214.83\n",
      "No. Observations:                  51   AIC:                             435.7\n",
      "Df Residuals:                      48   BIC:                             441.5\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        -32.0367     14.762     -2.170      0.035     -61.718      -2.356\n",
      "Energy         0.7058      0.165      4.273      0.000       0.374       1.038\n",
      "Dance          0.5278      0.171      3.080      0.003       0.183       0.872\n",
      "==============================================================================\n",
      "Omnibus:                        4.607   Durbin-Watson:                   2.305\n",
      "Prob(Omnibus):                  0.100   Jarque-Bera (JB):                1.978\n",
      "Skew:                           0.062   Prob(JB):                        0.372\n",
      "Kurtosis:                       2.043   Cond. No.                         642.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2010[[\"Energy\", \"Dance\"]]\n",
    "Y = Dataset_2010[\"Valence\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2010 dataset to see how accurate valence predictor is\n",
    "#Predicted valence should be close to 71 (the actual valence value of this song) if there's strong linear relationship\n",
    "\n",
    "New_Energy = 83\n",
    "New_Dance = 83\n",
    "\n",
    "\n",
    "print(\"Predicted Valence: \\n\", regression.predict([[ New_Energy, New_Dance]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "valence_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predictor worked very well, the predicted valence was 70.3 and actual is 71, very close values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230</td>\n",
       "      <td>She Looks So Perfect</td>\n",
       "      <td>5 Seconds of Summer</td>\n",
       "      <td>boy band</td>\n",
       "      <td>2014</td>\n",
       "      <td>160</td>\n",
       "      <td>95</td>\n",
       "      <td>49</td>\n",
       "      <td>-4</td>\n",
       "      <td>44</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>225</td>\n",
       "      <td>Love Me Again</td>\n",
       "      <td>John Newman</td>\n",
       "      <td>pop</td>\n",
       "      <td>2014</td>\n",
       "      <td>126</td>\n",
       "      <td>89</td>\n",
       "      <td>50</td>\n",
       "      <td>-5</td>\n",
       "      <td>21</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>226</td>\n",
       "      <td>Latch</td>\n",
       "      <td>Disclosure</td>\n",
       "      <td>house</td>\n",
       "      <td>2014</td>\n",
       "      <td>122</td>\n",
       "      <td>73</td>\n",
       "      <td>50</td>\n",
       "      <td>-5</td>\n",
       "      <td>52</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>227</td>\n",
       "      <td>Adore You</td>\n",
       "      <td>Miley Cyrus</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2014</td>\n",
       "      <td>120</td>\n",
       "      <td>66</td>\n",
       "      <td>58</td>\n",
       "      <td>-5</td>\n",
       "      <td>20</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>228</td>\n",
       "      <td>Love Never Felt So Good</td>\n",
       "      <td>Michael Jackson</td>\n",
       "      <td>pop</td>\n",
       "      <td>2014</td>\n",
       "      <td>118</td>\n",
       "      <td>72</td>\n",
       "      <td>78</td>\n",
       "      <td>-6</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index                    Title               Artist      Genre  Year  BPM  \\\n",
       "0    230     She Looks So Perfect  5 Seconds of Summer   boy band  2014  160   \n",
       "1    225            Love Me Again          John Newman        pop  2014  126   \n",
       "2    226                    Latch           Disclosure      house  2014  122   \n",
       "3    227                Adore You          Miley Cyrus  dance pop  2014  120   \n",
       "4    228  Love Never Felt So Good      Michael Jackson        pop  2014  118   \n",
       "\n",
       "   Energy  Dance  Loudness  Valence  Popularity  \n",
       "0      95     49        -4       44          71  \n",
       "1      89     50        -5       21          73  \n",
       "2      73     50        -5       52          72  \n",
       "3      66     58        -5       20          72  \n",
       "4      72     78        -6       71          71  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2014 Analysis\n",
    "\n",
    "Dataset_2014.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -38.404811643994684\n",
      "Coefficients: \n",
      " [0.40831148 1.00452347]\n",
      "Predicted Valence: \n",
      " [49.60642944]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Valence   R-squared:                       0.518\n",
      "Model:                            OLS   Adj. R-squared:                  0.500\n",
      "Method:                 Least Squares   F-statistic:                     29.54\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):           1.93e-09\n",
      "Time:                        13:32:19   Log-Likelihood:                -247.45\n",
      "No. Observations:                  58   AIC:                             500.9\n",
      "Df Residuals:                      55   BIC:                             507.1\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        -38.4048     12.104     -3.173      0.002     -62.663     -14.147\n",
      "Energy         0.4083      0.131      3.107      0.003       0.145       0.672\n",
      "Dance          1.0045      0.168      5.995      0.000       0.669       1.340\n",
      "==============================================================================\n",
      "Omnibus:                        0.170   Durbin-Watson:                   1.886\n",
      "Prob(Omnibus):                  0.919   Jarque-Bera (JB):                0.215\n",
      "Skew:                           0.119   Prob(JB):                        0.898\n",
      "Kurtosis:                       2.821   Cond. No.                         490.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2014[[\"Energy\", \"Dance\"]]\n",
    "Y = Dataset_2014[\"Valence\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2014 dataset to see how accurate valence predictor is\n",
    "#Predicted valence should be close to 44 (the actual valence value of this song) if there's strong linear relationship\n",
    "\n",
    "New_Energy = 95\n",
    "New_Dance = 49\n",
    "\n",
    "\n",
    "print(\"Predicted Valence: \\n\", regression.predict([[New_Energy, New_Dance]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "valence_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted was 49 and actual is 44, only 5 difference. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Artist</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Year</th>\n",
       "      <th>BPM</th>\n",
       "      <th>Energy</th>\n",
       "      <th>Dance</th>\n",
       "      <th>Loudness</th>\n",
       "      <th>Valence</th>\n",
       "      <th>Popularity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>386</td>\n",
       "      <td>Confident</td>\n",
       "      <td>Demi Lovato</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2016</td>\n",
       "      <td>130</td>\n",
       "      <td>75</td>\n",
       "      <td>59</td>\n",
       "      <td>-6</td>\n",
       "      <td>34</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>393</td>\n",
       "      <td>Close</td>\n",
       "      <td>Nick Jonas</td>\n",
       "      <td>dance pop</td>\n",
       "      <td>2016</td>\n",
       "      <td>124</td>\n",
       "      <td>62</td>\n",
       "      <td>65</td>\n",
       "      <td>-5</td>\n",
       "      <td>40</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>385</td>\n",
       "      <td>Electric Love</td>\n",
       "      <td>BØRNS</td>\n",
       "      <td>electropop</td>\n",
       "      <td>2016</td>\n",
       "      <td>120</td>\n",
       "      <td>80</td>\n",
       "      <td>61</td>\n",
       "      <td>-8</td>\n",
       "      <td>52</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>387</td>\n",
       "      <td>Too Good</td>\n",
       "      <td>Drake</td>\n",
       "      <td>canadian hip hop</td>\n",
       "      <td>2016</td>\n",
       "      <td>118</td>\n",
       "      <td>65</td>\n",
       "      <td>79</td>\n",
       "      <td>-8</td>\n",
       "      <td>39</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>388</td>\n",
       "      <td>Roses</td>\n",
       "      <td>The Chainsmokers</td>\n",
       "      <td>electropop</td>\n",
       "      <td>2016</td>\n",
       "      <td>100</td>\n",
       "      <td>80</td>\n",
       "      <td>71</td>\n",
       "      <td>-7</td>\n",
       "      <td>34</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Index          Title            Artist             Genre  Year  BPM  \\\n",
       "0    386      Confident       Demi Lovato         dance pop  2016  130   \n",
       "1    393          Close        Nick Jonas         dance pop  2016  124   \n",
       "2    385  Electric Love             BØRNS        electropop  2016  120   \n",
       "3    387       Too Good             Drake  canadian hip hop  2016  118   \n",
       "4    388          Roses  The Chainsmokers        electropop  2016  100   \n",
       "\n",
       "   Energy  Dance  Loudness  Valence  Popularity  \n",
       "0      75     59        -6       34          75  \n",
       "1      62     65        -5       40          72  \n",
       "2      80     61        -8       52          76  \n",
       "3      65     79        -8       39          74  \n",
       "4      80     71        -7       34          74  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Year 2016 Analysis\n",
    "\n",
    "Dataset_2016.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -22.84004831972633\n",
      "Coefficients: \n",
      " [0.28126634 0.77502413]\n",
      "Predicted Valence: \n",
      " [43.98135056]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                Valence   R-squared:                       0.406\n",
      "Model:                            OLS   Adj. R-squared:                  0.391\n",
      "Method:                 Least Squares   F-statistic:                     26.34\n",
      "Date:                Thu, 19 Mar 2020   Prob (F-statistic):           1.92e-09\n",
      "Time:                        13:32:22   Log-Likelihood:                -336.11\n",
      "No. Observations:                  80   AIC:                             678.2\n",
      "Df Residuals:                      77   BIC:                             685.4\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "const        -22.8400      9.597     -2.380      0.020     -41.950      -3.730\n",
      "Energy         0.2813      0.104      2.711      0.008       0.075       0.488\n",
      "Dance          0.7750      0.137      5.637      0.000       0.501       1.049\n",
      "==============================================================================\n",
      "Omnibus:                        4.585   Durbin-Watson:                   1.926\n",
      "Prob(Omnibus):                  0.101   Jarque-Bera (JB):                2.462\n",
      "Skew:                           0.169   Prob(JB):                        0.292\n",
      "Kurtosis:                       2.209   Cond. No.                         491.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\alexa\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "X = Dataset_2016[[\"Energy\", \"Dance\"]]\n",
    "Y = Dataset_2016[\"Valence\"]\n",
    "\n",
    "\n",
    "regression = linear_model.LinearRegression()\n",
    "regression.fit(X,Y)\n",
    "\n",
    "print(\"Intercept: \\n\" ,regression.intercept_)\n",
    "print(\"Coefficients: \\n\", regression.coef_)\n",
    "\n",
    "#Using the features from the first song of the 2016 dataset to see how accurate valence predictor is\n",
    "#Predicted valence should be close to 34 (the actual valence value of this song) if there's strong linear relationship\n",
    "\n",
    "New_Energy = 75\n",
    "New_Dance = 59\n",
    "\n",
    "\n",
    "print(\"Predicted Valence: \\n\", regression.predict([[New_Energy, New_Dance]]))\n",
    "\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y,X).fit()\n",
    "valence_prediction = model.predict(X)\n",
    "\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Predicted was 43 but actual was 34, meaning this year maybe did not have as good a linear\n",
    "#relationship between dance and energy and valence"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethical Considerations / Limitations\n",
    "\n",
    "Based on Spotify’s policies, we complied that we use the services for our own internal, personal, non-commercial use, and not on behalf of or for the benefit of any third party therefore we have the permission to use the data. Since what we are doing is a non-profit school project that is not for the benefit of any third party, we believe that we have permission to use the data. We obtained this dataset from  Kaggle user Leonardo Henrique who publicly put out this data and collected it from Spotify in order for others to analyze.  (Spotify terms of use: https://developer.spotify.com/terms/). Using this public data for the intended analysis does not pose any breach of data privacy or ethical concerns. \n",
    " \n",
    "We have given explicit credit to Spotify for publicly supplying this data set and all of the quantifications they created within the dataset (valence, danceability, etc).\n",
    " \n",
    "Since this is not a surveyed dataset, we did not have to factor individual consent into our ethical compliance concerns. \n",
    " \n",
    "The characteristics we used to measure how fun a song is are not organically quantifiable; they were created by the Spotify data team. We understand that this could provide a level of bias. Also, our team decided which of Spotify’s data characteristics we believed could fall under the category of ‘fun’. Our perception of fun is key to how the data is shaped.\n",
    " \n",
    "Our dataset was taken from Spotify but we extracted this dataset specifically from Leonardo Henrique, who collected this data and put it on Kaggle for public use. He extracted the numbers and data in order for others to publicly use. \n",
    " \n",
    "Since Spotify was released in 2008, we were only able to study a short time frame of popular music. Spotify has also grown in popularity over the time frame we analyzed, so the relationship between the popularity of the app and the popularity of the music may have contributed to our data.\n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results/ Discussion\n",
    "\n",
    "In the end, our hypothesis was disproved; there was very little relationship between popularity and the components of the music. We were trying to predict popularity based on these factors of ‘fun-ness’ such as BPM, Energy, Dance, Loudness and Valence but we saw that there was no strong linear relationship between the two sufficient enough to create a strong predictor model.\n",
    "\n",
    "In regards to how the years differed in their features and how they predicted popularity, we did find that there were certain years where these ‘fun-ness’ features did more accurately predict the popularity of the song. Regardless there was still no strong linear relationships in any of the years that would provide consistent enough prediction results.\n",
    "\n",
    "However in our EDA portion of the assignment we found linear relationships between Dance and Energy and Valence. Meaning when we ran the same multiple linear regression on these two parameters, Dance and Energy,  trying to predict valence, we came out with more successful results. The predictions were far more accurate. This led us to believe that maybe had we focused on less parameters as a means of predicting popularity this would have resulted in a higher performing predictor. \n",
    "\n",
    "If we were to replicate this study, we might experiment with different music streaming services to analyze whether the source of the music has a correlation with the music’s popularity. Spotify was launched in 2008, which limits the number of years and music listeners \n",
    "\n",
    "If we had access to a music streaming service with a longer history, we would also consider modifying the study to analyze the popularity of music over different timeframes. \n",
    "\n",
    "If we were to replicate this study, we might also narrow down how many parameters we used to measure fun. It is possible that some of our parameters might have correlated toward fun while others were confounding variables, but since we tested them as a single unit it is difficult to know.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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