Predicting_Hits_on_Spotify.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Predicting Hits on Spotify.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Thomas-George-T/Prediciting-Hits-on-Spotify/blob/main/Predicting_Hits_on_Spotify.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "sJVAFAqsVHgS"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Collection"
      ],
      "metadata": {
        "id": "ZFhTwgRq4po-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "file = files.upload()  #upload file into google colab session"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 247
        },
        "id": "p9t_N2kwap3Q",
        "outputId": "75622ebc-2eaf-464a-827a-836933f7f6fb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-17087197-641f-42f5-98b3-acca1c076507\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-17087197-641f-42f5-98b3-acca1c076507\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving dataset-of-00s.csv to dataset-of-00s.csv\n",
            "Saving dataset-of-10s.csv to dataset-of-10s.csv\n",
            "Saving dataset-of-60s.csv to dataset-of-60s.csv\n",
            "Saving dataset-of-70s.csv to dataset-of-70s.csv\n",
            "Saving dataset-of-80s.csv to dataset-of-80s.csv\n",
            "Saving dataset-of-90s.csv to dataset-of-90s.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_00 = pd.read_csv(\"dataset-of-00s.csv\") \n",
        "df_10 = pd.read_csv(\"dataset-of-10s.csv\")\n",
        "df_60 = pd.read_csv(\"dataset-of-60s.csv\")\n",
        "df_70 = pd.read_csv(\"dataset-of-70s.csv\")\n",
        "df_80 = pd.read_csv(\"dataset-of-80s.csv\")\n",
        "df_90 = pd.read_csv(\"dataset-of-90s.csv\")\n",
        "df = pd.concat([df_00, df_10, df_60, df_70, df_80, df_90])\n",
        "df.head()"
      ],
      "metadata": {
        "id": "_Eu6BrGJatgo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        },
        "outputId": "fda70e6d-8906-4e48-cda9-46f8158e1dfe"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      track             artist  \\\n",
              "0                 Lucky Man  Montgomery Gentry   \n",
              "1            On The Hotline       Pretty Ricky   \n",
              "2        Clouds Of Dementia         Candlemass   \n",
              "3  Heavy Metal, Raise Hell!      Zwartketterij   \n",
              "4           I Got A Feelin'   Billy Currington   \n",
              "\n",
              "                                    uri  danceability  energy  key  loudness  \\\n",
              "0  spotify:track:4GiXBCUF7H6YfNQsnBRIzl         0.578   0.471    4    -7.270   \n",
              "1  spotify:track:1zyqZONW985Cs4osz9wlsu         0.704   0.854   10    -5.477   \n",
              "2  spotify:track:6cHZf7RbxXCKwEkgAZT4mY         0.162   0.836    9    -3.009   \n",
              "3  spotify:track:2IjBPp2vMeX7LggzRN3iSX         0.188   0.994    4    -3.745   \n",
              "4  spotify:track:1tF370eYXUcWwkIvaq3IGz         0.630   0.764    2    -4.353   \n",
              "\n",
              "   mode  speechiness  acousticness  instrumentalness  liveness  valence  \\\n",
              "0     1       0.0289      0.368000           0.00000     0.159    0.532   \n",
              "1     0       0.1830      0.018500           0.00000     0.148    0.688   \n",
              "2     1       0.0473      0.000111           0.00457     0.174    0.300   \n",
              "3     1       0.1660      0.000007           0.07840     0.192    0.333   \n",
              "4     1       0.0275      0.363000           0.00000     0.125    0.631   \n",
              "\n",
              "     tempo  duration_ms  time_signature  chorus_hit  sections  target  \n",
              "0  133.061       196707               4    30.88059        13       1  \n",
              "1   92.988       242587               4    41.51106        10       1  \n",
              "2   86.964       338893               4    65.32887        13       0  \n",
              "3  148.440       255667               4    58.59528         9       0  \n",
              "4  112.098       193760               4    22.62384        10       1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52f510de-2a6c-4fea-8b79-84ff5ee54163\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>track</th>\n",
              "      <th>artist</th>\n",
              "      <th>uri</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Lucky Man</td>\n",
              "      <td>Montgomery Gentry</td>\n",
              "      <td>spotify:track:4GiXBCUF7H6YfNQsnBRIzl</td>\n",
              "      <td>0.578</td>\n",
              "      <td>0.471</td>\n",
              "      <td>4</td>\n",
              "      <td>-7.270</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0289</td>\n",
              "      <td>0.368000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.159</td>\n",
              "      <td>0.532</td>\n",
              "      <td>133.061</td>\n",
              "      <td>196707</td>\n",
              "      <td>4</td>\n",
              "      <td>30.88059</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>On The Hotline</td>\n",
              "      <td>Pretty Ricky</td>\n",
              "      <td>spotify:track:1zyqZONW985Cs4osz9wlsu</td>\n",
              "      <td>0.704</td>\n",
              "      <td>0.854</td>\n",
              "      <td>10</td>\n",
              "      <td>-5.477</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1830</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.148</td>\n",
              "      <td>0.688</td>\n",
              "      <td>92.988</td>\n",
              "      <td>242587</td>\n",
              "      <td>4</td>\n",
              "      <td>41.51106</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Clouds Of Dementia</td>\n",
              "      <td>Candlemass</td>\n",
              "      <td>spotify:track:6cHZf7RbxXCKwEkgAZT4mY</td>\n",
              "      <td>0.162</td>\n",
              "      <td>0.836</td>\n",
              "      <td>9</td>\n",
              "      <td>-3.009</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0473</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.00457</td>\n",
              "      <td>0.174</td>\n",
              "      <td>0.300</td>\n",
              "      <td>86.964</td>\n",
              "      <td>338893</td>\n",
              "      <td>4</td>\n",
              "      <td>65.32887</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Heavy Metal, Raise Hell!</td>\n",
              "      <td>Zwartketterij</td>\n",
              "      <td>spotify:track:2IjBPp2vMeX7LggzRN3iSX</td>\n",
              "      <td>0.188</td>\n",
              "      <td>0.994</td>\n",
              "      <td>4</td>\n",
              "      <td>-3.745</td>\n",
              "      <td>1</td>\n",
              "      <td>0.1660</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.07840</td>\n",
              "      <td>0.192</td>\n",
              "      <td>0.333</td>\n",
              "      <td>148.440</td>\n",
              "      <td>255667</td>\n",
              "      <td>4</td>\n",
              "      <td>58.59528</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I Got A Feelin'</td>\n",
              "      <td>Billy Currington</td>\n",
              "      <td>spotify:track:1tF370eYXUcWwkIvaq3IGz</td>\n",
              "      <td>0.630</td>\n",
              "      <td>0.764</td>\n",
              "      <td>2</td>\n",
              "      <td>-4.353</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0275</td>\n",
              "      <td>0.363000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.125</td>\n",
              "      <td>0.631</td>\n",
              "      <td>112.098</td>\n",
              "      <td>193760</td>\n",
              "      <td>4</td>\n",
              "      <td>22.62384</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing"
      ],
      "metadata": {
        "id": "UabFu0Pw6n-b"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finding the number of rows and columns"
      ],
      "metadata": {
        "id": "HkjFsHn56e-L"
      }
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    {
      "cell_type": "code",
      "source": [
        "df.shape"
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        "id": "5ID6Qjz16TGL",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2615fc50-e5d6-4202-98d5-65d9dfa73297"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41106, 19)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exploring data types and checking for nulls"
      ],
      "metadata": {
        "id": "p0TkXcOp6iwa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c145b36-f528-4abc-87c7-2de4a0b4532c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 41106 entries, 0 to 5519\n",
            "Data columns (total 19 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   track             41106 non-null  object \n",
            " 1   artist            41106 non-null  object \n",
            " 2   uri               41106 non-null  object \n",
            " 3   danceability      41106 non-null  float64\n",
            " 4   energy            41106 non-null  float64\n",
            " 5   key               41106 non-null  int64  \n",
            " 6   loudness          41106 non-null  float64\n",
            " 7   mode              41106 non-null  int64  \n",
            " 8   speechiness       41106 non-null  float64\n",
            " 9   acousticness      41106 non-null  float64\n",
            " 10  instrumentalness  41106 non-null  float64\n",
            " 11  liveness          41106 non-null  float64\n",
            " 12  valence           41106 non-null  float64\n",
            " 13  tempo             41106 non-null  float64\n",
            " 14  duration_ms       41106 non-null  int64  \n",
            " 15  time_signature    41106 non-null  int64  \n",
            " 16  chorus_hit        41106 non-null  float64\n",
            " 17  sections          41106 non-null  int64  \n",
            " 18  target            41106 non-null  int64  \n",
            "dtypes: float64(10), int64(6), object(3)\n",
            "memory usage: 6.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for nulls again. Making doubly sure"
      ],
      "metadata": {
        "id": "sqHgdlUf6zB4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "id": "SL7OQreU6vgD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46762202-1ada-49ff-ad4f-1282c6f5dab2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "track               0\n",
              "artist              0\n",
              "uri                 0\n",
              "danceability        0\n",
              "energy              0\n",
              "key                 0\n",
              "loudness            0\n",
              "mode                0\n",
              "speechiness         0\n",
              "acousticness        0\n",
              "instrumentalness    0\n",
              "liveness            0\n",
              "valence             0\n",
              "tempo               0\n",
              "duration_ms         0\n",
              "time_signature      0\n",
              "chorus_hit          0\n",
              "sections            0\n",
              "target              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see that there are no null values in the dataset. Taking the rough statistics of the data."
      ],
      "metadata": {
        "id": "uy2WDLw44ZrR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "ou4-vQYsjllp",
        "outputId": "55322fe1-96bf-4089-b7a2-396c3a2617da"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       danceability        energy           key      loudness          mode  \\\n",
              "count  41106.000000  41106.000000  41106.000000  41106.000000  41106.000000   \n",
              "mean       0.539695      0.579545      5.213594    -10.221525      0.693354   \n",
              "std        0.177821      0.252628      3.534977      5.311626      0.461107   \n",
              "min        0.000000      0.000251      0.000000    -49.253000      0.000000   \n",
              "25%        0.420000      0.396000      2.000000    -12.816000      0.000000   \n",
              "50%        0.552000      0.601000      5.000000     -9.257000      1.000000   \n",
              "75%        0.669000      0.787000      8.000000     -6.374250      1.000000   \n",
              "max        0.988000      1.000000     11.000000      3.744000      1.000000   \n",
              "\n",
              "        speechiness  acousticness  instrumentalness      liveness  \\\n",
              "count  41106.000000  41106.000000      41106.000000  41106.000000   \n",
              "mean       0.072960      0.364197          0.154416      0.201535   \n",
              "std        0.086112      0.338913          0.303530      0.172959   \n",
              "min        0.000000      0.000000          0.000000      0.013000   \n",
              "25%        0.033700      0.039400          0.000000      0.094000   \n",
              "50%        0.043400      0.258000          0.000120      0.132000   \n",
              "75%        0.069800      0.676000          0.061250      0.261000   \n",
              "max        0.960000      0.996000          1.000000      0.999000   \n",
              "\n",
              "            valence         tempo   duration_ms  time_signature    chorus_hit  \\\n",
              "count  41106.000000  41106.000000  4.110600e+04    41106.000000  41106.000000   \n",
              "mean       0.542440    119.338249  2.348776e+05        3.893689     40.106041   \n",
              "std        0.267329     29.098845  1.189674e+05        0.423073     19.005515   \n",
              "min        0.000000      0.000000  1.516800e+04        0.000000      0.000000   \n",
              "25%        0.330000     97.397000  1.729278e+05        4.000000     27.599792   \n",
              "50%        0.558000    117.565000  2.179070e+05        4.000000     35.850795   \n",
              "75%        0.768000    136.494000  2.667730e+05        4.000000     47.625615   \n",
              "max        0.996000    241.423000  4.170227e+06        5.000000    433.182000   \n",
              "\n",
              "           sections        target  \n",
              "count  41106.000000  41106.000000  \n",
              "mean      10.475673      0.500000  \n",
              "std        4.871850      0.500006  \n",
              "min        0.000000      0.000000  \n",
              "25%        8.000000      0.000000  \n",
              "50%       10.000000      0.500000  \n",
              "75%       12.000000      1.000000  \n",
              "max      169.000000      1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e745a8d7-bf14-4569-896f-fba60eb6dd47\">\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>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>time_signature</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>4.110600e+04</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "      <td>41106.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.539695</td>\n",
              "      <td>0.579545</td>\n",
              "      <td>5.213594</td>\n",
              "      <td>-10.221525</td>\n",
              "      <td>0.693354</td>\n",
              "      <td>0.072960</td>\n",
              "      <td>0.364197</td>\n",
              "      <td>0.154416</td>\n",
              "      <td>0.201535</td>\n",
              "      <td>0.542440</td>\n",
              "      <td>119.338249</td>\n",
              "      <td>2.348776e+05</td>\n",
              "      <td>3.893689</td>\n",
              "      <td>40.106041</td>\n",
              "      <td>10.475673</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.177821</td>\n",
              "      <td>0.252628</td>\n",
              "      <td>3.534977</td>\n",
              "      <td>5.311626</td>\n",
              "      <td>0.461107</td>\n",
              "      <td>0.086112</td>\n",
              "      <td>0.338913</td>\n",
              "      <td>0.303530</td>\n",
              "      <td>0.172959</td>\n",
              "      <td>0.267329</td>\n",
              "      <td>29.098845</td>\n",
              "      <td>1.189674e+05</td>\n",
              "      <td>0.423073</td>\n",
              "      <td>19.005515</td>\n",
              "      <td>4.871850</td>\n",
              "      <td>0.500006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000251</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-49.253000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.013000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.516800e+04</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.420000</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>-12.816000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.033700</td>\n",
              "      <td>0.039400</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.094000</td>\n",
              "      <td>0.330000</td>\n",
              "      <td>97.397000</td>\n",
              "      <td>1.729278e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>27.599792</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.552000</td>\n",
              "      <td>0.601000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>-9.257000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.043400</td>\n",
              "      <td>0.258000</td>\n",
              "      <td>0.000120</td>\n",
              "      <td>0.132000</td>\n",
              "      <td>0.558000</td>\n",
              "      <td>117.565000</td>\n",
              "      <td>2.179070e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>35.850795</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>0.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.669000</td>\n",
              "      <td>0.787000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>-6.374250</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.069800</td>\n",
              "      <td>0.676000</td>\n",
              "      <td>0.061250</td>\n",
              "      <td>0.261000</td>\n",
              "      <td>0.768000</td>\n",
              "      <td>136.494000</td>\n",
              "      <td>2.667730e+05</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>47.625615</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.988000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>3.744000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.960000</td>\n",
              "      <td>0.996000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.999000</td>\n",
              "      <td>0.996000</td>\n",
              "      <td>241.423000</td>\n",
              "      <td>4.170227e+06</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>433.182000</td>\n",
              "      <td>169.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e745a8d7-bf14-4569-896f-fba60eb6dd47')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['time_signature'].describe()"
      ],
      "metadata": {
        "id": "GfBBQ-hb2-4N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81ed690a-8ee3-4e59-d0e4-f9ca0962017a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    41106.000000\n",
              "mean         3.893689\n",
              "std          0.423073\n",
              "min          0.000000\n",
              "25%          4.000000\n",
              "50%          4.000000\n",
              "75%          4.000000\n",
              "max          5.000000\n",
              "Name: time_signature, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(axis=1, labels='time_signature')"
      ],
      "metadata": {
        "id": "MWkuOLbA-uJU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropping the column time signature as most values are 4.\n"
      ],
      "metadata": {
        "id": "jnpHfslhomj6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Scaling the data\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "scale = StandardScaler()\n",
        "\n",
        "df_X = df.iloc[:,3:17]\n",
        "\n",
        "\n",
        "df_scaled = scale.fit_transform(df_X)\n",
        "df_scaled = pd.DataFrame(df_scaled)\n",
        "df_scaled.columns = df_X.columns\n",
        "df_scaled"
      ],
      "metadata": {
        "id": "SNG6fqGyA7im",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "b1d33bf5-7949-4a2f-8354-4bae3c2840eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       danceability    energy       key  loudness      mode  speechiness  \\\n",
              "0          0.215416 -0.429668 -0.343315  0.555679  0.665030    -0.511673   \n",
              "1          0.924004  1.086412  1.354030  0.893245 -1.503691     1.277886   \n",
              "2         -2.124046  1.015160  1.071139  1.357892  0.665030    -0.297994   \n",
              "3         -1.977830  1.640592 -0.343315  1.219326  0.665030     1.080465   \n",
              "4          0.507849  0.730153 -0.909096  1.104859  0.665030    -0.527931   \n",
              "...             ...       ...       ...       ...       ...          ...   \n",
              "41101     -0.808099  0.852864  1.071139  0.781602 -1.503691     1.870147   \n",
              "41102      0.457236  0.298684  1.071139  0.741877 -1.503691    -0.488447   \n",
              "41103      0.125437 -1.051141  1.354030 -0.939738 -1.503691    -0.501221   \n",
              "41104      0.462860  0.797446  0.505358  0.779719  0.665030    -0.419930   \n",
              "41105      0.699055  0.631192 -0.909096  0.229033  0.665030    -0.543028   \n",
              "\n",
              "       acousticness  instrumentalness  liveness   valence     tempo  \\\n",
              "0          0.011220         -0.508740 -0.245926 -0.039055  0.471597   \n",
              "1         -1.020031         -0.508740 -0.309525  0.544503 -0.905554   \n",
              "2         -1.074290         -0.493684 -0.159199 -0.906910 -1.112575   \n",
              "3         -1.074596         -0.250443 -0.055127 -0.783465  1.000112   \n",
              "4         -0.003533         -0.508740 -0.442506  0.331280 -0.248819   \n",
              "...             ...               ...       ...       ...       ...   \n",
              "41101     -1.071880         -0.508052 -0.112945  0.821319  1.154862   \n",
              "41102     -0.925315         -0.488116 -0.623476  0.551984  0.509605   \n",
              "41103      0.223667         -0.508704 -0.552360  0.106835  1.632688   \n",
              "41104     -1.074320          1.978688  1.049193 -1.228615  0.024494   \n",
              "41105     -0.761849         -0.330173  0.760104 -0.315871 -0.930675   \n",
              "\n",
              "       duration_ms  chorus_hit  sections  \n",
              "0        -0.320853   -0.485415  0.518152  \n",
              "1         0.064804    0.073928 -0.097638  \n",
              "2         0.874330    1.327148  0.518152  \n",
              "3         0.174751    0.972847 -0.302901  \n",
              "4        -0.345625   -0.919860 -0.097638  \n",
              "...            ...         ...       ...  \n",
              "41101    -1.284154   -0.513802 -0.918691  \n",
              "41102    -0.195220   -0.274186 -0.097638  \n",
              "41103    -0.378684   -0.999112 -0.097638  \n",
              "41104     0.800023    0.369873  0.107625  \n",
              "41105     0.496743    0.126142  0.723415  \n",
              "\n",
              "[41106 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.215416</td>\n",
              "      <td>-0.429668</td>\n",
              "      <td>-0.343315</td>\n",
              "      <td>0.555679</td>\n",
              "      <td>0.665030</td>\n",
              "      <td>-0.511673</td>\n",
              "      <td>0.011220</td>\n",
              "      <td>-0.508740</td>\n",
              "      <td>-0.245926</td>\n",
              "      <td>-0.039055</td>\n",
              "      <td>0.471597</td>\n",
              "      <td>-0.320853</td>\n",
              "      <td>-0.485415</td>\n",
              "      <td>0.518152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.924004</td>\n",
              "      <td>1.086412</td>\n",
              "      <td>1.354030</td>\n",
              "      <td>0.893245</td>\n",
              "      <td>-1.503691</td>\n",
              "      <td>1.277886</td>\n",
              "      <td>-1.020031</td>\n",
              "      <td>-0.508740</td>\n",
              "      <td>-0.309525</td>\n",
              "      <td>0.544503</td>\n",
              "      <td>-0.905554</td>\n",
              "      <td>0.064804</td>\n",
              "      <td>0.073928</td>\n",
              "      <td>-0.097638</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.124046</td>\n",
              "      <td>1.015160</td>\n",
              "      <td>1.071139</td>\n",
              "      <td>1.357892</td>\n",
              "      <td>0.665030</td>\n",
              "      <td>-0.297994</td>\n",
              "      <td>-1.074290</td>\n",
              "      <td>-0.493684</td>\n",
              "      <td>-0.159199</td>\n",
              "      <td>-0.906910</td>\n",
              "      <td>-1.112575</td>\n",
              "      <td>0.874330</td>\n",
              "      <td>1.327148</td>\n",
              "      <td>0.518152</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-1.977830</td>\n",
              "      <td>1.640592</td>\n",
              "      <td>-0.343315</td>\n",
              "      <td>1.219326</td>\n",
              "      <td>0.665030</td>\n",
              "      <td>1.080465</td>\n",
              "      <td>-1.074596</td>\n",
              "      <td>-0.250443</td>\n",
              "      <td>-0.055127</td>\n",
              "      <td>-0.783465</td>\n",
              "      <td>1.000112</td>\n",
              "      <td>0.174751</td>\n",
              "      <td>0.972847</td>\n",
              "      <td>-0.302901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.507849</td>\n",
              "      <td>0.730153</td>\n",
              "      <td>-0.909096</td>\n",
              "      <td>1.104859</td>\n",
              "      <td>0.665030</td>\n",
              "      <td>-0.527931</td>\n",
              "      <td>-0.003533</td>\n",
              "      <td>-0.508740</td>\n",
              "      <td>-0.442506</td>\n",
              "      <td>0.331280</td>\n",
              "      <td>-0.248819</td>\n",
              "      <td>-0.345625</td>\n",
              "      <td>-0.919860</td>\n",
              "      <td>-0.097638</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41101</th>\n",
              "      <td>-0.808099</td>\n",
              "      <td>0.852864</td>\n",
              "      <td>1.071139</td>\n",
              "      <td>0.781602</td>\n",
              "      <td>-1.503691</td>\n",
              "      <td>1.870147</td>\n",
              "      <td>-1.071880</td>\n",
              "      <td>-0.508052</td>\n",
              "      <td>-0.112945</td>\n",
              "      <td>0.821319</td>\n",
              "      <td>1.154862</td>\n",
              "      <td>-1.284154</td>\n",
              "      <td>-0.513802</td>\n",
              "      <td>-0.918691</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41102</th>\n",
              "      <td>0.457236</td>\n",
              "      <td>0.298684</td>\n",
              "      <td>1.071139</td>\n",
              "      <td>0.741877</td>\n",
              "      <td>-1.503691</td>\n",
              "      <td>-0.488447</td>\n",
              "      <td>-0.925315</td>\n",
              "      <td>-0.488116</td>\n",
              "      <td>-0.623476</td>\n",
              "      <td>0.551984</td>\n",
              "      <td>0.509605</td>\n",
              "      <td>-0.195220</td>\n",
              "      <td>-0.274186</td>\n",
              "      <td>-0.097638</td>\n",
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              "    <tr>\n",
              "      <th>41103</th>\n",
              "      <td>0.125437</td>\n",
              "      <td>-1.051141</td>\n",
              "      <td>1.354030</td>\n",
              "      <td>-0.939738</td>\n",
              "      <td>-1.503691</td>\n",
              "      <td>-0.501221</td>\n",
              "      <td>0.223667</td>\n",
              "      <td>-0.508704</td>\n",
              "      <td>-0.552360</td>\n",
              "      <td>0.106835</td>\n",
              "      <td>1.632688</td>\n",
              "      <td>-0.378684</td>\n",
              "      <td>-0.999112</td>\n",
              "      <td>-0.097638</td>\n",
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              "    <tr>\n",
              "      <th>41104</th>\n",
              "      <td>0.462860</td>\n",
              "      <td>0.797446</td>\n",
              "      <td>0.505358</td>\n",
              "      <td>0.779719</td>\n",
              "      <td>0.665030</td>\n",
              "      <td>-0.419930</td>\n",
              "      <td>-1.074320</td>\n",
              "      <td>1.978688</td>\n",
              "      <td>1.049193</td>\n",
              "      <td>-1.228615</td>\n",
              "      <td>0.024494</td>\n",
              "      <td>0.800023</td>\n",
              "      <td>0.369873</td>\n",
              "      <td>0.107625</td>\n",
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              "    <tr>\n",
              "      <th>41105</th>\n",
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              "      <td>0.229033</td>\n",
              "      <td>0.665030</td>\n",
              "      <td>-0.543028</td>\n",
              "      <td>-0.761849</td>\n",
              "      <td>-0.330173</td>\n",
              "      <td>0.760104</td>\n",
              "      <td>-0.315871</td>\n",
              "      <td>-0.930675</td>\n",
              "      <td>0.496743</td>\n",
              "      <td>0.126142</td>\n",
              "      <td>0.723415</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41106 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-318ffd1f-8fea-4a2d-a215-597cf576e482')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-318ffd1f-8fea-4a2d-a215-597cf576e482 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-318ffd1f-8fea-4a2d-a215-597cf576e482');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Managing outliers and creating boxplots to analyze the distribution of data and remove outliers"
      ],
      "metadata": {
        "id": "aP4CBNx8ow6o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#box plot with outliers\n",
        "df_melt=pd.melt(df_scaled)\n",
        "df_melt\n",
        "sns.set(rc={'figure.figsize':(23,10)})\n",
        "sns.boxplot(x='variable', y='value', data=df_melt)\n",
        "plt.xlabel(None)\n",
        "plt.title('Distribution of predictors with outliers')"
      ],
      "metadata": {
        "id": "h5uX2ZGOCZnl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "de53b4ad-49aa-4170-d99b-93497631e2c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of predictors with outliers')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1656x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#boxplot without outliers\n",
        "df_melt=pd.melt(df_scaled)\n",
        "df_melt\n",
        "sns.set(rc={'figure.figsize':(23,10)})\n",
        "sns.boxplot(x='variable', y='value', data=df_melt, showfliers=False)\n",
        "plt.xlabel(None)\n",
        "plt.title('Distribution of predictors without outliers')"
      ],
      "metadata": {
        "id": "vpx3kRvA9KaK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "e9e1cdb8-4933-46ba-9a0b-4cc974d00830"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of predictors without outliers')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1656x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing the outliers"
      ],
      "metadata": {
        "id": "zWtO3cF2pAlo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_clean= df_scaled\n",
        "for col in df_clean.columns:\n",
        "  q1=df_clean[col].quantile(0.25)\n",
        "  q3=df_clean[col].quantile(0.75)\n",
        "  inq=q3-q1\n",
        "  filter=(df_clean[col] >= q1-1.5*inq) & (df_clean[col] <= q3+1.5*inq)\n",
        "  df_clean[col] = df_clean[col].loc[filter]"
      ],
      "metadata": {
        "id": "LkDpLXF9EbGo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#number of outliers for each predictor\n",
        "df_clean.isna().sum()"
      ],
      "metadata": {
        "id": "kXmPXdEuFcB0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39e4bd54-b39a-488f-d8eb-4a8e979bb6bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "danceability           1\n",
              "energy                 0\n",
              "key                    0\n",
              "loudness            1343\n",
              "mode                   0\n",
              "speechiness         5088\n",
              "acousticness           0\n",
              "instrumentalness    8920\n",
              "liveness            2688\n",
              "valence                0\n",
              "tempo                434\n",
              "duration_ms         1987\n",
              "chorus_hit          1839\n",
              "sections            1570\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing 2 predictors with a lot of outliers"
      ],
      "metadata": {
        "id": "99hUzWj0p3bV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#removing 2 predictors\n",
        "df_clean = df_clean.drop(axis=1, labels=['speechiness', 'instrumentalness'])"
      ],
      "metadata": {
        "id": "Hs8yzQQhXqfh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_remove = df_clean.dropna()\n",
        "df_remove.shape"
      ],
      "metadata": {
        "id": "X502wN9WXavd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c40883b-668b-41d1-d023-d92deabe97fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(33401, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Total of 33401 songs remain after cleaning"
      ],
      "metadata": {
        "id": "uvuamqflqCr3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#box plot after removing the outliers\n",
        "df_melt=pd.melt(df_remove)\n",
        "df_melt\n",
        "sns.set(rc={'figure.figsize':(23,10)})\n",
        "sns.boxplot(x='variable', y='value', data=df_melt, showfliers=False)\n",
        "plt.xlabel(None)\n",
        "plt.title('Distribution of predictors without outliers')"
      ],
      "metadata": {
        "id": "Egxqteb9NJ6E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "4e671265-2f6d-445a-ad4c-76371d49990a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Distribution of predictors without outliers')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1656x720 with 1 Axes>"
            ],
            "image/png": 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xx6Z///6bnB7/cmF45JFH5gMf+MBrxv3gBz+Y0047LR/72Mdy/PHHp66uLl/72te6nOuVvva1r6V///454YQTcvbZZ2fs2LGbXGm8M7vvvnt22WWXNDY25otf/GK+/vWvb/biSMnGcm3atGl55JFHcvzxx+eoo47K5MmTOwqvz33uc9lnn31y/PHH55xzzsm4ceO2+LwTJkzISSedlHPOOSdHHHFELrzwwqxevbpjnEmTJmX48OGZNWtW3vOe92TixIn5/Oc/n2OOOSaLFi3a5Mrhr/bCCy9k8uTJGTlyZI477rg0NDTk3HPP3exjBwwYkLe//e1585vf3HHhnWHDhmWfffbZ4orbzub31d70pjflsssuy8UXX5yjjjoqs2fPzrRp0zqe78ILL8zs2bMzfPjw/PKXv8wJJ5zQ8bUHHXRQTjnllJxwwgkZPnz4Zk9Vf73XwNFHH52TTz45p512Wk4//fQcd9xxm3ztxz/+8dxxxx0ZMWJEmpqaXvf7eOc735mLL744U6dOzYgRI3LiiSfm5ptv3uLj16xZk8svvzxHHnlkjjnmmKxYsSIXXHBBp8cLACBJ+rS3t7cXHQIAgOpy7733dnoFcAAAKIIVngAAAABAaSg8AQAAAIDScEo7AAAAAFAaVngCAAAAAKWh8AQAAAAASkPhCQAAAACURt+iA1TSypUvZMMGW5ICAAAAQFnV1PTJrrvutMX7S1V4btjQrvAEAAAAgF7MKe0AAAAAQGkoPAEAAACA0lB4AgAAAAClofAEAAAAAEpD4QkAAAAAlIbCEwAAAAAoDYUnAAAAAFAaCk8AAAAAoDQUngAAAABAaSg8AQAAAIDSUHgCAAAAAKWh8AQAAAAASkPhCQAAAACUhsITAAAAACgNhScAAAAAUBoKTwAAAACgNBSeAAAAAEBpKDwBAAAAgNJQeAIAAAAApaHwBAAAAABKQ+EJAAAAAJRG36IDAAAAwMvmzr0rc+b8rqJjtrW1Jknq6xsqOu6oUWPS2Di6omMCsO0UngAAAJRaa2v3FJ4A9Ex92tvb24sOUSktLc9nw4bSfDsAAABUQFPTlCTJ5MlTC04CQCXU1PTJoEE7b/F+KzwroJpOuUicdgEAAABAeSk8eyinXAAAAADA1lN4VkBj4+iKr5h0ygUAAAAAbL2aogMAAAAAAFSKwhMAAAAAKA2FJwAAAABQGgpPAAAAAKA0FJ4AAAAAQGkoPAEAAACA0lB4AgAAAAClofAEAAAAAEpD4QkAAAAAlIbCEwAAAAAoDYUnAAAAAFAaCk8AAAAAoDQUngAAAABAafQtOgAA1W3u3LsyZ87vKjpmW1trkqS+vqGi4ybJqFFj0tg4uuLjAgAA0DMoPAHocVpbu6/wBAAAoNwUngBsk8bG0RVfMdnUNCVJMnny1IqOCwAAQPnZwxMAAAAAKA2FJwAAAABQGgpPAAAAAKA0FJ4AAAAAQGkoPAEAAACA0lB4AgAAAAClofAEAAAAAEpD4QkAAAAAlIbCEwAAAAAoDYUnAAAAAFAaCk8AAAAAoDQUngAAAABAaSg8AQAAAIDSUHgCAAAAAKWh8AQAAAAASkPhCQAAAACUhsITAAAAACgNhScAAAAAUBoKTwAAAACgNBSeAAAAAEBpKDwBAAAAgNJQeAIAAAAApaHwBAAAAABKQ+EJAAAAAJSGwhMAAAAAKA2FJwAAAABQGgpPAAAAAKA0FJ4AAAAAQGkoPAEAAACA0uhbdICXrVy5Ml/+8pezcOHC1NXVZciQIZk6dWp22223oqMBAAAAAFWix6zw7NOnTz75yU/mjjvuyC9/+cvsv//++fa3v110LAAAAACgivSYwrOhoSFHHnlkx+3DDz88S5YsKTARAAAAAFBteswp7a+0YcOG/PjHP86YMWO26usGDdq5mxJtf3V1G6dmjz0GFpwEYPvzdyAAUEneWwD0Lj2y8Lz44oszYMCAfOxjH9uqr2tpeT4bNrR3U6rta82adUmS5cufKzgJwPbn70AAoJK8twAol5qaPq+78LHHFZ6XXnppmpubM23atNTU9Jgz7gEAAACAKtCjCs/vfOc7+ctf/pLp06enrq6u6DgAAAAAQJXpMYXn3/72t1x77bU58MADc9ZZZyVJ9ttvv1x99dUFJwMAAAAAqkWPKTzf8pa35NFHHy06BgAAAABQxWySCQAAAACUhsITAAAAACgNhScAAAAAUBoKTwAAAACgNBSeAAAAAEBpKDwBAAAAgNLoW3QAAAA2NXfuXZkz53cVHbOtrTVJUl/fUNFxk2TUqDFpbBxd8XEBAOCNUHgCAPQCra3dV3gCAEBPovAEAOhhGhtHV3zFZFPTlCTJ5MlTKzouAAD0NPbwBAAAAABKQ+EJAAAAAJSGwhMAAAAAKA2FJwAAAABQGgpPAAAAAKA0FJ4AAAAAQGkoPAEAAACA0lB4AgAAAAClofAEAAAAAEpD4QkAAAAAlIbCEwAAAAAoDYUnAAAAAFAafYsOAAAAAED3mTv3rsyZ87uKjtnW1pokqa9vqOi4o0aNSWPj6IqOSe+j8AQAAABgq7S2dk/hCZWg8AQAAErP6iagN2tsHF3xv1eamqYkSSZPnlrRcaESFJ4AAABvgNVNANAzKTwBAIDSs7oJAHoPhSe9SjWdypQ4nQkAAABgayk8YRs5lQkAAACg51B40qs4lal6WI0LAAAAvBEKT6DXsBoXAAAAyk/hCfRIVuMCAAAAb0RN0QEAAAAAACpF4QkAAAAAlIbCEwAAAAAoDYUnAAAAAFAaCk8AAAAAoDQUngAAAABAafQtOgAAAAAA9HZz596VOXN+V/Fx29pakyT19Q0VHXfUqDFpbBxd0TErReEJAAAAACXV2to9hWdPpvAEAAAAgII1No7ulhWTTU1TkiSTJ0+t+Ng9lT08AQAAAIDSUHgCAAAAAKWh8AQAAAAASkPhCQAAAACUhsITAAAAACgNhScAAAAAUBoKTwAAAACgNBSeAAAAAEBpKDwBAAAAgNJQeAIAAAAApaHwBAAAAABKQ+EJAAAAAJSGwhMAAAAAKA2FJwAAAABQGgpPAAAAAKA0FJ4AAAAAQGkoPAEAAACA0lB4AgAAAAClofAEAAAAAEpD4QkAAAAAlIbCEwAAAAAoDYUnAAAAAFAafYsOAAAA1Wru3LsyZ87vKjpmW1trkqS+vqGi4ybJqFFj0tg4uuLjAgD0JApPAADoQVpbu6/wBADoDRSeAADwBjU2jq74ismmpilJksmTp1Z0XACA3sIengAAAABAaSg8AQAAAIDScEo7AAAAsNWq6cJtLtoGvYvCEwAAAOgRXLgNqASFJwAAALDVXLgN6Kns4QkAAAAAlIbCEwAAAAAoDYUnAAAAAFAaCk8AAAAAoDQUngAAAABAaSg8AQAAAIDSUHgCAAAAAKWh8AQAAAAASkPhCQAAAACUhsITAAAAACgNhScAAAAAUBoKTwAAAACgNBSeAAAAAEBpKDwBAAAAgNJQeAIAAAAApaHwBAAAAABKQ+EJAAAAAJRGjyo8L7300owZMyYHH3xwHnvssaLjAAAAAABVpkcVnscff3xuvPHG7LvvvkVHAQAAAACqUN+iA7zS8OHDi44AAAAAAFSxHrXCEwAAAABgW/SoFZ7batCgnYuOUDF1dRunZo89BhachM6Yq+phrqqHuYLK83NVPcxV9TBX1cNcVQ9zVT3MVfXojXNVqsKzpeX5bNjQXnSMilizZl2SZPny5wpOQmfMVfUwV9XDXEHl+bmqHuaqepir6mGuqoe5qh7mqnqUca5qavq87sJHp7QDAAAAAKXRowrPpqamHHvssXn66aczYcKEnHLKKUVHAgAAAACqSI86pX3y5MmZPHly0TEAAAAAgCrVo1Z4AgAAAABsC4UnAAAAAFAaCk8AAAAAoDQUngAAAABAafSoixYBAAC92w03zEhz8/yiY3RJc/OCJElT05Rig3TRkCFDM378hKJjAEC3U3gCAAA9RnPz/Dzy5BOpGbRn0VE61V7XL0nyWNtzBSfp3IaWZUVHAIDtRuEJAAD0KDWD9syOYz9SdIxSWT3zx0VHAIDtxh6eAAAAAEBpKDwBAAAAgNJQeAIAAAAApaHwBAAAAABKw0WL6LFuuGFGmpvnFx2jU83NC5IkTU1Tig3SRUOGDM348ROKjgEAAADQLRSe9FjNzfPT/Phfs/8utUVHeV31NRuSJBuWPVpwks4tenZ90REAAAAAupXCkx5t/11q86V3Dyg6Rmlcds+qoiMAAAAAdCuFJ7DNbD/QPWw/AAAAAFuv1xWeipnuoZjp3Zqb52f+E3/NHg1FJ3l9/f73b7znW/5abJAuWN5adAKgK6rlfUXivQUAAL1Hrys8m5vnZ+Hjf8sB9bsVHeV1NdT+79Qsbyk2SBcsbFtRdAR6gD0akg8f17P3W60mP5ttv1WoBs3N8/PIk48lg3YuOkrn6jb+8UjbkmJzdEXL80UnAACgivW6wjNJDqjfLZMbTyw6Rmk0zf110REAoDiDdk7fccOLTlEq626dV3QEAACqWK8sPAF6I6fedh+n3gIAAPQcCk+AXqK5eX6eeOKvqd+16CSde3lXj2dW9Pz9VttWFp0AAACAV1J4AvQi9bsmx9rRo6LutqsHAABAj1JTdAAAAAAAgEpReAIAAAAApaHwBAAAAABKQ+EJAAAAAJSGwhMAAAAAKA1XaQeAHuab37w4Tzzxt4qPu379uqxbt67i43aXvn37pra2sm9VDjroLZk06WsVHRMAAOhZFJ4A0MO0tCzPiy+tSp8dKjtu+/ok7ZUdszutWb8mfbKmYuO1r914bAEAgHJTeAJAD1Nf35CWLMnu4/oUHaVUnrm1PfX1DUXHAAAAupk9PAEAAACA0lB4AgAAAAClofAEAAAAAEpD4QkAAAAAlIbCEwAAAAAoDVdpBwAAYKvdcMOMNDfPLzpGlzQ3L0iSNDVNKTZIFw0ZMjTjx08oOgZA1VJ4AgAAsNWam+fnsSfnZ8d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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating the unscaled dataframe without the outliers"
      ],
      "metadata": {
        "id": "jgBRof7oqTEU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_merge = df.iloc[df_remove.index]"
      ],
      "metadata": {
        "id": "mQlQUNBLbLAN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_merge = df_merge.drop(axis=1, labels=['speechiness', 'instrumentalness'])"
      ],
      "metadata": {
        "id": "xrHrtRZMelCN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_merge.columns"
      ],
      "metadata": {
        "id": "ajdCE17ofJkY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed146ed7-d602-46dd-bdab-a35bb24f50b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['track', 'artist', 'uri', 'danceability', 'energy', 'key', 'loudness',\n",
              "       'mode', 'acousticness', 'liveness', 'valence', 'tempo', 'duration_ms',\n",
              "       'chorus_hit', 'sections', 'target'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Counting the number of observations in each class"
      ],
      "metadata": {
        "id": "8ki0FRQgrUCT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#observations in each class\n",
        "df_merge.groupby('target')[['danceability', 'energy', 'key', 'loudness',\n",
        "       'mode', 'acousticness', 'liveness', 'valence', 'tempo', 'duration_ms',\n",
        "       'chorus_hit', 'sections']].count()"
      ],
      "metadata": {
        "id": "arLiQVupZ_e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "a469aa91-c98a-41cb-970c-30e594b9e222"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        danceability  energy    key  loudness   mode  acousticness  liveness  \\\n",
              "target                                                                         \n",
              "0              15247   15247  15247     15247  15247         15247     15247   \n",
              "1              18154   18154  18154     18154  18154         18154     18154   \n",
              "\n",
              "        valence  tempo  duration_ms  chorus_hit  sections  \n",
              "target                                                     \n",
              "0         15247  15247        15247       15247     15247  \n",
              "1         18154  18154        18154       18154     18154  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2cb6a312-e45e-44dd-b800-c650c07e5a57\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>target</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></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>0</th>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "      <td>15247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "      <td>18154</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2cb6a312-e45e-44dd-b800-c650c07e5a57')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2cb6a312-e45e-44dd-b800-c650c07e5a57 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2cb6a312-e45e-44dd-b800-c650c07e5a57');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plotting the number of observations\n",
        "sns.countplot(x='target', data=df_merge)\n",
        "sns.set(rc={'figure.figsize':(12,8)})"
      ],
      "metadata": {
        "id": "dTwruG1JmHYT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 593
        },
        "outputId": "7fd6c45b-bd38-4f67-ae26-c871074cc8af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1656x720 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plotting box plots based on target variable for different predictors\n",
        "plt.rcParams[\"figure.figsize\"] = [12, 15]\n",
        "fig, axes = plt.subplots(nrows=3, ncols=4, )\n",
        "df_merge.boxplot(column= 'danceability', by='target', ax=axes[0][0], showfliers=False)\n",
        "df_merge.boxplot(column= 'energy', by='target', ax=axes[0][1], showfliers=False)\n",
        "df_merge.boxplot(column='key', by='target', ax=axes[0][2], showfliers=False)\n",
        "df_merge.boxplot(column='loudness', by='target', ax=axes[0][3], showfliers=False)\n",
        "df_merge.boxplot(column='mode', by='target', ax=axes[1][0], showfliers=False)\n",
        "df_merge.boxplot(column='acousticness', by='target', ax=axes[1][1], showfliers=False)\n",
        "df_merge.boxplot(column='liveness', by='target', ax=axes[1][2], showfliers=False)\n",
        "df_merge.boxplot(column='valence', by='target', ax=axes[1][3], showfliers=False)\n",
        "df_merge.boxplot(column='tempo', by='target', ax=axes[2][0], showfliers=False)\n",
        "df_merge.boxplot(column='duration_ms', by='target', ax=axes[2][1], showfliers=False)\n",
        "df_merge.boxplot(column='chorus_hit', by='target', ax=axes[2][2], showfliers=False)\n",
        "df_merge.boxplot(column='sections', by='target', ax=axes[2][3], showfliers=False)\n",
        "plt.tight_layout()\n",
        "plt.suptitle(None)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "SIx6XA8nmy5J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "2e44afe5-3e45-4f59-cc20-be69eac61aa4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n",
            "/usr/local/lib/python3.7/dist-packages/matplotlib/cbook/__init__.py:1376: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
            "  X = np.atleast_1d(X.T if isinstance(X, np.ndarray) else np.asarray(X))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x1080 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#correlation between the predictors\n",
        "fig, ax = plt.subplots(figsize=(11,11)) \n",
        "ax = sns.heatmap(df_remove.corr(), annot=True, cmap='RdBu').set(title='Predictors Correlation Heatmap')"
      ],
      "metadata": {
        "id": "MiviF-2-HNMf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "outputId": "69eca409-2485-40ef-8404-7b4a588df2e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 792x792 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_merge.iloc[:,3:15]"
      ],
      "metadata": {
        "id": "1mEk8QpG5h78",
        "outputId": "8ae8617c-e1ae-4397-d121-70e1425f5fe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      danceability  energy  key  loudness  mode  acousticness  liveness  \\\n",
              "0            0.578   0.471    4    -7.270     1      0.368000    0.1590   \n",
              "1            0.704   0.854   10    -5.477     0      0.018500    0.1480   \n",
              "2            0.162   0.836    9    -3.009     1      0.000111    0.1740   \n",
              "3            0.188   0.994    4    -3.745     1      0.000007    0.1920   \n",
              "4            0.630   0.764    2    -4.353     1      0.363000    0.1250   \n",
              "...            ...     ...  ...       ...   ...           ...       ...   \n",
              "5515         0.396   0.795    9    -6.070     0      0.000928    0.1820   \n",
              "5516         0.621   0.655    9    -6.281     0      0.050600    0.0937   \n",
              "5517         0.562   0.314   10   -15.213     0      0.440000    0.1060   \n",
              "5518         0.622   0.781    7    -6.080     1      0.000101    0.3830   \n",
              "5519         0.664   0.739    2    -9.005     1      0.106000    0.3330   \n",
              "\n",
              "      valence    tempo  duration_ms  chorus_hit  sections  \n",
              "0       0.532  133.061       196707    30.88059        13  \n",
              "1       0.688   92.988       242587    41.51106        10  \n",
              "2       0.300   86.964       338893    65.32887        13  \n",
              "3       0.333  148.440       255667    58.59528         9  \n",
              "4       0.631  112.098       193760    22.62384        10  \n",
              "...       ...      ...          ...         ...       ...  \n",
              "5515    0.762  152.943        82107    30.34109         6  \n",
              "5516    0.690  134.167       211653    34.89506        10  \n",
              "5517    0.571  166.847       189827    21.11763        10  \n",
              "5518    0.214  120.051       330053    47.13558        11  \n",
              "5519    0.458   92.257       293973    42.50341        14  \n",
              "\n",
              "[33401 rows x 12 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8ea0251-c5d5-4116-abf6-3af41afd0651\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>key</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>chorus_hit</th>\n",
              "      <th>sections</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.578</td>\n",
              "      <td>0.471</td>\n",
              "      <td>4</td>\n",
              "      <td>-7.270</td>\n",
              "      <td>1</td>\n",
              "      <td>0.368000</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.532</td>\n",
              "      <td>133.061</td>\n",
              "      <td>196707</td>\n",
              "      <td>30.88059</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.704</td>\n",
              "      <td>0.854</td>\n",
              "      <td>10</td>\n",
              "      <td>-5.477</td>\n",
              "      <td>0</td>\n",
              "      <td>0.018500</td>\n",
              "      <td>0.1480</td>\n",
              "      <td>0.688</td>\n",
              "      <td>92.988</td>\n",
              "      <td>242587</td>\n",
              "      <td>41.51106</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.162</td>\n",
              "      <td>0.836</td>\n",
              "      <td>9</td>\n",
              "      <td>-3.009</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000111</td>\n",
              "      <td>0.1740</td>\n",
              "      <td>0.300</td>\n",
              "      <td>86.964</td>\n",
              "      <td>338893</td>\n",
              "      <td>65.32887</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.188</td>\n",
              "      <td>0.994</td>\n",
              "      <td>4</td>\n",
              "      <td>-3.745</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000007</td>\n",
              "      <td>0.1920</td>\n",
              "      <td>0.333</td>\n",
              "      <td>148.440</td>\n",
              "      <td>255667</td>\n",
              "      <td>58.59528</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.630</td>\n",
              "      <td>0.764</td>\n",
              "      <td>2</td>\n",
              "      <td>-4.353</td>\n",
              "      <td>1</td>\n",
              "      <td>0.363000</td>\n",
              "      <td>0.1250</td>\n",
              "      <td>0.631</td>\n",
              "      <td>112.098</td>\n",
              "      <td>193760</td>\n",
              "      <td>22.62384</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5515</th>\n",
              "      <td>0.396</td>\n",
              "      <td>0.795</td>\n",
              "      <td>9</td>\n",
              "      <td>-6.070</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000928</td>\n",
              "      <td>0.1820</td>\n",
              "      <td>0.762</td>\n",
              "      <td>152.943</td>\n",
              "      <td>82107</td>\n",
              "      <td>30.34109</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5516</th>\n",
              "      <td>0.621</td>\n",
              "      <td>0.655</td>\n",
              "      <td>9</td>\n",
              "      <td>-6.281</td>\n",
              "      <td>0</td>\n",
              "      <td>0.050600</td>\n",
              "      <td>0.0937</td>\n",
              "      <td>0.690</td>\n",
              "      <td>134.167</td>\n",
              "      <td>211653</td>\n",
              "      <td>34.89506</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5517</th>\n",
              "      <td>0.562</td>\n",
              "      <td>0.314</td>\n",
              "      <td>10</td>\n",
              "      <td>-15.213</td>\n",
              "      <td>0</td>\n",
              "      <td>0.440000</td>\n",
              "      <td>0.1060</td>\n",
              "      <td>0.571</td>\n",
              "      <td>166.847</td>\n",
              "      <td>189827</td>\n",
              "      <td>21.11763</td>\n",
              "      <td>10</td>\n",
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              "    <tr>\n",
              "      <th>5518</th>\n",
              "      <td>0.622</td>\n",
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              "      <td>0.214</td>\n",
              "      <td>120.051</td>\n",
              "      <td>330053</td>\n",
              "      <td>47.13558</td>\n",
              "      <td>11</td>\n",
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              "      <th>5519</th>\n",
              "      <td>0.664</td>\n",
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              "      <td>-9.005</td>\n",
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              "      <td>0.106000</td>\n",
              "      <td>0.3330</td>\n",
              "      <td>0.458</td>\n",
              "      <td>92.257</td>\n",
              "      <td>293973</td>\n",
              "      <td>42.50341</td>\n",
              "      <td>14</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "#creating final scaled data with all the variables\n",
        "scale = StandardScaler()\n",
        "scale_X = scale.fit_transform(df_merge.iloc[:,3:15])\n",
        "df_merge.iloc[:,3:15] = scale_X"
      ],
      "metadata": {
        "id": "KG8fjk4d6_cg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "df_merge"
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        "outputId": "8ee6fe5e-bc61-4a9a-f119-cc2c6936ade1"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   track             artist  \\\n",
              "0                              Lucky Man  Montgomery Gentry   \n",
              "1                         On The Hotline       Pretty Ricky   \n",
              "2                     Clouds Of Dementia         Candlemass   \n",
              "3               Heavy Metal, Raise Hell!      Zwartketterij   \n",
              "4                        I Got A Feelin'   Billy Currington   \n",
              "...                                  ...                ...   \n",
              "5515               (You're A) Go Nowhere       Reagan Youth   \n",
              "5516                  La Fiebre de Norma       La Castañeda   \n",
              "5517                          Good Times      Edie Brickell   \n",
              "5518                               Inane              KMFDM   \n",
              "5519  You Can Make History (Young Again)         Elton John   \n",
              "\n",
              "                                       uri  danceability    energy       key  \\\n",
              "0     spotify:track:4GiXBCUF7H6YfNQsnBRIzl      0.118486 -0.518795 -0.339478   \n",
              "1     spotify:track:1zyqZONW985Cs4osz9wlsu      0.861614  1.100931  1.356353   \n",
              "2     spotify:track:6cHZf7RbxXCKwEkgAZT4mY     -2.335015  1.024808  1.073715   \n",
              "3     spotify:track:2IjBPp2vMeX7LggzRN3iSX     -2.181672  1.692998 -0.339478   \n",
              "4     spotify:track:1tF370eYXUcWwkIvaq3IGz      0.425174  0.720316 -0.904754   \n",
              "...                                    ...           ...       ...       ...   \n",
              "5515  spotify:track:4e86fqSFhqRQk3Z9hm7XHt     -0.954921  0.851417  1.073715   \n",
              "5516  spotify:track:43DFcnOZprnVlAFKwgBJ3e      0.372093  0.259350  1.073715   \n",
              "5517  spotify:track:6UPfnVoOq3y3BvapBIKs8J      0.024120 -1.182755  1.356353   \n",
              "5518  spotify:track:2Ao3Wi4raEOQfKQiU9EU8y      0.377991  0.792210  0.508438   \n",
              "5519  spotify:track:3ca91BX2k7GSzEUsx1mPgI      0.625700  0.614590 -0.904754   \n",
              "\n",
              "      loudness      mode  acousticness  liveness   valence     tempo  \\\n",
              "0     0.535085  0.651748      0.072572 -0.076165 -0.126019  0.495782   \n",
              "1     0.957657 -1.534336     -0.997974 -0.181124  0.478866 -0.953376   \n",
              "2     1.539313  0.651748     -1.054301  0.066960 -1.025591 -1.171222   \n",
              "3     1.365853  0.651748     -1.054618  0.238710 -0.897635  1.051932   \n",
              "4     1.222560  0.651748      0.057257 -0.400582  0.257851 -0.262302   \n",
              "...        ...       ...           ...       ...       ...       ...   \n",
              "5515  0.817900 -1.534336     -1.051798  0.143293  0.765799  1.214773   \n",
              "5516  0.768171 -1.534336     -0.899649 -0.699237  0.486621  0.535778   \n",
              "5517 -1.336912 -1.534336      0.293114 -0.581874  0.025203  1.717583   \n",
              "5518  0.815543  0.651748     -1.054332  2.061170 -1.359053  0.025302   \n",
              "5519  0.126182  0.651748     -0.729955  1.584086 -0.412951 -0.979811   \n",
              "\n",
              "      duration_ms  chorus_hit  sections  target  \n",
              "0       -0.315094   -0.479443  1.088622       1  \n",
              "1        0.405103    0.308361  0.031727       1  \n",
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              "3        0.610425    1.574441 -0.320571       0  \n",
              "4       -0.361354   -1.091335  0.031727       1  \n",
              "...           ...         ...       ...     ...  \n",
              "5515    -2.114016   -0.519424 -1.377467       0  \n",
              "5516    -0.080480   -0.181938  0.031727       0  \n",
              "5517    -0.423092   -1.202957  0.031727       1  \n",
              "5518     1.778092    0.725184  0.384025       0  \n",
              "5519     1.211730    0.381903  1.440921       1  \n",
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              "      <th></th>\n",
              "      <th>track</th>\n",
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              "      <th>0</th>\n",
              "      <td>Lucky Man</td>\n",
              "      <td>Montgomery Gentry</td>\n",
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              "      <td>0.535085</td>\n",
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              "      <td>0.072572</td>\n",
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              "      <td>-0.126019</td>\n",
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              "      <td>-0.479443</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>On The Hotline</td>\n",
              "      <td>Pretty Ricky</td>\n",
              "      <td>spotify:track:1zyqZONW985Cs4osz9wlsu</td>\n",
              "      <td>0.861614</td>\n",
              "      <td>1.100931</td>\n",
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              "      <td>0.957657</td>\n",
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              "      <td>-0.997974</td>\n",
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              "      <td>-0.953376</td>\n",
              "      <td>0.405103</td>\n",
              "      <td>0.308361</td>\n",
              "      <td>0.031727</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Clouds Of Dementia</td>\n",
              "      <td>Candlemass</td>\n",
              "      <td>spotify:track:6cHZf7RbxXCKwEkgAZT4mY</td>\n",
              "      <td>-2.335015</td>\n",
              "      <td>1.024808</td>\n",
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              "      <td>0.066960</td>\n",
              "      <td>-1.025591</td>\n",
              "      <td>-1.171222</td>\n",
              "      <td>1.916857</td>\n",
              "      <td>2.073455</td>\n",
              "      <td>1.088622</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Heavy Metal, Raise Hell!</td>\n",
              "      <td>Zwartketterij</td>\n",
              "      <td>spotify:track:2IjBPp2vMeX7LggzRN3iSX</td>\n",
              "      <td>-2.181672</td>\n",
              "      <td>1.692998</td>\n",
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              "      <td>1.365853</td>\n",
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              "      <td>-1.054618</td>\n",
              "      <td>0.238710</td>\n",
              "      <td>-0.897635</td>\n",
              "      <td>1.051932</td>\n",
              "      <td>0.610425</td>\n",
              "      <td>1.574441</td>\n",
              "      <td>-0.320571</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>I Got A Feelin'</td>\n",
              "      <td>Billy Currington</td>\n",
              "      <td>spotify:track:1tF370eYXUcWwkIvaq3IGz</td>\n",
              "      <td>0.425174</td>\n",
              "      <td>0.720316</td>\n",
              "      <td>-0.904754</td>\n",
              "      <td>1.222560</td>\n",
              "      <td>0.651748</td>\n",
              "      <td>0.057257</td>\n",
              "      <td>-0.400582</td>\n",
              "      <td>0.257851</td>\n",
              "      <td>-0.262302</td>\n",
              "      <td>-0.361354</td>\n",
              "      <td>-1.091335</td>\n",
              "      <td>0.031727</td>\n",
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              "    <tr>\n",
              "      <th>5515</th>\n",
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              "      <td>-0.954921</td>\n",
              "      <td>0.851417</td>\n",
              "      <td>1.073715</td>\n",
              "      <td>0.817900</td>\n",
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              "      <td>-1.051798</td>\n",
              "      <td>0.143293</td>\n",
              "      <td>0.765799</td>\n",
              "      <td>1.214773</td>\n",
              "      <td>-2.114016</td>\n",
              "      <td>-0.519424</td>\n",
              "      <td>-1.377467</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5516</th>\n",
              "      <td>La Fiebre de Norma</td>\n",
              "      <td>La Castañeda</td>\n",
              "      <td>spotify:track:43DFcnOZprnVlAFKwgBJ3e</td>\n",
              "      <td>0.372093</td>\n",
              "      <td>0.259350</td>\n",
              "      <td>1.073715</td>\n",
              "      <td>0.768171</td>\n",
              "      <td>-1.534336</td>\n",
              "      <td>-0.899649</td>\n",
              "      <td>-0.699237</td>\n",
              "      <td>0.486621</td>\n",
              "      <td>0.535778</td>\n",
              "      <td>-0.080480</td>\n",
              "      <td>-0.181938</td>\n",
              "      <td>0.031727</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>5517</th>\n",
              "      <td>Good Times</td>\n",
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              "      <td>spotify:track:6UPfnVoOq3y3BvapBIKs8J</td>\n",
              "      <td>0.024120</td>\n",
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              "      <td>-1.336912</td>\n",
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              "      <td>-0.581874</td>\n",
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              "      <td>1.717583</td>\n",
              "      <td>-0.423092</td>\n",
              "      <td>-1.202957</td>\n",
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              "      <th>5518</th>\n",
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              "      <td>0.725184</td>\n",
              "      <td>0.384025</td>\n",
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              "    <tr>\n",
              "      <th>5519</th>\n",
              "      <td>You Can Make History (Young Again)</td>\n",
              "      <td>Elton John</td>\n",
              "      <td>spotify:track:3ca91BX2k7GSzEUsx1mPgI</td>\n",
              "      <td>0.625700</td>\n",
              "      <td>0.614590</td>\n",
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              "      <td>0.126182</td>\n",
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              "      <td>1.584086</td>\n",
              "      <td>-0.412951</td>\n",
              "      <td>-0.979811</td>\n",
              "      <td>1.211730</td>\n",
              "      <td>0.381903</td>\n",
              "      <td>1.440921</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>33401 rows × 16 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ba41dd0d-dbfc-4b36-98f3-743f7b283209')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ba41dd0d-dbfc-4b36-98f3-743f7b283209 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ba41dd0d-dbfc-4b36-98f3-743f7b283209');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building the Model"
      ],
      "metadata": {
        "id": "YUTQqtQqO3UV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating X and Y"
      ],
      "metadata": {
        "id": "mYji37owVKNH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df_merge.iloc[:,3:15]\n",
        "X.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BM1ZIY3lQgW0",
        "outputId": "56a36945-29d7-44cf-840a-21208d87fed3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(33401, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_merge['target']\n",
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zGZMPp8NQCGd",
        "outputId": "a66344f4-d213-4dbb-8dbc-a90638e58315"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(33401,)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Step 1 for choosing the top models**"
      ],
      "metadata": {
        "id": "HLRJP4ojVRoP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "id": "V2oSruSsO7bs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, shuffle=True, random_state=1)"
      ],
      "metadata": {
        "id": "ig9tCBus8sge"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# First Model Training"
      ],
      "metadata": {
        "id": "pT8-XBQg8n-n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.svm import LinearSVC, SVC\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
        "from sklearn import svm\n",
        "from sklearn.model_selection import cross_val_score"
      ],
      "metadata": {
        "id": "a8IdTMgEPCJE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#training the models\n",
        "models = {\n",
        "    \"                   Logistic Regression\": LogisticRegression(),\n",
        "    \"                   K-Nearest Neighbors\": KNeighborsClassifier(),\n",
        "    \"                         Decision Tree\": DecisionTreeClassifier(),\n",
        "    \"Support Vector Machine (Linear Kernel)\": LinearSVC(),\n",
        "    \"   Support Vector Machine (RBF Kernel)\": SVC(),\n",
        "    \"                        Neural Network\": MLPClassifier(),\n",
        "    \"                         Random Forest\": RandomForestClassifier(),\n",
        "    \"                     Gradient Boosting\": GradientBoostingClassifier()\n",
        "}\n",
        "\n",
        "for name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "    print(name + \" trained.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Se49IBLPJY0",
        "outputId": "02507975-855c-4a63-9a22-3c944878b7de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   Logistic Regression trained.\n",
            "                   K-Nearest Neighbors trained.\n",
            "                         Decision Tree trained.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:1208: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Support Vector Machine (Linear Kernel) trained.\n",
            "   Support Vector Machine (RBF Kernel) trained.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        Neural Network trained.\n",
            "                         Random Forest trained.\n",
            "                     Gradient Boosting trained.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#3 fold cross validation for choosing the top models\n",
        "models = {\n",
        "    \"                   Logistic Regression\": LogisticRegression(),\n",
        "    \"                   K-Nearest Neighbors\": KNeighborsClassifier(),\n",
        "    \"                         Decision Tree\": DecisionTreeClassifier(),\n",
        "    \"Support Vector Machine (Linear Kernel)\": LinearSVC(),\n",
        "    \"   Support Vector Machine (RBF Kernel)\": SVC(),\n",
        "    \"                        Neural Network\": MLPClassifier(),\n",
        "    \"                         Random Forest\": RandomForestClassifier(),\n",
        "    \"                     Gradient Boosting\": GradientBoostingClassifier()\n",
        "}\n",
        "\n",
        "mean_df = []\n",
        "for name, model in models.items():\n",
        "  mean_arr = {}\n",
        "  cv_results = cross_val_score(model, X_train, y_train, cv=3)\n",
        "  print(name, cv_results)\n",
        "  mean = np.mean(cv_results)\n",
        "  mean_arr['name']=name\n",
        "  mean_arr['result']=mean\n",
        "  mean_df.append(mean_arr)\n",
        "  \n",
        "    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-NrAzoYPjOEW",
        "outputId": "53909909-2c27-4b2a-aca4-d9422be69827"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                   Logistic Regression [0.68092512 0.68260918 0.68100157]\n",
            "                   K-Nearest Neighbors [0.68126193 0.6813742  0.68942286]\n",
            "                         Decision Tree [0.64937689 0.64960144 0.64596901]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:1208: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/svm/_base.py:1208: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Support Vector Machine (Linear Kernel) [0.68193556 0.68272146 0.68212441]\n",
            "   Support Vector Machine (RBF Kernel) [0.73234535 0.73492759 0.73545924]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                        Neural Network [0.73268216 0.73279443 0.72838536]\n",
            "                         Random Forest [0.73762209 0.73896935 0.73253986]\n",
            "                     Gradient Boosting [0.73402942 0.73021219 0.72860993]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(mean_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "6sQLwsWhkfGp",
        "outputId": "93499672-c58c-4253-8733-dd02d608d5b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     name    result\n",
              "0                     Logistic Regression  0.681512\n",
              "1                     K-Nearest Neighbors  0.684020\n",
              "2                           Decision Tree  0.648316\n",
              "3  Support Vector Machine (Linear Kernel)  0.682260\n",
              "4     Support Vector Machine (RBF Kernel)  0.734244\n",
              "5                          Neural Network  0.731287\n",
              "6                           Random Forest  0.736377\n",
              "7                       Gradient Boosting  0.730951"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .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>name</th>\n",
              "      <th>result</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.681512</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K-Nearest Neighbors</td>\n",
              "      <td>0.684020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.648316</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Support Vector Machine (Linear Kernel)</td>\n",
              "      <td>0.682260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Support Vector Machine (RBF Kernel)</td>\n",
              "      <td>0.734244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Neural Network</td>\n",
              "      <td>0.731287</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.736377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.730951</td>\n",
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              "  </tbody>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e2826192-05b0-40c4-96e2-7d628c6e78ce');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the first step we choose the top 4 models.\n",
        "\n"
      ],
      "metadata": {
        "id": "XrLy9FjHTdfq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **5 fold Cross Validation for choosing the best model**"
      ],
      "metadata": {
        "id": "QeBSK7HZaDiw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Support Vector Machine"
      ],
      "metadata": {
        "id": "Z6DRt0ccaB--"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Support vector machine\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "clf = SVC()\n",
        "cv_results = cross_val_score(clf, X, y, cv=5)\n",
        "cv_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDg7WKFMXL5M",
        "outputId": "de3bf391-da00-4906-c2d5-c8113e1e2db9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.7536297 , 0.71901198, 0.68203593, 0.70778443, 0.73203593])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(cv_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "81Fm4aMlYWWu",
        "outputId": "63860c7a-c802-489f-8553-62fd931af693"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.718899591925265"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy of support vector machine model is 0.71"
      ],
      "metadata": {
        "id": "vcd9jG7BYxos"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Neural Networks"
      ],
      "metadata": {
        "id": "ZELysTiqa8nI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "cv_results = cross_val_score(clf, X, y, cv=5)\n",
        "cv_results = cross_val_score(clf, X, y, cv=5)\n",
        "cv_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "19Z-N_GUeWSa",
        "outputId": "042879e2-77a3-46a4-aeb7-857f77758e67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:696: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  ConvergenceWarning,\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.7597665 , 0.71946108, 0.66511976, 0.70194611, 0.74026946])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(cv_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PIdsfDfVgFNm",
        "outputId": "5044f73c-c723-4cb3-ccb5-7c2ce5f6448d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7173125818412569"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy for neural network model is 0.71"
      ],
      "metadata": {
        "id": "dlWq7XFYY6lh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Forest**"
      ],
      "metadata": {
        "id": "0rgvooH6ub4u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "random_grid = {'bootstrap': [True, False],\n",
        " 'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, None],\n",
        " 'max_features': ['auto', 'sqrt'],\n",
        " 'min_samples_leaf': [1, 2, 4],\n",
        " 'min_samples_split': [2, 5, 10],\n",
        " 'n_estimators': [200, 400, 600, 800, 1000, 1200, 1400, 1600, 1800, 2000]}\n",
        "\n",
        "rand = RandomizedSearchCV(RandomForestClassifier(), param_distributions = random_grid, n_iter = 1, cv = 2)\n",
        "rand.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "BupOn9_KubaN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8dee2159-dd1d-4d71-fdb6-93e106aa7260"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomizedSearchCV(cv=2, estimator=RandomForestClassifier(), n_iter=1,\n",
              "                   param_distributions={'bootstrap': [True, False],\n",
              "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
              "                                                      70, 80, 90, 100, None],\n",
              "                                        'max_features': ['auto', 'sqrt'],\n",
              "                                        'min_samples_leaf': [1, 2, 4],\n",
              "                                        'min_samples_split': [2, 5, 10],\n",
              "                                        'n_estimators': [200, 400, 600, 800,\n",
              "                                                         1000, 1200, 1400, 1600,\n",
              "                                                         1800, 2000]})"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest\n",
        "\n",
        "from sklearn.model_selection import cross_val_score\n",
        "m = RandomForestClassifier()\n",
        "print(cross_val_score(RandomForestClassifier(),X_train, y_train,cv=5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lr5T1r7QJdcV",
        "outputId": "9027aa90-e31c-4b27-edea-f6b89e3bed97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.74438623 0.72735778 0.7369012  0.74195359 0.7376497 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(([0.74513473, 0.72922904, 0.74045659, 0.74326347, 0.74195359]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "euMeiPDaZFD0",
        "outputId": "d7c402e5-1798-4426-bffa-34ddd5ba73fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.740007484"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy for the random forest model is 0.74"
      ],
      "metadata": {
        "id": "5pDmndldZuJg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gradient Boosting"
      ],
      "metadata": {
        "id": "RTMOq6j5aLF1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#gradient boosting\n",
        "\n",
        "clf = GradientBoostingClassifier()\n",
        "cv_results = cross_val_score(clf, X, y, cv=5)\n",
        "cv_results\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CGOz3OezaNIG",
        "outputId": "b7d0883f-8557-4900-e16e-2ab19d9b57d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.75886843, 0.71721557, 0.65643713, 0.7005988 , 0.73173653])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(cv_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgaaIkCcdaZQ",
        "outputId": "971b021b-9934-47eb-82c9-bdc239e53a2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7129712913642854"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy for the gradient boosting model is 0.71"
      ],
      "metadata": {
        "id": "oxY0QBPwddrJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We choose Random Forest as our final model."
      ],
      "metadata": {
        "id": "t2PUEs1Udh45"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Implementation Part 2: Random Forest Model**"
      ],
      "metadata": {
        "id": "BUXQdSKsh916"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DKG1UkB9h8Ks",
        "outputId": "0b36004a-b7ec-4085-baa4-c29569987211"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yzbpx2v3pT4O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importance of the variables**"
      ],
      "metadata": {
        "id": "bT-DK7c2ntr5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#feature importance\n",
        "importances = list(rf.feature_importances_)\n",
        "\n",
        "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(X.columns, importances)]\n",
        "feature_importances"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ujxn8AZkixG",
        "outputId": "e1c5fb0f-d3b9-46a8-c3ac-8b44d9594431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[('danceability', 0.13),\n",
              " ('energy', 0.11),\n",
              " ('key', 0.04),\n",
              " ('loudness', 0.1),\n",
              " ('mode', 0.01),\n",
              " ('acousticness', 0.15),\n",
              " ('liveness', 0.07),\n",
              " ('valence', 0.09),\n",
              " ('tempo', 0.07),\n",
              " ('duration_ms', 0.11),\n",
              " ('chorus_hit', 0.07),\n",
              " ('sections', 0.04)]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plotting feature importance\n",
        "feat_importances_plot = pd.Series(rf.feature_importances_, index=X.columns)\n",
        "feat_importances_plot.plot(kind='barh')\n",
        "plt.ylabel(\"Predictors\")\n",
        "plt.xlabel(\"Feature Importance\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 901
        },
        "id": "vSnQ-lm0q8Dc",
        "outputId": "ac31c8af-9f21-49db-a3c5-5d25d3a03b7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'Feature Importance')"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x1080 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#dropping 3 less important predictors\n",
        "X_reduced = X.drop(labels=['key', 'mode', 'sections'], axis=1)\n",
        "X_reduced"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "pDuKqZcJlPRg",
        "outputId": "d5cf3782-2b08-4ce7-ad6b-19c92cd59bb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      danceability    energy  loudness  acousticness  liveness   valence  \\\n",
              "0         0.118486 -0.518795  0.535085      0.072572 -0.076165 -0.126019   \n",
              "1         0.861614  1.100931  0.957657     -0.997974 -0.181124  0.478866   \n",
              "2        -2.335015  1.024808  1.539313     -1.054301  0.066960 -1.025591   \n",
              "3        -2.181672  1.692998  1.365853     -1.054618  0.238710 -0.897635   \n",
              "4         0.425174  0.720316  1.222560      0.057257 -0.400582  0.257851   \n",
              "...            ...       ...       ...           ...       ...       ...   \n",
              "5515     -0.954921  0.851417  0.817900     -1.051798  0.143293  0.765799   \n",
              "5516      0.372093  0.259350  0.768171     -0.899649 -0.699237  0.486621   \n",
              "5517      0.024120 -1.182755 -1.336912      0.293114 -0.581874  0.025203   \n",
              "5518      0.377991  0.792210  0.815543     -1.054332  2.061170 -1.359053   \n",
              "5519      0.625700  0.614590  0.126182     -0.729955  1.584086 -0.412951   \n",
              "\n",
              "         tempo  duration_ms  chorus_hit  \n",
              "0     0.495782    -0.315094   -0.479443  \n",
              "1    -0.953376     0.405103    0.308361  \n",
              "2    -1.171222     1.916857    2.073455  \n",
              "3     1.051932     0.610425    1.574441  \n",
              "4    -0.262302    -0.361354   -1.091335  \n",
              "...        ...          ...         ...  \n",
              "5515  1.214773    -2.114016   -0.519424  \n",
              "5516  0.535778    -0.080480   -0.181938  \n",
              "5517  1.717583    -0.423092   -1.202957  \n",
              "5518  0.025302     1.778092    0.725184  \n",
              "5519 -0.979811     1.211730    0.381903  \n",
              "\n",
              "[33401 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-98d0f962-05d9-4dad-9aad-ba7b84448881\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>loudness</th>\n",
              "      <th>acousticness</th>\n",
              "      <th>liveness</th>\n",
              "      <th>valence</th>\n",
              "      <th>tempo</th>\n",
              "      <th>duration_ms</th>\n",
              "      <th>chorus_hit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.118486</td>\n",
              "      <td>-0.518795</td>\n",
              "      <td>0.535085</td>\n",
              "      <td>0.072572</td>\n",
              "      <td>-0.076165</td>\n",
              "      <td>-0.126019</td>\n",
              "      <td>0.495782</td>\n",
              "      <td>-0.315094</td>\n",
              "      <td>-0.479443</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.861614</td>\n",
              "      <td>1.100931</td>\n",
              "      <td>0.957657</td>\n",
              "      <td>-0.997974</td>\n",
              "      <td>-0.181124</td>\n",
              "      <td>0.478866</td>\n",
              "      <td>-0.953376</td>\n",
              "      <td>0.405103</td>\n",
              "      <td>0.308361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-2.335015</td>\n",
              "      <td>1.024808</td>\n",
              "      <td>1.539313</td>\n",
              "      <td>-1.054301</td>\n",
              "      <td>0.066960</td>\n",
              "      <td>-1.025591</td>\n",
              "      <td>-1.171222</td>\n",
              "      <td>1.916857</td>\n",
              "      <td>2.073455</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-2.181672</td>\n",
              "      <td>1.692998</td>\n",
              "      <td>1.365853</td>\n",
              "      <td>-1.054618</td>\n",
              "      <td>0.238710</td>\n",
              "      <td>-0.897635</td>\n",
              "      <td>1.051932</td>\n",
              "      <td>0.610425</td>\n",
              "      <td>1.574441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.425174</td>\n",
              "      <td>0.720316</td>\n",
              "      <td>1.222560</td>\n",
              "      <td>0.057257</td>\n",
              "      <td>-0.400582</td>\n",
              "      <td>0.257851</td>\n",
              "      <td>-0.262302</td>\n",
              "      <td>-0.361354</td>\n",
              "      <td>-1.091335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5515</th>\n",
              "      <td>-0.954921</td>\n",
              "      <td>0.851417</td>\n",
              "      <td>0.817900</td>\n",
              "      <td>-1.051798</td>\n",
              "      <td>0.143293</td>\n",
              "      <td>0.765799</td>\n",
              "      <td>1.214773</td>\n",
              "      <td>-2.114016</td>\n",
              "      <td>-0.519424</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5516</th>\n",
              "      <td>0.372093</td>\n",
              "      <td>0.259350</td>\n",
              "      <td>0.768171</td>\n",
              "      <td>-0.899649</td>\n",
              "      <td>-0.699237</td>\n",
              "      <td>0.486621</td>\n",
              "      <td>0.535778</td>\n",
              "      <td>-0.080480</td>\n",
              "      <td>-0.181938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5517</th>\n",
              "      <td>0.024120</td>\n",
              "      <td>-1.182755</td>\n",
              "      <td>-1.336912</td>\n",
              "      <td>0.293114</td>\n",
              "      <td>-0.581874</td>\n",
              "      <td>0.025203</td>\n",
              "      <td>1.717583</td>\n",
              "      <td>-0.423092</td>\n",
              "      <td>-1.202957</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5518</th>\n",
              "      <td>0.377991</td>\n",
              "      <td>0.792210</td>\n",
              "      <td>0.815543</td>\n",
              "      <td>-1.054332</td>\n",
              "      <td>2.061170</td>\n",
              "      <td>-1.359053</td>\n",
              "      <td>0.025302</td>\n",
              "      <td>1.778092</td>\n",
              "      <td>0.725184</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5519</th>\n",
              "      <td>0.625700</td>\n",
              "      <td>0.614590</td>\n",
              "      <td>0.126182</td>\n",
              "      <td>-0.729955</td>\n",
              "      <td>1.584086</td>\n",
              "      <td>-0.412951</td>\n",
              "      <td>-0.979811</td>\n",
              "      <td>1.211730</td>\n",
              "      <td>0.381903</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>33401 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-98d0f962-05d9-4dad-9aad-ba7b84448881')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-98d0f962-05d9-4dad-9aad-ba7b84448881 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-98d0f962-05d9-4dad-9aad-ba7b84448881');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_red_train, X_red_test, y_red_train , y_red_test = train_test_split(X_reduced, y, test_size=0.2, random_state=1)"
      ],
      "metadata": {
        "id": "MWZ1S6Wpl0F8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_score(RandomForestClassifier(),X_red_train, y_red_train,cv=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gCXUc2VDpmYm",
        "outputId": "185b5358-643c-4fdb-d2b0-74a422149794"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.73409431, 0.72735778, 0.73671407, 0.73353293, 0.73484281])"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean([0.73409431, 0.72735778, 0.73671407, 0.73353293, 0.73484281])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RITs09WsqVSL",
        "outputId": "ac8e5adb-2969-46c9-8e8e-619ad7b9e176"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.73330838"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The accuracy is decreased by 0.006 and the running time is reduced by 1 second, therefore we will not use this trade off in our model and we will continue to use all the features."
      ],
      "metadata": {
        "id": "P3lS4MQ2nE75"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**PCA**"
      ],
      "metadata": {
        "id": "WErIjC12nyA-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca_test = PCA(n_components=12)\n",
        "pca_test.fit(X_train)\n",
        "sns.set(style='whitegrid')\n",
        "sns.set(rc={'figure.figsize':(8,6)})\n",
        "\n",
        "plt.plot(np.cumsum(pca_test.explained_variance_ratio_))\n",
        "plt.xlabel('number of components')\n",
        "plt.ylabel('cumulative explained variance')\n",
        "display(plt.show())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "5sTZmvcin0jg",
        "outputId": "cfdf52ee-794e-43cb-a3f3-d96789a7a241"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "None"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We Can use 7 principle components to capture 90% of the total variance."
      ],
      "metadata": {
        "id": "ltEm_mqPp-hk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pca = PCA(n_components=7)\n",
        "pca.fit(X_train)\n",
        "X_train_pca = pca.transform(X_train)\n",
        "X_test_pca = pca.transform(X_test)"
      ],
      "metadata": {
        "id": "VMP9PY2WqPXP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cross_val_score(RandomForestClassifier(),X_train_pca, y_train,cv=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ANwNYFcQquLv",
        "outputId": "a3d3e140-dd38-4a55-cf77-579bc66bb348"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.68712575, 0.68731287, 0.67477545, 0.68787425, 0.6925524 ])"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean([0.68712575, 0.68731287, 0.67477545, 0.68787425, 0.6925524 ])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJ5LAYyytACG",
        "outputId": "c2df7bc9-26a3-45fa-b99e-63d4198fa73f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6859281439999999"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "It can be seen that the performance of the model is not enhanced with dimension reduction."
      ],
      "metadata": {
        "id": "rfbSKN9HrFDX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hyperparameter"
      ],
      "metadata": {
        "id": "fDt9a8W6m0lf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "\n",
        "#number of trees in the model\n",
        "n_estimators = [int(x) for x in np.linspace(start = 100, stop = 1000, num = 10)]\n",
        "\n",
        "#maximum number of splits for each tree\n",
        "max_depth = [int(x) for x in np.linspace(start = 1, stop = 15, num = 15)]\n",
        "\n",
        "#minimum number of cases required before spliting\n",
        "min_samples_split = [int(x) for x in np.linspace(start = 2, stop = 50, num = 10)]\n",
        "\n",
        "#minimum number required in each leaf at the end\n",
        "min_samples_leaf = [int(x) for x in np.linspace(start = 2, stop = 50, num = 10)]\n",
        "\n",
        "bootstrap = [True, False]\n",
        "\n",
        "param_dist = {'n_estimators': n_estimators,\n",
        "               'max_depth': max_depth,\n",
        "               'min_samples_split': min_samples_split,\n",
        "               'min_samples_leaf': min_samples_leaf,\n",
        "               'bootstrap': bootstrap}\n",
        "\n",
        "               "
      ],
      "metadata": {
        "id": "puFaqr7Zrf54"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rf_rs = RandomizedSearchCV(rf, \n",
        "                        param_dist, \n",
        "                        n_iter = 40, \n",
        "                        cv = 3, \n",
        "                        n_jobs=-1, \n",
        "                        random_state=1)\n",
        "rf_rs.fit(X_train, y_train)\n",
        "rf_rs.best_params_\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1lDnuSt8sg1B",
        "outputId": "eeafea1f-4951-40e4-c3e2-b25a975b0069"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': False,\n",
              " 'max_depth': 13,\n",
              " 'min_samples_leaf': 12,\n",
              " 'min_samples_split': 2,\n",
              " 'n_estimators': 500}"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_df = pd.DataFrame(rf_rs.cv_results_).sort_values('rank_test_score').reset_index(drop=True)\n",
        "random_df = random_df.drop([\n",
        "            'mean_fit_time', \n",
        "            'std_fit_time', \n",
        "            'mean_score_time',\n",
        "            'std_score_time', \n",
        "            'params', \n",
        "            'split0_test_score', \n",
        "            'split1_test_score', \n",
        "            'split2_test_score', \n",
        "            'std_test_score'],\n",
        "            axis=1)"
      ],
      "metadata": {
        "id": "Gf0TEdxVft-L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axs = plt.subplots(ncols=3, nrows=2)\n",
        "sns.set(style=\"whitegrid\", color_codes=True, font_scale = 1)\n",
        "fig.set_size_inches(18,18)\n",
        "\n",
        "sns.barplot(x='param_n_estimators', y='mean_test_score', data=random_df, ax=axs[0,0], color='lightgrey')\n",
        "axs[0,0].set_ylim([.65,.75])\n",
        "axs[0,0].set_title(label = 'n_estimators', size=10, weight='bold')\n",
        "\n",
        "sns.barplot(x='param_min_samples_split', y='mean_test_score', data=random_df, ax=axs[0,1], color='coral')\n",
        "axs[0,1].set_ylim([.65,.75])\n",
        "axs[0,1].set_title(label = 'min_samples_split', size=10, weight='bold')\n",
        "\n",
        "sns.barplot(x='param_min_samples_leaf', y='mean_test_score', data=random_df, ax=axs[0,2], color='lightgreen')\n",
        "axs[0,2].set_ylim([.65,.75])\n",
        "axs[0,2].set_title(label = 'min_samples_leaf', size=10, weight='bold')\n",
        "\n",
        "\n",
        "sns.barplot(x='param_max_depth', y='mean_test_score', data=random_df, ax=axs[1,1], color='lightpink')\n",
        "axs[1,1].set_ylim([.65,.75])\n",
        "axs[1,1].set_title(label = 'max_depth', size=10, weight='bold')\n",
        "\n",
        "sns.barplot(x='param_bootstrap',y='mean_test_score', data=random_df, ax=axs[1,2], color='skyblue')\n",
        "axs[1,2].set_ylim([.65,.75])\n",
        "axs[1,2].set_title(label = 'bootstrap', size=10, weight='bold')\n",
        "\n",
        "axs[1,0].set_axis_off()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ITCjDmzJgXuE",
        "outputId": "ef25b81e-b7db-4594-afb5-8ac10923a8ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x1296 with 6 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the results for the number of estimators we choose [400,900].\n",
        "For min sample split we choose [2,12,28].\n",
        "For min sample leaf we choose [28,34].\n",
        "For max depth we choose [12,13].\n",
        "And we choose bootstrap False."
      ],
      "metadata": {
        "id": "JT4o8kvqjNOF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grid = {'bootstrap': [False],\n",
        " 'max_depth': [12,13],\n",
        " 'max_features': ['auto', 'sqrt'],\n",
        " 'min_samples_leaf': [28, 34],\n",
        " 'min_samples_split': [2, 12, 28],\n",
        " 'n_estimators': [ 400, 900]}"
      ],
      "metadata": {
        "id": "FnpCCrmHkAWf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gs = GridSearchCV(rf, grid, cv = 3, verbose = 1, n_jobs=-1)\n",
        "gs.fit(X_train, y_train)\n",
        "rfc_3 = gs.best_estimator_\n",
        "gs.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UoQfY6AWkfco",
        "outputId": "c21a4fe5-c8b7-4e7f-c918-62577f8bc5bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 48 candidates, totalling 144 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': False,\n",
              " 'max_depth': 13,\n",
              " 'max_features': 'sqrt',\n",
              " 'min_samples_leaf': 28,\n",
              " 'min_samples_split': 12,\n",
              " 'n_estimators': 400}"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_gs = gs.best_estimator_.predict(X_test)\n",
        "accuracy_score(y_test,y_pred_gs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAzgf3dH87Ib",
        "outputId": "023fc9c2-1029-48a6-c91c-20cc971075cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7307289327944918"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(y_test == 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DRFPhThU-noB",
        "outputId": "10642921-d669-449b-9cd2-5c5dcfd8cf6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3606"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Performance Evaluation and Interpretation"
      ],
      "metadata": {
        "id": "5AUksX-JCiu2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing Accuracy score, confusion matrix and Classification report"
      ],
      "metadata": {
        "id": "sq02Q7fEHvCu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "kxJQqjfDCre4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating accuracy scores"
      ],
      "metadata": {
        "id": "Wi49OR8sH0rQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izVcgAiRDUQ7",
        "outputId": "5b06a998-26b9-4aba-9434-c86484e1087e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7353689567430025"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "View Confusion Matrix for test data and the predictions"
      ],
      "metadata": {
        "id": "GFu5PdUYH6rP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test, y_pred, labels=[1, 0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EnLcy01YDYwO",
        "outputId": "e0c4b214-579a-445d-b9ea-92654aaf0d8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2935,  671],\n",
              "       [1097, 1978]])"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Printing the classification report for test data and the predictions"
      ],
      "metadata": {
        "id": "luxq_0yQIEoS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6IzKJdKDkJd",
        "outputId": "3dc753a2-06cb-47bf-b5bf-5a24f9da4915"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.64      0.69      3075\n",
            "           1       0.73      0.81      0.77      3606\n",
            "\n",
            "    accuracy                           0.74      6681\n",
            "   macro avg       0.74      0.73      0.73      6681\n",
            "weighted avg       0.74      0.74      0.73      6681\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_prob= rf.predict_proba((X_test))"
      ],
      "metadata": {
        "id": "9u8H8Gc_vUXt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prob_1 = y_prob[::,1]"
      ],
      "metadata": {
        "id": "XwNlC3aDwsGP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  prob_1)\n",
        "auc = metrics.roc_auc_score(y_test, prob_1)\n",
        "\n",
        "#create ROC curve\n",
        "plt.plot(fpr,tpr, label=\"AUC=\"+str(auc))\n",
        "plt.ylabel('Sensitivity')\n",
        "plt.xlabel('1-Specificity')\n",
        "plt.legend(loc=4)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "yekwsTyACfKI",
        "outputId": "7a70d22a-31e0-40d1-964a-c589913eb7d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}