From 425fb2f9c1c3e8c3aba8286697cc965fec3fa102 Mon Sep 17 00:00:00 2001
From: Pooja8428 <poojapgotarne@gmail.com>
Date: Thu, 25 Aug 2022 11:45:37 +0530
Subject: [PATCH] updated

---
 Project - song population.ipynb | 2764 +++++++++++++++++++++++++++++++
 1 file changed, 2764 insertions(+)
 create mode 100644 Project - song population.ipynb

diff --git a/Project - song population.ipynb b/Project - song population.ipynb
new file mode 100644
index 0000000..851a66a
--- /dev/null
+++ b/Project - song population.ipynb	
@@ -0,0 +1,2764 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "Untitled6.ipynb",
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dbjvacVW0eJA"
+      },
+      "source": [
+        "#**Song Popularity Prediction**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5Or8mhAYznV_"
+      },
+      "outputs": [],
+      "source": [
+        "dataset_url = 'https://www.kaggle.com/yamaerenay/spotify-dataset-19212020-160k-tracks'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bE-Zboe41L_-",
+        "outputId": "f4dfa086-7300-4e7f-a8f5-f5c78d2dc429"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "['dict_artists.json',\n",
+              " 'artists.csv',\n",
+              " 'data_by_artists_o.csv',\n",
+              " 'data_by_year_o.csv',\n",
+              " '.ipynb_checkpoints',\n",
+              " 'tracks.csv',\n",
+              " 'data_o.csv',\n",
+              " 'data_by_genres_o.csv']"
+            ]
+          },
+          "execution_count": 6,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import os\n",
+        "os.listdir(data_dir)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0kFDmegMFm-C"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 643
+        },
+        "id": "vJME3YFPFsUR",
+        "outputId": "b450539e-19ac-45c6-dd28-fc53ededc4f4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>id</th>\n",
+              "      <th>name</th>\n",
+              "      <th>popularity</th>\n",
+              "      <th>duration_ms</th>\n",
+              "      <th>explicit</th>\n",
+              "      <th>artists</th>\n",
+              "      <th>id_artists</th>\n",
+              "      <th>release_date</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>time_signature</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>35iwgR4jXetI318WEWsa1Q</td>\n",
+              "      <td>Carve</td>\n",
+              "      <td>6</td>\n",
+              "      <td>126903</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['Uli']</td>\n",
+              "      <td>['45tIt06XoI0Iio4LBEVpls']</td>\n",
+              "      <td>1922-02-22</td>\n",
+              "      <td>0.645</td>\n",
+              "      <td>0.4450</td>\n",
+              "      <td>0</td>\n",
+              "      <td>-13.338</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.4510</td>\n",
+              "      <td>0.674</td>\n",
+              "      <td>0.744000</td>\n",
+              "      <td>0.1510</td>\n",
+              "      <td>0.1270</td>\n",
+              "      <td>104.851</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>021ht4sdgPcrDgSk7JTbKY</td>\n",
+              "      <td>Capítulo 2.16 - Banquero Anarquista</td>\n",
+              "      <td>0</td>\n",
+              "      <td>98200</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['Fernando Pessoa']</td>\n",
+              "      <td>['14jtPCOoNZwquk5wd9DxrY']</td>\n",
+              "      <td>1922-06-01</td>\n",
+              "      <td>0.695</td>\n",
+              "      <td>0.2630</td>\n",
+              "      <td>0</td>\n",
+              "      <td>-22.136</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.9570</td>\n",
+              "      <td>0.797</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.1480</td>\n",
+              "      <td>0.6550</td>\n",
+              "      <td>102.009</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>07A5yehtSnoedViJAZkNnc</td>\n",
+              "      <td>Vivo para Quererte - Remasterizado</td>\n",
+              "      <td>0</td>\n",
+              "      <td>181640</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['Ignacio Corsini']</td>\n",
+              "      <td>['5LiOoJbxVSAMkBS2fUm3X2']</td>\n",
+              "      <td>1922-03-21</td>\n",
+              "      <td>0.434</td>\n",
+              "      <td>0.1770</td>\n",
+              "      <td>1</td>\n",
+              "      <td>-21.180</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0512</td>\n",
+              "      <td>0.994</td>\n",
+              "      <td>0.021800</td>\n",
+              "      <td>0.2120</td>\n",
+              "      <td>0.4570</td>\n",
+              "      <td>130.418</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>08FmqUhxtyLTn6pAh6bk45</td>\n",
+              "      <td>El Prisionero - Remasterizado</td>\n",
+              "      <td>0</td>\n",
+              "      <td>176907</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['Ignacio Corsini']</td>\n",
+              "      <td>['5LiOoJbxVSAMkBS2fUm3X2']</td>\n",
+              "      <td>1922-03-21</td>\n",
+              "      <td>0.321</td>\n",
+              "      <td>0.0946</td>\n",
+              "      <td>7</td>\n",
+              "      <td>-27.961</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0504</td>\n",
+              "      <td>0.995</td>\n",
+              "      <td>0.918000</td>\n",
+              "      <td>0.1040</td>\n",
+              "      <td>0.3970</td>\n",
+              "      <td>169.980</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>08y9GfoqCWfOGsKdwojr5e</td>\n",
+              "      <td>Lady of the Evening</td>\n",
+              "      <td>0</td>\n",
+              "      <td>163080</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['Dick Haymes']</td>\n",
+              "      <td>['3BiJGZsyX9sJchTqcSA7Su']</td>\n",
+              "      <td>1922</td>\n",
+              "      <td>0.402</td>\n",
+              "      <td>0.1580</td>\n",
+              "      <td>3</td>\n",
+              "      <td>-16.900</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0390</td>\n",
+              "      <td>0.989</td>\n",
+              "      <td>0.130000</td>\n",
+              "      <td>0.3110</td>\n",
+              "      <td>0.1960</td>\n",
+              "      <td>103.220</td>\n",
+              "      <td>4</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",
+              "      <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>586667</th>\n",
+              "      <td>5rgu12WBIHQtvej2MdHSH0</td>\n",
+              "      <td>云与海</td>\n",
+              "      <td>50</td>\n",
+              "      <td>258267</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['阿YueYue']</td>\n",
+              "      <td>['1QLBXKM5GCpyQQSVMNZqrZ']</td>\n",
+              "      <td>2020-09-26</td>\n",
+              "      <td>0.560</td>\n",
+              "      <td>0.5180</td>\n",
+              "      <td>0</td>\n",
+              "      <td>-7.471</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0292</td>\n",
+              "      <td>0.785</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.0648</td>\n",
+              "      <td>0.2110</td>\n",
+              "      <td>131.896</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>586668</th>\n",
+              "      <td>0NuWgxEp51CutD2pJoF4OM</td>\n",
+              "      <td>blind</td>\n",
+              "      <td>72</td>\n",
+              "      <td>153293</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['ROLE MODEL']</td>\n",
+              "      <td>['1dy5WNgIKQU6ezkpZs4y8z']</td>\n",
+              "      <td>2020-10-21</td>\n",
+              "      <td>0.765</td>\n",
+              "      <td>0.6630</td>\n",
+              "      <td>0</td>\n",
+              "      <td>-5.223</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0652</td>\n",
+              "      <td>0.141</td>\n",
+              "      <td>0.000297</td>\n",
+              "      <td>0.0924</td>\n",
+              "      <td>0.6860</td>\n",
+              "      <td>150.091</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>586669</th>\n",
+              "      <td>27Y1N4Q4U3EfDU5Ubw8ws2</td>\n",
+              "      <td>What They'll Say About Us</td>\n",
+              "      <td>70</td>\n",
+              "      <td>187601</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['FINNEAS']</td>\n",
+              "      <td>['37M5pPGs6V1fchFJSgCguX']</td>\n",
+              "      <td>2020-09-02</td>\n",
+              "      <td>0.535</td>\n",
+              "      <td>0.3140</td>\n",
+              "      <td>7</td>\n",
+              "      <td>-12.823</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0.0408</td>\n",
+              "      <td>0.895</td>\n",
+              "      <td>0.000150</td>\n",
+              "      <td>0.0874</td>\n",
+              "      <td>0.0663</td>\n",
+              "      <td>145.095</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>586670</th>\n",
+              "      <td>45XJsGpFTyzbzeWK8VzR8S</td>\n",
+              "      <td>A Day At A Time</td>\n",
+              "      <td>58</td>\n",
+              "      <td>142003</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['Gentle Bones', 'Clara Benin']</td>\n",
+              "      <td>['4jGPdu95icCKVF31CcFKbS', '5ebPSE9YI5aLeZ1Z2g...</td>\n",
+              "      <td>2021-03-05</td>\n",
+              "      <td>0.696</td>\n",
+              "      <td>0.6150</td>\n",
+              "      <td>10</td>\n",
+              "      <td>-6.212</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0345</td>\n",
+              "      <td>0.206</td>\n",
+              "      <td>0.000003</td>\n",
+              "      <td>0.3050</td>\n",
+              "      <td>0.4380</td>\n",
+              "      <td>90.029</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>586671</th>\n",
+              "      <td>5Ocn6dZ3BJFPWh4ylwFXtn</td>\n",
+              "      <td>Mar de Emociones</td>\n",
+              "      <td>38</td>\n",
+              "      <td>214360</td>\n",
+              "      <td>0</td>\n",
+              "      <td>['Afrosound']</td>\n",
+              "      <td>['0i4Qda0k4nf7jnNHmSNpYv']</td>\n",
+              "      <td>2015-07-01</td>\n",
+              "      <td>0.686</td>\n",
+              "      <td>0.7230</td>\n",
+              "      <td>6</td>\n",
+              "      <td>-7.067</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0.0363</td>\n",
+              "      <td>0.105</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>0.2640</td>\n",
+              "      <td>0.9750</td>\n",
+              "      <td>112.204</td>\n",
+              "      <td>4</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>586672 rows × 20 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                            id  ... time_signature\n",
+              "0       35iwgR4jXetI318WEWsa1Q  ...              3\n",
+              "1       021ht4sdgPcrDgSk7JTbKY  ...              1\n",
+              "2       07A5yehtSnoedViJAZkNnc  ...              5\n",
+              "3       08FmqUhxtyLTn6pAh6bk45  ...              3\n",
+              "4       08y9GfoqCWfOGsKdwojr5e  ...              4\n",
+              "...                        ...  ...            ...\n",
+              "586667  5rgu12WBIHQtvej2MdHSH0  ...              4\n",
+              "586668  0NuWgxEp51CutD2pJoF4OM  ...              4\n",
+              "586669  27Y1N4Q4U3EfDU5Ubw8ws2  ...              4\n",
+              "586670  45XJsGpFTyzbzeWK8VzR8S  ...              4\n",
+              "586671  5Ocn6dZ3BJFPWh4ylwFXtn  ...              4\n",
+              "\n",
+              "[586672 rows x 20 columns]"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df = pd.read_csv(data_dir + '/tracks.csv')\n",
+        "tracks_df"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TmfEBjUmEJ7c"
+      },
+      "source": [
+        "## Exploratory Data Analysis and Visualization"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LqqQMqvsJEeV"
+      },
+      "outputs": [],
+      "source": [
+        "import seaborn as sns\n",
+        "import matplotlib\n",
+        "import matplotlib.pyplot as plt\n",
+        "import numpy as np\n",
+        "import plotly.express as px\n",
+        "%matplotlib inline\n",
+        "\n",
+        "sns.set_style('darkgrid')\n",
+        "matplotlib.rcParams['font.size'] = 14\n",
+        "matplotlib.rcParams['figure.figsize'] = (12, 6)\n",
+        "matplotlib.rcParams['figure.facecolor'] = '#00000000'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 506
+        },
+        "id": "gIsCeUlqIIBW",
+        "outputId": "763b0847-ce14-4a36-c716-e5433707ce56"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 864x432 with 2 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "corr = tracks_df[['danceability', \n",
+        "                  'energy', \n",
+        "                  'loudness', \n",
+        "                  'speechiness', \n",
+        "                  'acousticness', \n",
+        "                  'instrumentalness', \n",
+        "                  'liveness',\n",
+        "                  'valence', \n",
+        "                  'tempo' ]].corr()\n",
+        "\n",
+        "plt.title('Correlation between the audio features')\n",
+        "sns.heatmap(corr, annot=True);"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YoxmHRWcznWA",
+        "outputId": "9ae3f87f-8c48-4e59-9817-92da0cb80d18"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "0    560808\n",
+              "1     25864\n",
+              "Name: explicit, dtype: int64"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df.explicit.value_counts()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 377
+        },
+        "id": "yQzbiJxSKaJw",
+        "outputId": "04c24f22-3885-49be-92d9-034c6f7000ef"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 864x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.title('Songs with Parental Advisory')\n",
+        "plt.pie(tracks_df.explicit.value_counts(), \n",
+        "        explode=(0.2, 0.01), \n",
+        "        colors=['skyblue','#ffcccc'], \n",
+        "        autopct='%1.1f%%', \n",
+        "        startangle=180);"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Q4x1uYRrKc4L"
+      },
+      "outputs": [],
+      "source": [
+        "px.scatter(tracks_df, x='release_date', y='popularity', title='Release Date vs. Popularity')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wBae5-m1SBZ7"
+      },
+      "source": [
+        "## Preprocessing\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Ff2hmJh5TPq6",
+        "outputId": "2d583eeb-1369-4e2b-b6c6-50abfd5a3038"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 586672 entries, 0 to 586671\n",
+            "Data columns (total 20 columns):\n",
+            " #   Column            Non-Null Count   Dtype  \n",
+            "---  ------            --------------   -----  \n",
+            " 0   id                586672 non-null  object \n",
+            " 1   name              586601 non-null  object \n",
+            " 2   popularity        586672 non-null  int64  \n",
+            " 3   duration_ms       586672 non-null  int64  \n",
+            " 4   explicit          586672 non-null  int64  \n",
+            " 5   artists           586672 non-null  object \n",
+            " 6   id_artists        586672 non-null  object \n",
+            " 7   release_date      586672 non-null  object \n",
+            " 8   danceability      586672 non-null  float64\n",
+            " 9   energy            586672 non-null  float64\n",
+            " 10  key               586672 non-null  int64  \n",
+            " 11  loudness          586672 non-null  float64\n",
+            " 12  mode              586672 non-null  int64  \n",
+            " 13  speechiness       586672 non-null  float64\n",
+            " 14  acousticness      586672 non-null  float64\n",
+            " 15  instrumentalness  586672 non-null  float64\n",
+            " 16  liveness          586672 non-null  float64\n",
+            " 17  valence           586672 non-null  float64\n",
+            " 18  tempo             586672 non-null  float64\n",
+            " 19  time_signature    586672 non-null  int64  \n",
+            "dtypes: float64(9), int64(6), object(5)\n",
+            "memory usage: 89.5+ MB\n"
+          ]
+        }
+      ],
+      "source": [
+        "tracks_df.info()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "LOPLIJPOOoaa",
+        "outputId": "98ca5fe9-460c-4974-9162-37a3ce5434f7"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "280451          1954\n",
+              "318897    1994-01-01\n",
+              "467545    2003-01-01\n",
+              "570339          1954\n",
+              "38400     1964-01-01\n",
+              "367025    1996-08-09\n",
+              "567268    2000-12-18\n",
+              "305275    2020-05-15\n",
+              "65610     1992-01-01\n",
+              "392670    1982-01-01\n",
+              "Name: release_date, dtype: object"
+            ]
+          },
+          "execution_count": 16,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df['release_date'].sample(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oo-pGd9FT_Sa"
+      },
+      "outputs": [],
+      "source": [
+        "def to_year(x):\n",
+        "    result = []\n",
+        "    x.to_string()\n",
+        "    for i in x:\n",
+        "        result.append(int(i[:4]))\n",
+        "    return result\n",
+        "\n",
+        "tracks_df['year'] = to_year(tracks_df.release_date)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "J9iRK7NgWjAo",
+        "outputId": "14adb386-fa38-4678-d79f-7c33be0d848e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "491215    1988\n",
+              "436448    2010\n",
+              "365012    1990\n",
+              "526481    2002\n",
+              "436743    2012\n",
+              "374357    2020\n",
+              "398975    2017\n",
+              "79429     2018\n",
+              "429506    1973\n",
+              "269191    1985\n",
+              "Name: year, dtype: int64"
+            ]
+          },
+          "execution_count": 18,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df['year'].sample(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_M0MDFbH3rYY"
+      },
+      "outputs": [],
+      "source": [
+        "max_year = tracks_df['year'].max()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xfBPuQN53qs6"
+      },
+      "outputs": [],
+      "source": [
+        "tracks_df['years_old'] = max_year - tracks_df['year']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5fPzzDaC3oy8",
+        "outputId": "e2cb616a-dc13-4b8f-9f00-65b981949880"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "560214     7\n",
+              "224465    34\n",
+              "563031    65\n",
+              "121726    86\n",
+              "92242      2\n",
+              "416956    63\n",
+              "297638    46\n",
+              "58044     37\n",
+              "418599    18\n",
+              "491004    37\n",
+              "Name: years_old, dtype: int64"
+            ]
+          },
+          "execution_count": 21,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df['years_old'].sample(10)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "38lRp2X6VAQJ",
+        "outputId": "9fc65165-9648-4dd2-a8c2-dd0c081fa21d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "0.000      328\n",
+              "128.003     98\n",
+              "119.994     91\n",
+              "139.980     89\n",
+              "127.994     86\n",
+              "          ... \n",
+              "185.442      1\n",
+              "170.351      1\n",
+              "122.670      1\n",
+              "55.513       1\n",
+              "144.505      1\n",
+              "Name: tempo, Length: 122706, dtype: int64"
+            ]
+          },
+          "execution_count": 22,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df['tempo'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uRnlSzbwWk0f"
+      },
+      "outputs": [],
+      "source": [
+        "tracks_df.loc[tracks_df['tempo'] == 0 , 'tempo'] = tracks_df.loc[tracks_df['tempo'] > 0 , 'tempo'].mean()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CD5ngOJsXRcb",
+        "outputId": "2785e3e0-4a73-451a-9331-082605605496"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "118.531126    328\n",
+              "128.003000     98\n",
+              "119.994000     91\n",
+              "139.980000     89\n",
+              "127.994000     86\n",
+              "             ... \n",
+              "185.442000      1\n",
+              "170.351000      1\n",
+              "122.670000      1\n",
+              "61.106000       1\n",
+              "144.505000      1\n",
+              "Name: tempo, Length: 122706, dtype: int64"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df['tempo'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "mHarAvN8G-yD"
+      },
+      "outputs": [],
+      "source": [
+        "tracks_df['mean'] = tracks_df.groupby('artists')['popularity'].transform('mean')\n",
+        "tracks_df['count'] = tracks_df.groupby('artists')['popularity'].transform('count')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XdSN5gV6WtoL"
+      },
+      "outputs": [],
+      "source": [
+        "def get_artist_popularity(x, y):\n",
+        "  result = y.groupby(x.artists).agg(['mean', 'count'])\n",
+        "  result.loc[result['count'] <= 2, 'mean'] = y.mean()\n",
+        "  result.loc[result['count'] >= 600, 'mean'] = 0\n",
+        "  return result"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vyNLxj43IfIi"
+      },
+      "outputs": [],
+      "source": [
+        "temp_df = tracks_df.drop('popularity', axis=1)\n",
+        "result_df = get_artist_popularity(temp_df, tracks_df['popularity'])['mean']\n",
+        "tracks_df['artist_popularity'] = np.where(tracks_df['artists'].isin(result_df.index), \n",
+        "                                          tracks_df['artists'], \n",
+        "                                          'unknown')\n",
+        "tracks_df['artist_popularity'] = tracks_df['artist_popularity'].map(result_df)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359
+        },
+        "id": "Jl99CwpyM-YC",
+        "outputId": "27926ab9-d6f4-4bc1-84f7-0a4dfebe71e9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>artists</th>\n",
+              "      <th>artist_popularity</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>37724</th>\n",
+              "      <td>['Bob Dylan']</td>\n",
+              "      <td>31.243070</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>308484</th>\n",
+              "      <td>['Cast of \"Folk Og Røvere I Kardemomme By\"']</td>\n",
+              "      <td>21.285714</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>73411</th>\n",
+              "      <td>['Gustavo Cerati']</td>\n",
+              "      <td>49.400000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>200336</th>\n",
+              "      <td>['Vicente Garcia']</td>\n",
+              "      <td>45.041667</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>135171</th>\n",
+              "      <td>['The Victor Orchestra', 'Nat Shilkret']</td>\n",
+              "      <td>0.031250</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>584631</th>\n",
+              "      <td>['Per Gessle', 'Helena Josefsson']</td>\n",
+              "      <td>27.570053</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>501328</th>\n",
+              "      <td>['Johannes Brahms', 'Wilhelm Furtwängler', 'Wi...</td>\n",
+              "      <td>0.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>301688</th>\n",
+              "      <td>['Dr. Dre', 'Snoop Dogg']</td>\n",
+              "      <td>64.714286</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>261596</th>\n",
+              "      <td>['Hex']</td>\n",
+              "      <td>16.733333</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>29816</th>\n",
+              "      <td>['Kishore Kumar', 'Asha Bhosle']</td>\n",
+              "      <td>19.616162</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "                                                  artists  artist_popularity\n",
+              "37724                                       ['Bob Dylan']          31.243070\n",
+              "308484       ['Cast of \"Folk Og Røvere I Kardemomme By\"']          21.285714\n",
+              "73411                                  ['Gustavo Cerati']          49.400000\n",
+              "200336                                 ['Vicente Garcia']          45.041667\n",
+              "135171           ['The Victor Orchestra', 'Nat Shilkret']           0.031250\n",
+              "584631                 ['Per Gessle', 'Helena Josefsson']          27.570053\n",
+              "501328  ['Johannes Brahms', 'Wilhelm Furtwängler', 'Wi...           0.000000\n",
+              "301688                          ['Dr. Dre', 'Snoop Dogg']          64.714286\n",
+              "261596                                            ['Hex']          16.733333\n",
+              "29816                    ['Kishore Kumar', 'Asha Bhosle']          19.616162"
+            ]
+          },
+          "execution_count": 30,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df[['artists', 'artist_popularity']].sample(10)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ql2PoKapPKo6"
+      },
+      "source": [
+        "### Input and Target Columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aWTMcVz3PWNb",
+        "outputId": "4df9c2f5-2af9-475e-ca9b-46a559fd0f01"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Index(['id', 'name', 'popularity', 'duration_ms', 'explicit', 'artists',\n",
+              "       'id_artists', 'release_date', 'danceability', 'energy', 'key',\n",
+              "       'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness',\n",
+              "       'liveness', 'valence', 'tempo', 'time_signature', 'year', 'years_old',\n",
+              "       'mean', 'count', 'artist_popularity'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "execution_count": 31,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df.columns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qMG1BZ_HPwDj"
+      },
+      "outputs": [],
+      "source": [
+        "input_columns = ['years_old', 'duration_ms', 'explicit', 'danceability', 'energy', 'key', \n",
+        "                 'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', \n",
+        "                 'liveness', 'valence', 'tempo', 'time_signature', 'artist_popularity']\n",
+        "\n",
+        "target_column = 'popularity'"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_183l99NSZ1S"
+      },
+      "outputs": [],
+      "source": [
+        "numeric_cols = ['years_old', 'duration_ms', 'explicit', 'danceability', 'energy', \n",
+        "                'loudness', 'mode', 'speechiness', 'acousticness', 'instrumentalness', \n",
+        "                'liveness', 'valence', 'tempo', 'artist_popularity']\n",
+        "categorical_cols = ['key', 'time_signature']"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lE0SHpOMTvA7",
+        "outputId": "029f0358-6a05-4039-bf5b-be59bade01ac"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "0     74950\n",
+              "7     73779\n",
+              "2     66552\n",
+              "9     65128\n",
+              "5     53614\n",
+              "4     48220\n",
+              "1     41736\n",
+              "11    39132\n",
+              "10    37710\n",
+              "8     33460\n",
+              "6     30856\n",
+              "3     21535\n",
+              "Name: key, dtype: int64"
+            ]
+          },
+          "execution_count": 34,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df['key'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "02dTLENVQOZT",
+        "outputId": "a56a6f54-b81c-43d3-fb6c-bf44641ed78f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "4    503808\n",
+              "3     64523\n",
+              "5     11400\n",
+              "1      6604\n",
+              "0       337\n",
+              "Name: time_signature, dtype: int64"
+            ]
+          },
+          "execution_count": 35,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df['time_signature'].value_counts()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0GaXkWw3b02U"
+      },
+      "source": [
+        "### Training, Validation and Test Split"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ah_-LxXjb-ft"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "M_BHbHB8b9GV"
+      },
+      "outputs": [],
+      "source": [
+        "train_val_df, test_df = train_test_split(tracks_df, test_size=0.2, random_state=42)\n",
+        "train_df, val_df = train_test_split(train_val_df, test_size=0.25, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "f7zVCam2b8dZ",
+        "outputId": "3030e2ee-36ec-489c-d968-13e896d389b9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "train_df (352002, 25)\n",
+            "val_df (117335, 25)\n",
+            "test_df (117335, 25)\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('train_df', train_df.shape)\n",
+        "print('val_df', val_df.shape)\n",
+        "print('test_df', test_df.shape)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Ckn-18WkdLt8"
+      },
+      "outputs": [],
+      "source": [
+        "train_inputs = train_df[input_columns].copy()\n",
+        "val_inputs = val_df[input_columns].copy()\n",
+        "test_inputs = test_df[input_columns].copy()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LtgQB66bdql2"
+      },
+      "outputs": [],
+      "source": [
+        "Y_train = train_df[target_column].copy()\n",
+        "Y_val = val_df[target_column].copy()\n",
+        "Y_test = test_df[target_column].copy()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2hIgacOuGaEF"
+      },
+      "source": [
+        "### Imputing missing values"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "XAzlVfFvBbSs",
+        "outputId": "b4e33bcd-5a3f-493a-c6e5-81a587930f3a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "years_old            0\n",
+              "duration_ms          0\n",
+              "explicit             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",
+              "time_signature       0\n",
+              "artist_popularity    0\n",
+              "dtype: int64"
+            ]
+          },
+          "execution_count": 41,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "tracks_df[input_columns].isna().sum()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_fzgQ9j-UcpK"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import MinMaxScaler"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Yb-a9C4JZqgD"
+      },
+      "outputs": [],
+      "source": [
+        "scaler = MinMaxScaler().fit(tracks_df[numeric_cols])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NCnL6tRcZ6nK"
+      },
+      "outputs": [],
+      "source": [
+        "train_inputs[numeric_cols] = scaler.transform(train_inputs[numeric_cols])\n",
+        "val_inputs[numeric_cols] = scaler.transform(val_inputs[numeric_cols])\n",
+        "test_inputs[numeric_cols] = scaler.transform(test_inputs[numeric_cols])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "k7ygB65h59fR"
+      },
+      "outputs": [],
+      "source": [
+        "Y_train = Y_train / 100\n",
+        "Y_val = Y_val / 100\n",
+        "Y_test = Y_test / 100"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "aREAofmxfPmM"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.preprocessing import OneHotEncoder"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LMqtib80gUnZ"
+      },
+      "outputs": [],
+      "source": [
+        "encoder = OneHotEncoder(sparse=False, handle_unknown='ignore').fit(tracks_df[categorical_cols])\n",
+        "encoded_cols = list(encoder.get_feature_names(categorical_cols))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uASUJa5ThDKd"
+      },
+      "outputs": [],
+      "source": [
+        "train_inputs[encoded_cols] = encoder.transform(train_inputs[categorical_cols])\n",
+        "val_inputs[encoded_cols] = encoder.transform(val_inputs[categorical_cols])\n",
+        "test_inputs[encoded_cols] = encoder.transform(test_inputs[categorical_cols])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IcDZ_RSXvOsq"
+      },
+      "outputs": [],
+      "source": [
+        "X_train = train_inputs[numeric_cols + encoded_cols]\n",
+        "X_val = val_inputs[numeric_cols + encoded_cols]\n",
+        "X_test = test_inputs[numeric_cols + encoded_cols]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QUg41r3GhoS6"
+      },
+      "source": [
+        "## Linear Regression\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GsQ6ZeLEhWpU"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.linear_model import Ridge"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SuPX0R3pj7F9"
+      },
+      "outputs": [],
+      "source": [
+        "linear_model = Ridge()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "CarpJWtZkAgS",
+        "outputId": "2548895a-9312-413d-a348-216da7bee3fa"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
+              "      normalize=False, random_state=None, solver='auto', tol=0.001)"
+            ]
+          },
+          "execution_count": 52,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "linear_model.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lLcaui2ykRok"
+      },
+      "outputs": [],
+      "source": [
+        "linear_train_predictions = linear_model.predict(X_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cTsVO3Ekkxn-",
+        "outputId": "1e29872f-8fdc-43e3-80af-7a9bd6671c85"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0.2382466 , 0.33454942, 0.56792125, ..., 0.04601526, 0.46541006,\n",
+              "       0.37539489])"
+            ]
+          },
+          "execution_count": 54,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "linear_train_predictions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "nKPWHRgekiHf"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.metrics import mean_squared_error"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ri_KsaNJkpyS"
+      },
+      "outputs": [],
+      "source": [
+        "train_rmse = mean_squared_error(linear_train_predictions, Y_train, squared=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wnj5P-fOkpn7",
+        "outputId": "d665701a-bc03-4501-82ff-7c953cba5882"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for training set is  0.11869256238860909\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('The RMSE loss for training set is ', train_rmse)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "91i_xiyxs2yE",
+        "outputId": "9404d41b-9c7a-4840-91ef-606bba48c294"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for validation set is  0.11880934110046901\n"
+          ]
+        }
+      ],
+      "source": [
+        "linear_val_predictions = linear_model.predict(X_val)\n",
+        "val_rmse = mean_squared_error(linear_val_predictions, Y_val, squared=False)\n",
+        "print('The RMSE loss for validation set is ', val_rmse)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AlzK_EUNs2po",
+        "outputId": "72bb75d3-362b-406b-9806-586694669457"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for test set is  0.11783782221260501\n"
+          ]
+        }
+      ],
+      "source": [
+        "linear_test_predictions = linear_model.predict(X_test)\n",
+        "test_rmse = mean_squared_error(linear_test_predictions, Y_test, squared=False)\n",
+        "print('The RMSE loss for test set is ', test_rmse)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "RpdKhQ46n5vB"
+      },
+      "source": [
+        "## Random Forest\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yOoEkhsSoYCu"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.ensemble import RandomForestRegressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "NGXukVJ9on2N"
+      },
+      "outputs": [],
+      "source": [
+        "random_forest_model = RandomForestRegressor(n_jobs=-1, random_state=42)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7ZmPX9ZMoqV2",
+        "outputId": "f9fbf002-45a3-437b-dd71-3a8c5447068a"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
+              "                      max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
+              "                      max_samples=None, min_impurity_decrease=0.0,\n",
+              "                      min_impurity_split=None, min_samples_leaf=1,\n",
+              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+              "                      n_estimators=100, n_jobs=-1, oob_score=False,\n",
+              "                      random_state=42, verbose=0, warm_start=False)"
+            ]
+          },
+          "execution_count": 62,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "random_forest_model.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GZctl-83v-rX",
+        "outputId": "fa2c62a9-70f9-4fbe-dc63-dee99a51a5c2"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0.2692 , 0.2128 , 0.4946 , ..., 0.0655 , 0.423  , 0.37572])"
+            ]
+          },
+          "execution_count": 63,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "random_forest_train_preds = random_forest_model.predict(X_train)\n",
+        "random_forest_train_preds"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "tadF94KgwoFw",
+        "outputId": "82eaf99b-9449-4b09-f752-0bac38383541"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for training set is  0.03816131456756738\n"
+          ]
+        }
+      ],
+      "source": [
+        "train_rmse = mean_squared_error(random_forest_train_preds, Y_train, squared=False)\n",
+        "print('The RMSE loss for training set is ', train_rmse)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nbgwOzxgv-pg",
+        "outputId": "d2c151ac-5601-47ba-fd68-4b46f0dbe1dd"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([3.0130e-02, 3.4100e-02, 5.6460e-01, ..., 5.7665e-01, 4.3920e-01,\n",
+              "       3.0000e-04])"
+            ]
+          },
+          "execution_count": 65,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "random_forest_val_preds = random_forest_model.predict(X_val)\n",
+        "random_forest_val_preds"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "bfbF5qy-wnmw",
+        "outputId": "9fa4a06f-53d6-4ecd-ba98-761b2daeb51e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for validation set is  0.0985493109542275\n"
+          ]
+        }
+      ],
+      "source": [
+        "val_rmse = mean_squared_error(random_forest_val_preds, Y_val, squared=False)\n",
+        "print('The RMSE loss for validation set is ', val_rmse)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_crlXeKYxXCL"
+      },
+      "outputs": [],
+      "source": [
+        "def test_params(**params):\n",
+        "    model = RandomForestRegressor(random_state=42, n_jobs=-1, **params).fit(X_train, Y_train)\n",
+        "    train_rmse = mean_squared_error(model.predict(X_train), Y_train, squared=False)\n",
+        "    val_rmse = mean_squared_error(model.predict(X_val), Y_val, squared=False)\n",
+        "    return train_rmse, val_rmse"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kP-mijjlxW5S"
+      },
+      "outputs": [],
+      "source": [
+        "def test_param_and_plot(param_name, param_values):\n",
+        "    train_errors, val_errors = [], [] \n",
+        "    for value in param_values:\n",
+        "        params = {param_name: value}\n",
+        "        train_rmse, val_rmse = test_params(**params)\n",
+        "        train_errors.append(train_rmse)\n",
+        "        val_errors.append(val_rmse)\n",
+        "    plt.figure(figsize=(10,6))\n",
+        "    plt.title('Overfitting curve: ' + param_name)\n",
+        "    plt.plot(param_values, train_errors, 'b-o')\n",
+        "    plt.plot(param_values, val_errors, 'r-o')\n",
+        "    plt.xlabel(param_name)\n",
+        "    plt.ylabel('RMSE')\n",
+        "    plt.legend(['Training', 'Validation'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "jHbCXlwGxW3N",
+        "outputId": "3561f3a6-3da1-4ca8-9ae6-f9421f70458f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "test_param_and_plot('max_depth', [5, 10, 15, 20, 25])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "vSPgxv4Ky0tS",
+        "outputId": "5d41f34b-e24e-493d-84a3-4835844a5219"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "test_param_and_plot('n_estimators', [100, 150, 200, 250])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "CwQNnbdsy1nr",
+        "outputId": "039c5042-da2d-44e5-e51c-ec7e7dafe764"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "test_param_and_plot('max_features', [6, 8, 10, 12, 14])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fR-IHgtQ4KWA",
+        "outputId": "6f1013c1-069f-46c4-86b0-c43b51bc2922"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
+              "                      max_depth=15, max_features=10, max_leaf_nodes=None,\n",
+              "                      max_samples=None, min_impurity_decrease=0.0,\n",
+              "                      min_impurity_split=None, min_samples_leaf=1,\n",
+              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
+              "                      n_estimators=100, n_jobs=-1, oob_score=False,\n",
+              "                      random_state=42, verbose=0, warm_start=False)"
+            ]
+          },
+          "execution_count": 72,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "random_forest_model = RandomForestRegressor(n_jobs=-1, \n",
+        "                                            random_state=42, \n",
+        "                                            max_depth=15, \n",
+        "                                            n_estimators=100, \n",
+        "                                            max_features=10)\n",
+        "random_forest_model.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "wWganxAMo5Zu",
+        "outputId": "24b05fd5-fcf0-45a0-c517-6e671b58f493"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for training set is  0.08949642866541821  and accuracy is  76.32310537376918 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "train_acc = random_forest_model.score(X_train, Y_train)\n",
+        "train_rmse = mean_squared_error(random_forest_model.predict(X_train), Y_train, squared=False)\n",
+        "print('The RMSE loss for training set is ', train_rmse, ' and accuracy is ', train_acc*100, '%')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IVSzoug8vfru",
+        "outputId": "71c57344-c33b-4196-d4c9-ad12070ebc0a"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for validation set is  0.09923756195532543  and accuracy is  70.85634588193562 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "val_acc = random_forest_model.score(X_val, Y_val)\n",
+        "val_rmse = mean_squared_error(random_forest_model.predict(X_val), Y_val, squared=False)\n",
+        "print('The RMSE loss for validation set is ', val_rmse, ' and accuracy is ', val_acc*100, '%')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "MR6Sqda3CK1c",
+        "outputId": "bec86351-ab06-422d-c9cf-cae8fc9c4f2f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for test set is  0.09848852947939368  and accuracy is  71.01177622937989 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "test_acc = random_forest_model.score(X_test, Y_test)\n",
+        "test_rmse = mean_squared_error(random_forest_model.predict(X_test), Y_test, squared=False)\n",
+        "print('The RMSE loss for test set is ', test_rmse, ' and accuracy is ', test_acc*100, '%')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "m0WqLndX-m9N"
+      },
+      "outputs": [],
+      "source": [
+        "feature_importance_df = pd.DataFrame({\n",
+        "    'feature': X_train.columns,\n",
+        "    'importance': random_forest_model.feature_importances_\n",
+        "}).sort_values('importance', ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "X7sNfu5T-neu",
+        "outputId": "084b02d3-b9fc-4429-b975-d2bdc67ac1b3"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10,6))\n",
+        "plt.title('Feature Importance')\n",
+        "sns.barplot(data=feature_importance_df.head(10), x='importance', y='feature', palette='icefire');"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2izqM5gq8Wie"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install xgboost --upgrade --quiet"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vJrPQLHo1Mxs"
+      },
+      "outputs": [],
+      "source": [
+        "from xgboost import XGBRegressor"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zrV71PJl1Mln"
+      },
+      "outputs": [],
+      "source": [
+        "gbm_model = XGBRegressor(random_state=42, n_jobs=-1, n_estimators=20, max_depth=4)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YOcqrLgf1MOC",
+        "outputId": "78ea1bef-0754-453c-86e1-21fc98101bbe"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
+              "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
+              "             importance_type='gain', interaction_constraints='',\n",
+              "             learning_rate=0.300000012, max_delta_step=0, max_depth=4,\n",
+              "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
+              "             n_estimators=20, n_jobs=-1, num_parallel_tree=1,\n",
+              "             objective='reg:squarederror', random_state=42, reg_alpha=0,\n",
+              "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
+              "             validate_parameters=1, verbosity=None)"
+            ]
+          },
+          "execution_count": 82,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gbm_model.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PnxDD-792BFp",
+        "outputId": "f6648790-790b-4862-b78f-fa382fc19c1c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0.22382605, 0.35105187, 0.5886918 , ..., 0.02703674, 0.4237026 ,\n",
+              "       0.36623526], dtype=float32)"
+            ]
+          },
+          "execution_count": 83,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gbm_train_preds = gbm_model.predict(X_train)\n",
+        "gbm_train_preds"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gVz0oGI-2baV",
+        "outputId": "d88275b2-edc0-4f44-8836-f1f48a8076e6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for training set is  0.1030407038841221\n"
+          ]
+        }
+      ],
+      "source": [
+        "train_rmse = mean_squared_error(gbm_train_preds, Y_train, squared=False)\n",
+        "print('The RMSE loss for training set is ', train_rmse)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "IW8ksI5g2XcH",
+        "outputId": "9c274b37-5ee0-4b39-8c2c-b00bfbf08822"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "array([0.03452813, 0.0514764 , 0.47231337, ..., 0.4423982 , 0.4675226 ,\n",
+              "       0.01998455], dtype=float32)"
+            ]
+          },
+          "execution_count": 85,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gbm_val_preds = gbm_model.predict(X_val)\n",
+        "gbm_val_preds"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gGAtXaDd2bYR",
+        "outputId": "65f550ff-3930-4c20-d6db-d90638d7884f"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for validation set is  0.10338607422749921\n"
+          ]
+        }
+      ],
+      "source": [
+        "val_rmse = mean_squared_error(gbm_val_preds, Y_val, squared=False)\n",
+        "print('The RMSE loss for validation set is ', val_rmse)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QdXTFd_Q2lQP"
+      },
+      "outputs": [],
+      "source": [
+        "def test_params(**params):\n",
+        "    model = XGBRegressor(random_state=42, n_jobs=-1, **params).fit(X_train, Y_train)\n",
+        "    train_rmse = mean_squared_error(model.predict(X_train), Y_train, squared=False)\n",
+        "    val_rmse = mean_squared_error(model.predict(X_val), Y_val, squared=False)\n",
+        "    return train_rmse, val_rmse"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "aApfo5y32lGm",
+        "outputId": "38ce2692-7520-4eb6-ada4-f7f6c124efee"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "test_param_and_plot('n_estimators', [1000, 1200, 1500])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "QqoQENTv0YiM",
+        "outputId": "27490843-8395-43c1-9d24-5bbcd33a3035"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "test_param_and_plot('n_estimators', [400, 600, 800])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "U2628PWe5FHP",
+        "outputId": "e462ad1a-ab3b-4c96-f2ac-1ab8a91f539d"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "test_param_and_plot('n_estimators', [100, 200, 400])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "zzanGy9Y2lBE",
+        "outputId": "6f113b3d-9572-425c-8c08-f97d5271a853"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "test_param_and_plot('max_depth', [8, 10, 12, 14])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "tmfXi6imOHHg",
+        "outputId": "7059078f-f4ed-4bec-c4e8-8d4c516915a9"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "test_param_and_plot('max_depth', [2, 4, 6, 8])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YDOkqtYr7fGf"
+      },
+      "outputs": [],
+      "source": [
+        "def test_and_plot_learning_rate(param_values):\n",
+        "    train_errors, val_errors = [], [] \n",
+        "    for value in param_values:\n",
+        "        params = {'learning_rate': value, 'n_estimators': 200}\n",
+        "        train_rmse, val_rmse = test_params(**params)\n",
+        "        train_errors.append(train_rmse)\n",
+        "        val_errors.append(val_rmse)\n",
+        "    plt.figure(figsize=(10,6))\n",
+        "    plt.title('Overfitting curve: ' + 'learning_rate')\n",
+        "    plt.plot(param_values, train_errors, 'b-o')\n",
+        "    plt.plot(param_values, val_errors, 'r-o')\n",
+        "    plt.xlabel('learning_rate')\n",
+        "    plt.ylabel('RMSE')\n",
+        "    plt.legend(['Training', 'Validation'])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "bTXMpL2f2k-t",
+        "outputId": "a9b35c89-417f-40a6-c4c3-6d1de9447ae8"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "test_and_plot_learning_rate([0.1, 0.2, 0.3, 0.4])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GBoPNo-x6DpK",
+        "outputId": "e533cb28-f7d2-4726-9071-6aee97457e85"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
+              "             colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
+              "             importance_type='gain', interaction_constraints='',\n",
+              "             learning_rate=0.2, max_delta_step=0, max_depth=8,\n",
+              "             min_child_weight=1, missing=nan, monotone_constraints='()',\n",
+              "             n_estimators=200, n_jobs=-1, num_parallel_tree=1,\n",
+              "             objective='reg:squarederror', random_state=42, reg_alpha=0,\n",
+              "             reg_lambda=1, scale_pos_weight=1, subsample=1, tree_method='exact',\n",
+              "             validate_parameters=1, verbosity=None)"
+            ]
+          },
+          "execution_count": 96,
+          "metadata": {
+            "tags": []
+          },
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "gbm_model = XGBRegressor(random_state=42, \n",
+        "                         n_jobs=-1, \n",
+        "                         n_estimators=200, \n",
+        "                         max_depth=8, \n",
+        "                         learning_rate=0.2)\n",
+        "gbm_model.fit(X_train, Y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "r0ecwyLh58x-",
+        "outputId": "e7794b4f-9f29-4058-f15b-e59477221cfa"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for training set is  0.0881456457592302  and accuracy is  77.03242948569432 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "train_acc = gbm_model.score(X_train, Y_train)\n",
+        "train_rmse = mean_squared_error(gbm_model.predict(X_train), Y_train, squared=False)\n",
+        "print('The RMSE loss for training set is ', train_rmse, ' and accuracy is ', train_acc*100, '%')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W9LZ5YEH6Jrl",
+        "outputId": "82703662-f13b-4085-80e8-8f688dee5aa1"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for validation set is  0.09907624298519543  and accuracy is  70.95101977146088 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "val_acc = gbm_model.score(X_val, Y_val)\n",
+        "val_rmse = mean_squared_error(gbm_model.predict(X_val), Y_val, squared=False)\n",
+        "print('The RMSE loss for validation set is ', val_rmse, ' and accuracy is ', val_acc*100, '%')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "sApJgaVl6Jjl",
+        "outputId": "d4e4c040-4c48-4b22-ac0f-98bd0db23e75"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "The RMSE loss for test set is  0.09836366440351287  and accuracy is  71.08523295130684 %\n"
+          ]
+        }
+      ],
+      "source": [
+        "test_acc = gbm_model.score(X_test, Y_test)\n",
+        "test_rmse = mean_squared_error(gbm_model.predict(X_test), Y_test, squared=False)\n",
+        "print('The RMSE loss for test set is ', test_rmse, ' and accuracy is ', test_acc*100, '%')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "uox18X3j-6TM"
+      },
+      "outputs": [],
+      "source": [
+        "feature_importance_df = pd.DataFrame({\n",
+        "    'feature': X_train.columns,\n",
+        "    'importance': gbm_model.feature_importances_\n",
+        "}).sort_values('importance', ascending=False)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 416
+        },
+        "id": "jgbMucX1-6LQ",
+        "outputId": "e18f3cfa-74b6-4953-b1a6-f39995071d32"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 720x432 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light",
+            "tags": []
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "plt.figure(figsize=(10,6))\n",
+        "plt.title('Feature Importance')\n",
+        "sns.barplot(data=feature_importance_df.head(10), x='importance', y='feature', palette='icefire');"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0F2RBk8-FIAl"
+      },
+      "source": [
+        "## Making Predictions"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QUkUK-FI-C1c"
+      },
+      "source": [
+        "Now let's make predictions on a single input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_8XUQiIRFK8k"
+      },
+      "outputs": [],
+      "source": [
+        "new_input = {\n",
+        "    'name' : 'Watermelon Sugar',\n",
+        "    'duration_ms' : 174000,\n",
+        "    'explicit' : 0,\n",
+        "    'artists'\t: \"['Harry Styles']\",\n",
+        "    'release_date' : '2019',\n",
+        "    'danceability' : 0.548,\n",
+        "    'energy' : 0.816,\n",
+        "    'key' : 0,\n",
+        "    'loudness' : -4.209,\n",
+        "    'mode' : 1,\n",
+        "    'speechiness' : 0.0465,\n",
+        "    'acousticness' : 0.122,\n",
+        "    'instrumentalness' : 0.0,\n",
+        "    'liveness' : 0.335,\n",
+        "    'valence' : 0.557,\n",
+        "    'tempo' : 95.39,\n",
+        "    'time_signature' : 4\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QbC25XYrZYF8"
+      },
+      "source": [
+        "Let's declare the variables that we will need for preprocessing."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4JunVwk4LNAV"
+      },
+      "outputs": [],
+      "source": [
+        "max_year = tracks_df['year'].max()\n",
+        "mean_tempo = tracks_df.loc[tracks_df['tempo'] > 0 , 'tempo'].mean()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KV89kgjq-Yyx"
+      },
+      "outputs": [],
+      "source": [
+        "def predict_input(model, single_input):\n",
+        "  input_df = pd.DataFrame([single_input])\n",
+        "\n",
+        "  #Year\n",
+        "  input_df['year'] = to_year(input_df.release_date)\n",
+        "  input_df['years_old'] = max_year - input_df['year']\n",
+        "\n",
+        "  #Tempo\n",
+        "  if input_df.tempo[0] == 0:\n",
+        "    input_df['tempo'] = mean_tempo\n",
+        "  \n",
+        "  #Artists\n",
+        "  flag = False\n",
+        "  for index in tracks_df.index:\n",
+        "    if tracks_df.loc[index]['artists'] == input_df.artists[0]:\n",
+        "      flag = True\n",
+        "      input_df['artist_popularity'] = tracks_df.loc[index]['artist_popularity']\n",
+        "      break\n",
+        "  \n",
+        "  if(flag == False):\n",
+        "    input_df['artist_popularity'] = tracks_df['popularity'].mean()\n",
+        "\n",
+        "  #scaling and Encoding\n",
+        "  input_df[numeric_cols] = scaler.transform(input_df[numeric_cols])\n",
+        "  input_df[encoded_cols] = encoder.transform(input_df[categorical_cols])\n",
+        "  X_input = input_df[numeric_cols + encoded_cols]\n",
+        "\n",
+        "  pred = model.predict(X_input)[0]\n",
+        "  pred = pred * 100\n",
+        "  return pred"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W-PBQX_hFK6b",
+        "outputId": "9591ba23-f592-4c0d-aef3-f4e7ce5935eb"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Predicted popularity of the song is  76.92539095878601\n"
+          ]
+        }
+      ],
+      "source": [
+        "print('Predicted popularity of the song is ', predict_input(gbm_model, new_input))"
+      ]
+    }
+  ]
+}
\ No newline at end of file