Internship_Project.ipynb

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      "name": "Internship_Project.ipynb",
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      "authorship_tag": "ABX9TyMSOp8BtNXdA9xHTVFj/5Rs",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MohammedNayaz/Machine-learning-Internship-2/blob/master/Internship_Project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-7QJvJeLuUZD"
      },
      "source": [
        "#Project: Youtube adview Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDLT1pGhuWSw"
      },
      "source": [
        "##1.Importing the datasets and libraries, check shape and datatype.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfF0MYgeuAdV"
      },
      "source": [
        "#importing the libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.cm as cm\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SxcnnM3kuwrX"
      },
      "source": [
        "#importing the datasets\n",
        "data_train = pd.read_csv(\"train.csv\")"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ga-q4Q69u_Kx",
        "outputId": "73aeaa16-3a85-4890-bb89-2033adc2d914",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "#Displaying the imported dataset\n",
        "data_train"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
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              "      <td>2</td>\n",
              "      <td>2016-07-02</td>\n",
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              "      <td>C</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VID_23096</td>\n",
              "      <td>6</td>\n",
              "      <td>620860</td>\n",
              "      <td>777</td>\n",
              "      <td>161</td>\n",
              "      <td>153</td>\n",
              "      <td>2016-07-27</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>666</td>\n",
              "      <td>1</td>\n",
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              "      <td>2016-06-29</td>\n",
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              "      <td>525949</td>\n",
              "      <td>1137</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>2015-05-18</td>\n",
              "      <td>PT6M10S</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>VID_5861</td>\n",
              "      <td>1</td>\n",
              "      <td>665673</td>\n",
              "      <td>3849</td>\n",
              "      <td>156</td>\n",
              "      <td>569</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>G</td>\n",
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              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>VID_8534</td>\n",
              "      <td>1</td>\n",
              "      <td>15212</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2016-02-19</td>\n",
              "      <td>PT1M1S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>14999 rows × 9 columns</p>\n",
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              "           vidid  adview    views likes  ... comment   published duration category\n",
              "0      VID_18655      40  1031602  8523  ...    1095  2016-09-14  PT7M37S        F\n",
              "1      VID_14135       2     1707    56  ...       6  2016-10-01  PT9M30S        D\n",
              "2       VID_2187       1     2023    25  ...       2  2016-07-02  PT2M16S        C\n",
              "3      VID_23096       6   620860   777  ...     153  2016-07-27  PT4M22S        H\n",
              "4      VID_10175       1      666     1  ...       0  2016-06-29    PT31S        D\n",
              "...          ...     ...      ...   ...  ...     ...         ...      ...      ...\n",
              "14994     VID_31       2   525949  1137  ...      86  2015-05-18  PT6M10S        A\n",
              "14995   VID_5861       1   665673  3849  ...     569  2015-10-20  PT3M56S        D\n",
              "14996    VID_805       4     3479    16  ...       1  2013-08-23  PT3M13S        B\n",
              "14997  VID_19843       1      963     0  ...       0  2010-10-02    PT26S        G\n",
              "14998   VID_8534       1    15212    22  ...       4  2016-02-19   PT1M1S        D\n",
              "\n",
              "[14999 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 3
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    {
      "cell_type": "code",
      "metadata": {
        "id": "xO85pU7-vAAP",
        "outputId": "528c04b3-13d6-4c2e-f719-2e30ad63222d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#checking the shape of dataset\n",
        "data_train.shape"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(14999, 9)"
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          "metadata": {
            "tags": []
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          "execution_count": 4
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    {
      "cell_type": "code",
      "metadata": {
        "id": "MxCcm7nyu_7F",
        "outputId": "4465f5c3-16af-45af-aea9-4fb021768d64",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "#checking the head part of the dataset\n",
        "data_train.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>F</td>\n",
              "    </tr>\n",
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              "      <td>153</td>\n",
              "      <td>2016-07-27</td>\n",
              "      <td>PT4M22S</td>\n",
              "      <td>H</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>VID_10175</td>\n",
              "      <td>1</td>\n",
              "      <td>666</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-06-29</td>\n",
              "      <td>PT31S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "       vidid  adview    views likes  ... comment   published duration category\n",
              "0  VID_18655      40  1031602  8523  ...    1095  2016-09-14  PT7M37S        F\n",
              "1  VID_14135       2     1707    56  ...       6  2016-10-01  PT9M30S        D\n",
              "2   VID_2187       1     2023    25  ...       2  2016-07-02  PT2M16S        C\n",
              "3  VID_23096       6   620860   777  ...     153  2016-07-27  PT4M22S        H\n",
              "4  VID_10175       1      666     1  ...       0  2016-06-29    PT31S        D\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pz3KIbdhu_4b",
        "outputId": "22d5df06-9c6f-4e87-b3d2-6f6425aa28cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "#checking the tail part of the dataset\n",
        "data_train.tail()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>VID_31</td>\n",
              "      <td>2</td>\n",
              "      <td>525949</td>\n",
              "      <td>1137</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>2015-05-18</td>\n",
              "      <td>PT6M10S</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
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              "      <th>14995</th>\n",
              "      <td>VID_5861</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>2013-08-23</td>\n",
              "      <td>PT3M13S</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>VID_19843</td>\n",
              "      <td>1</td>\n",
              "      <td>963</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2010-10-02</td>\n",
              "      <td>PT26S</td>\n",
              "      <td>G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>VID_8534</td>\n",
              "      <td>1</td>\n",
              "      <td>15212</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2016-02-19</td>\n",
              "      <td>PT1M1S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ],
            "text/plain": [
              "           vidid  adview   views likes  ... comment   published duration category\n",
              "14994     VID_31       2  525949  1137  ...      86  2015-05-18  PT6M10S        A\n",
              "14995   VID_5861       1  665673  3849  ...     569  2015-10-20  PT3M56S        D\n",
              "14996    VID_805       4    3479    16  ...       1  2013-08-23  PT3M13S        B\n",
              "14997  VID_19843       1     963     0  ...       0  2010-10-02    PT26S        G\n",
              "14998   VID_8534       1   15212    22  ...       4  2016-02-19   PT1M1S        D\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ROlVwztCu_08",
        "outputId": "779a4e97-730d-4a77-c3ca-63606fafb15d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "#describing the dataset\n",
        "data_train.describe"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of            vidid  adview    views likes  ... comment   published duration category\n",
              "0      VID_18655      40  1031602  8523  ...    1095  2016-09-14  PT7M37S        F\n",
              "1      VID_14135       2     1707    56  ...       6  2016-10-01  PT9M30S        D\n",
              "2       VID_2187       1     2023    25  ...       2  2016-07-02  PT2M16S        C\n",
              "3      VID_23096       6   620860   777  ...     153  2016-07-27  PT4M22S        H\n",
              "4      VID_10175       1      666     1  ...       0  2016-06-29    PT31S        D\n",
              "...          ...     ...      ...   ...  ...     ...         ...      ...      ...\n",
              "14994     VID_31       2   525949  1137  ...      86  2015-05-18  PT6M10S        A\n",
              "14995   VID_5861       1   665673  3849  ...     569  2015-10-20  PT3M56S        D\n",
              "14996    VID_805       4     3479    16  ...       1  2013-08-23  PT3M13S        B\n",
              "14997  VID_19843       1      963     0  ...       0  2010-10-02    PT26S        G\n",
              "14998   VID_8534       1    15212    22  ...       4  2016-02-19   PT1M1S        D\n",
              "\n",
              "[14999 rows x 9 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xyJgdG2VvzEo",
        "outputId": "1ab3896b-4a8d-42b4-df24-00e7d4c337de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#checking the datatype of the dataset\n",
        "type(data_train)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OD23O0O-Rl16"
      },
      "source": [
        "##2.Visualizing the dataset using plotting, using heatmaps abd plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vb6colXNvzHa",
        "outputId": "70ae171f-0d7d-4c67-8fbc-4cc6957317ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "#visualization of the dataset\n",
        "\n",
        "#2D plotting\n",
        "  #Individual plots\n",
        "\n",
        "#line graph \n",
        "plt.plot(data_train[\"adview\"])\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vIVXEJvhvy4O",
        "outputId": "6fd6915d-d3af-41f3-9d75-15426fed7ab1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(data_train[\"views\"])\n",
        "plt.show()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EX94-RQaUCbN",
        "outputId": "0d52418a-5b0e-4fd1-b4e5-55d4fb71f513",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(data_train[\"likes\"])\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J19s3EMQUCnf",
        "outputId": "c5722ea0-a333-4e27-e347-92b6fc127d00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(data_train[\"dislikes\"])\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DhIyIfw9UCvK",
        "outputId": "9cf0263c-6465-412f-ff89-fd4be8ddd561",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(data_train[\"comment\"])\n",
        "plt.show()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "myVi5H1IUC6m",
        "outputId": "bb513ddd-8f4e-440b-b101-7d9a5ae99ec4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(data_train[\"category\"])\n",
        "plt.show()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxY_5BM6UC9F",
        "outputId": "0c4339ca-b29d-4d2f-8a46-b68e9ebd0641",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "plt.plot(data_train[\"duration\"])\n",
        "plt.show()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZIrkf2M-UC_i",
        "outputId": "fdeb23f4-4b6b-4321-e207-f12725453cd5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "source": [
        "#plotting histogram plot\n",
        "plt.hist(data_train[\"category\"])\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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w8dff9nA8KpGk11ow9JP8PvDPgIuSHGfmKpzbgHuT7AKeBW5ow+8HrgOmgJeBmwGq6lSSW4GH27hbqurMHw5LkpbYgqFfVTfNs+jqOcYWsGee9RwADiyqO0nSSHlHriR1xNCXpI4Y+pLUEUNfkjpi6EtSR875jlytHOO8L+GZ264f27YlDc8jfUnqiKEvSR0x9CWpI57T13nN368kLY5H+pLUEUNfkjri6R1Jmscb8TJoj/QlqSOGviR1xNCXpI4Y+pLUEUNfkjpi6EtSR5Y99JNsS/JUkqkke5d7+5LUs2UN/SQXAL8JXAtcCtyU5NLl7EGSerbcR/pXAFNV9XRVfR+4B9i+zD1IUreW+47c9cCxgfnjwJWDA5LsBna32W8neeoctncR8Ffn8PrldD71Cmf0m0+OsZOFnU9/tgv2usL+rN9Qf7YrST55Tv2+a74FK+7XMFTVfmD/KNaVZLKqtoxiXUvtfOoVzq9+7XXpnE/9nk+9wtL1u9ynd04AGwfmN7SaJGkZLHfoPwxsTnJJkguBG4FDy9yDJHVrWU/vVNUrST4CPABcAByoqseXcJMjOU20TM6nXuH86tdel8751O/51CssUb+pqqVYryRpBfKOXEnqiKEvSR15Q4Z+kleTPDrw2DTunuYy0OfjSb6e5JeSrOi/kyR/J8k9Sb6R5JEk9yf5e+Pu60xJvn3G/C8k+Y1x9bOQOd6zK/pXlAz0+/UkX03yj8bd03ySrEvymSRPt/fsnyb5F+Pu62yS7EhSSd496nWvuOv0R+T/VNVl425iCD/oM8k7gc8AbwP2jbWreSQJ8HngYFXd2GrvBdYBfznO3t4Azpf37KzB9+41wK8C/3S8Lf2w9p79H8y8Z/9Vq70L+OBYG1vYTcCftOeR5sGKPqrsSVWdZOZO5I+0N+pK9LPA/62q354tVNXXq+p/jbEnjd/bgNPjbmIeHwC+f8Z79tmq+i9j7OmskvwY8DPALmYuax+pN+qR/o8mebRNf7OqVvR/5WZV1dPtl9K9E3hh3P3M4SeBR8bdxJAG3wMAa1nZ94Sc2e+vVtVnx9bNwmb7fQtwMTPhuhK9B/jquJtYpO3AF6vqL5O8mOSnqmpk/+7eqKF/vv1XWaP3mvdAkl8AVvIt+Ofbe3bw9M77gbuT/GSt8GvAk/wmM0fR36+qfzjufuZxE/CpNn1Pmzf034iS/F3gVeDkuHuZx+PAh8bdhFaWqvrTJBcBE6y89+7jwL+cnamqPa3XyfG1NL8ka5n5X9PfT1LM3MRaSf7DqD5QPae/QiSZAH4b+I0VfLT0IPDm9ptQAUjyD5L84zH2pDFrV5hcALw47l7m8CDwliT/bqD2t8bVzBA+BHy6qt5VVZuqaiPwTWBk/8Y80h+v2fOibwJeAT4N/Np4W5pfVVW71O3Xk3wM+C7wDPDRsTb2xnDmOf0vVtVKvmxzsN8AO6vq1XE2NJf2nt0B3J7kl4Fp4DvAx8bb2bxuAs785dl/0Op/PIoN+GsYJKkjnt6RpI4Y+pLUEUNfkjpi6EtSRwx9SeqIoS9JHTH0Jakj/x9umkRRinzseAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JEeCp4QNX1nS",
        "outputId": "93de2346-cf82-4d08-d776-c21a18926c18",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "#ploting the 2D graph before removal of the outlier\n",
        "plt.plot(data_train[\"adview\"])\n",
        "plt.show()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zkq_N_93UDEe"
      },
      "source": [
        "#Considering the adview column as per the objective of this model\n",
        "#removing the outliers: lets remove thevideos with adview grater than 2000000\n",
        "data_train = data_train[data_train[\"adview\"]<2000000]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-CspFqJlUDHE",
        "outputId": "f67da03a-f578-4aed-a3fa-3ca7a48542a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "#ploting the 2D graph to check the removal of the outlier\n",
        "plt.plot(data_train[\"adview\"])\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tzZdu5_XUDJs",
        "outputId": "6ae94ab0-cb59-49f4-e34b-e61776c28802",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "#Ploting Heatmap\n",
        "f, ax = plt.subplots(figsize=(10,8))\n",
        "corr = data_train.corr()\n",
        "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True), square=True, ax=ax, annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-iPPEal7cTJu"
      },
      "source": [
        "##3.Cleaning the datasets by removing missing values and other things"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnYNDcFxew1b",
        "outputId": "0183b67c-11d7-4d16-ba82-f59afc63fb6e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "#removing chharacter \"F\" present in data\n",
        "data_train = data_train[data_train.views != 'F']\n",
        "data_train = data_train[data_train.likes != 'F']\n",
        "data_train = data_train[data_train.dislikes != 'F']\n",
        "data_train = data_train[data_train.comment != 'F']\n",
        "\n",
        "data_train.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
              "      <th>published</th>\n",
              "      <th>duration</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VID_18655</td>\n",
              "      <td>40</td>\n",
              "      <td>1031602</td>\n",
              "      <td>8523</td>\n",
              "      <td>363</td>\n",
              "      <td>1095</td>\n",
              "      <td>2016-09-14</td>\n",
              "      <td>PT7M37S</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>VID_14135</td>\n",
              "      <td>2</td>\n",
              "      <td>1707</td>\n",
              "      <td>56</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2016-10-01</td>\n",
              "      <td>PT9M30S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>VID_2187</td>\n",
              "      <td>1</td>\n",
              "      <td>2023</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-07-02</td>\n",
              "      <td>PT2M16S</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VID_23096</td>\n",
              "      <td>6</td>\n",
              "      <td>620860</td>\n",
              "      <td>777</td>\n",
              "      <td>161</td>\n",
              "      <td>153</td>\n",
              "      <td>2016-07-27</td>\n",
              "      <td>PT4M22S</td>\n",
              "      <td>H</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>VID_10175</td>\n",
              "      <td>1</td>\n",
              "      <td>666</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-06-29</td>\n",
              "      <td>PT31S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       vidid  adview    views likes  ... comment   published duration category\n",
              "0  VID_18655      40  1031602  8523  ...    1095  2016-09-14  PT7M37S        F\n",
              "1  VID_14135       2     1707    56  ...       6  2016-10-01  PT9M30S        D\n",
              "2   VID_2187       1     2023    25  ...       2  2016-07-02  PT2M16S        C\n",
              "3  VID_23096       6   620860   777  ...     153  2016-07-27  PT4M22S        H\n",
              "4  VID_10175       1      666     1  ...       0  2016-06-29    PT31S        D\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "07ws3HyjYyVc",
        "outputId": "fab0ae51-5e29-4035-d663-b8fa108eafb5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        }
      },
      "source": [
        "data_train.tail(20)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
              "      <th>published</th>\n",
              "      <th>duration</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14976</th>\n",
              "      <td>VID_2132</td>\n",
              "      <td>1</td>\n",
              "      <td>12934</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-07-06</td>\n",
              "      <td>PT2M33S</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14978</th>\n",
              "      <td>VID_4424</td>\n",
              "      <td>1</td>\n",
              "      <td>216284</td>\n",
              "      <td>486</td>\n",
              "      <td>29</td>\n",
              "      <td>17</td>\n",
              "      <td>2014-04-27</td>\n",
              "      <td>PT3M49S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14979</th>\n",
              "      <td>VID_1314</td>\n",
              "      <td>272</td>\n",
              "      <td>1520441</td>\n",
              "      <td>18677</td>\n",
              "      <td>367</td>\n",
              "      <td>3971</td>\n",
              "      <td>2016-05-15</td>\n",
              "      <td>PT8M28S</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14980</th>\n",
              "      <td>VID_14670</td>\n",
              "      <td>231</td>\n",
              "      <td>1532</td>\n",
              "      <td>266</td>\n",
              "      <td>24</td>\n",
              "      <td>5</td>\n",
              "      <td>2016-02-26</td>\n",
              "      <td>PT5M9S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14981</th>\n",
              "      <td>VID_7541</td>\n",
              "      <td>1</td>\n",
              "      <td>168226</td>\n",
              "      <td>184</td>\n",
              "      <td>27</td>\n",
              "      <td>29</td>\n",
              "      <td>2014-03-31</td>\n",
              "      <td>PT2M13S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14982</th>\n",
              "      <td>VID_4956</td>\n",
              "      <td>1</td>\n",
              "      <td>45964</td>\n",
              "      <td>36</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2014-12-03</td>\n",
              "      <td>PT2M42S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14983</th>\n",
              "      <td>VID_7956</td>\n",
              "      <td>12</td>\n",
              "      <td>180204</td>\n",
              "      <td>361</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>2013-07-08</td>\n",
              "      <td>PT1M14S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14984</th>\n",
              "      <td>VID_13145</td>\n",
              "      <td>1</td>\n",
              "      <td>1511</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014-01-20</td>\n",
              "      <td>PT1M27S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14985</th>\n",
              "      <td>VID_566</td>\n",
              "      <td>1</td>\n",
              "      <td>32631</td>\n",
              "      <td>76</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>2014-01-03</td>\n",
              "      <td>PT13M24S</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14986</th>\n",
              "      <td>VID_17470</td>\n",
              "      <td>3207</td>\n",
              "      <td>22237</td>\n",
              "      <td>52</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>2016-09-21</td>\n",
              "      <td>PT5M18S</td>\n",
              "      <td>E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14987</th>\n",
              "      <td>VID_18084</td>\n",
              "      <td>1</td>\n",
              "      <td>303879</td>\n",
              "      <td>481</td>\n",
              "      <td>51</td>\n",
              "      <td>76</td>\n",
              "      <td>2014-06-12</td>\n",
              "      <td>PT2M17S</td>\n",
              "      <td>F</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14988</th>\n",
              "      <td>VID_22836</td>\n",
              "      <td>2</td>\n",
              "      <td>214596</td>\n",
              "      <td>1431</td>\n",
              "      <td>44</td>\n",
              "      <td>67</td>\n",
              "      <td>2015-08-06</td>\n",
              "      <td>PT3M32S</td>\n",
              "      <td>G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14989</th>\n",
              "      <td>VID_20638</td>\n",
              "      <td>22</td>\n",
              "      <td>1204587</td>\n",
              "      <td>10284</td>\n",
              "      <td>379</td>\n",
              "      <td>2016</td>\n",
              "      <td>2013-04-10</td>\n",
              "      <td>PT10H19S</td>\n",
              "      <td>G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14992</th>\n",
              "      <td>VID_11653</td>\n",
              "      <td>1</td>\n",
              "      <td>78021</td>\n",
              "      <td>422</td>\n",
              "      <td>19</td>\n",
              "      <td>14</td>\n",
              "      <td>2016-08-12</td>\n",
              "      <td>PT2M57S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14993</th>\n",
              "      <td>VID_13909</td>\n",
              "      <td>11</td>\n",
              "      <td>96816</td>\n",
              "      <td>122</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>2014-01-30</td>\n",
              "      <td>PT2M6S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>VID_31</td>\n",
              "      <td>2</td>\n",
              "      <td>525949</td>\n",
              "      <td>1137</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>2015-05-18</td>\n",
              "      <td>PT6M10S</td>\n",
              "      <td>A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>VID_5861</td>\n",
              "      <td>1</td>\n",
              "      <td>665673</td>\n",
              "      <td>3849</td>\n",
              "      <td>156</td>\n",
              "      <td>569</td>\n",
              "      <td>2015-10-20</td>\n",
              "      <td>PT3M56S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>VID_805</td>\n",
              "      <td>4</td>\n",
              "      <td>3479</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-08-23</td>\n",
              "      <td>PT3M13S</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>VID_19843</td>\n",
              "      <td>1</td>\n",
              "      <td>963</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2010-10-02</td>\n",
              "      <td>PT26S</td>\n",
              "      <td>G</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>VID_8534</td>\n",
              "      <td>1</td>\n",
              "      <td>15212</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2016-02-19</td>\n",
              "      <td>PT1M1S</td>\n",
              "      <td>D</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           vidid  adview    views  likes  ... comment   published  duration category\n",
              "14976   VID_2132       1    12934     30  ...       0  2016-07-06   PT2M33S        C\n",
              "14978   VID_4424       1   216284    486  ...      17  2014-04-27   PT3M49S        D\n",
              "14979   VID_1314     272  1520441  18677  ...    3971  2016-05-15   PT8M28S        B\n",
              "14980  VID_14670     231     1532    266  ...       5  2016-02-26    PT5M9S        D\n",
              "14981   VID_7541       1   168226    184  ...      29  2014-03-31   PT2M13S        D\n",
              "14982   VID_4956       1    45964     36  ...       2  2014-12-03   PT2M42S        D\n",
              "14983   VID_7956      12   180204    361  ...      20  2013-07-08   PT1M14S        D\n",
              "14984  VID_13145       1     1511      6  ...       0  2014-01-20   PT1M27S        D\n",
              "14985    VID_566       1    32631     76  ...      14  2014-01-03  PT13M24S        B\n",
              "14986  VID_17470    3207    22237     52  ...      14  2016-09-21   PT5M18S        E\n",
              "14987  VID_18084       1   303879    481  ...      76  2014-06-12   PT2M17S        F\n",
              "14988  VID_22836       2   214596   1431  ...      67  2015-08-06   PT3M32S        G\n",
              "14989  VID_20638      22  1204587  10284  ...    2016  2013-04-10  PT10H19S        G\n",
              "14992  VID_11653       1    78021    422  ...      14  2016-08-12   PT2M57S        D\n",
              "14993  VID_13909      11    96816    122  ...      11  2014-01-30    PT2M6S        D\n",
              "14994     VID_31       2   525949   1137  ...      86  2015-05-18   PT6M10S        A\n",
              "14995   VID_5861       1   665673   3849  ...     569  2015-10-20   PT3M56S        D\n",
              "14996    VID_805       4     3479     16  ...       1  2013-08-23   PT3M13S        B\n",
              "14997  VID_19843       1      963      0  ...       0  2010-10-02     PT26S        G\n",
              "14998   VID_8534       1    15212     22  ...       4  2016-02-19    PT1M1S        D\n",
              "\n",
              "[20 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sswlhmwoclXu",
        "outputId": "eb0ef2bb-b691-40cf-a830-f4c4ad41e147",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "#Assigning each category a number for category feature\n",
        "category = {'A':1 , 'B':2 , 'C':3 , 'D':4 , 'E':5 , 'F':6 , 'G':7 , 'H':8}\n",
        "data_train[\"category\"] = data_train[\"category\"].map(category)\n",
        "\n",
        "#Dataset after cleaning and categorizing the features\n",
        "data_train.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
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              "      <th>duration</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>VID_18655</td>\n",
              "      <td>40</td>\n",
              "      <td>1031602</td>\n",
              "      <td>8523</td>\n",
              "      <td>363</td>\n",
              "      <td>1095</td>\n",
              "      <td>2016-09-14</td>\n",
              "      <td>PT7M37S</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>VID_14135</td>\n",
              "      <td>2</td>\n",
              "      <td>1707</td>\n",
              "      <td>56</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2016-10-01</td>\n",
              "      <td>PT9M30S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>VID_2187</td>\n",
              "      <td>1</td>\n",
              "      <td>2023</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2016-07-02</td>\n",
              "      <td>PT2M16S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>VID_23096</td>\n",
              "      <td>6</td>\n",
              "      <td>620860</td>\n",
              "      <td>777</td>\n",
              "      <td>161</td>\n",
              "      <td>153</td>\n",
              "      <td>2016-07-27</td>\n",
              "      <td>PT4M22S</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>VID_10175</td>\n",
              "      <td>1</td>\n",
              "      <td>666</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-06-29</td>\n",
              "      <td>PT31S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       vidid  adview    views likes  ... comment   published duration category\n",
              "0  VID_18655      40  1031602  8523  ...    1095  2016-09-14  PT7M37S        6\n",
              "1  VID_14135       2     1707    56  ...       6  2016-10-01  PT9M30S        4\n",
              "2   VID_2187       1     2023    25  ...       2  2016-07-02  PT2M16S        3\n",
              "3  VID_23096       6   620860   777  ...     153  2016-07-27  PT4M22S        8\n",
              "4  VID_10175       1      666     1  ...       0  2016-06-29    PT31S        4\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qilfK3VkclaS",
        "outputId": "4e2d9a46-bd5b-4497-8bc1-2ac541e98c48",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        }
      },
      "source": [
        "data_train.tail(20)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
              "      <th>published</th>\n",
              "      <th>duration</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14976</th>\n",
              "      <td>VID_2132</td>\n",
              "      <td>1</td>\n",
              "      <td>12934</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2016-07-06</td>\n",
              "      <td>PT2M33S</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14978</th>\n",
              "      <td>VID_4424</td>\n",
              "      <td>1</td>\n",
              "      <td>216284</td>\n",
              "      <td>486</td>\n",
              "      <td>29</td>\n",
              "      <td>17</td>\n",
              "      <td>2014-04-27</td>\n",
              "      <td>PT3M49S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14979</th>\n",
              "      <td>VID_1314</td>\n",
              "      <td>272</td>\n",
              "      <td>1520441</td>\n",
              "      <td>18677</td>\n",
              "      <td>367</td>\n",
              "      <td>3971</td>\n",
              "      <td>2016-05-15</td>\n",
              "      <td>PT8M28S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14980</th>\n",
              "      <td>VID_14670</td>\n",
              "      <td>231</td>\n",
              "      <td>1532</td>\n",
              "      <td>266</td>\n",
              "      <td>24</td>\n",
              "      <td>5</td>\n",
              "      <td>2016-02-26</td>\n",
              "      <td>PT5M9S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14981</th>\n",
              "      <td>VID_7541</td>\n",
              "      <td>1</td>\n",
              "      <td>168226</td>\n",
              "      <td>184</td>\n",
              "      <td>27</td>\n",
              "      <td>29</td>\n",
              "      <td>2014-03-31</td>\n",
              "      <td>PT2M13S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14982</th>\n",
              "      <td>VID_4956</td>\n",
              "      <td>1</td>\n",
              "      <td>45964</td>\n",
              "      <td>36</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>2014-12-03</td>\n",
              "      <td>PT2M42S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14983</th>\n",
              "      <td>VID_7956</td>\n",
              "      <td>12</td>\n",
              "      <td>180204</td>\n",
              "      <td>361</td>\n",
              "      <td>17</td>\n",
              "      <td>20</td>\n",
              "      <td>2013-07-08</td>\n",
              "      <td>PT1M14S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14984</th>\n",
              "      <td>VID_13145</td>\n",
              "      <td>1</td>\n",
              "      <td>1511</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2014-01-20</td>\n",
              "      <td>PT1M27S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14985</th>\n",
              "      <td>VID_566</td>\n",
              "      <td>1</td>\n",
              "      <td>32631</td>\n",
              "      <td>76</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>2014-01-03</td>\n",
              "      <td>PT13M24S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14986</th>\n",
              "      <td>VID_17470</td>\n",
              "      <td>3207</td>\n",
              "      <td>22237</td>\n",
              "      <td>52</td>\n",
              "      <td>4</td>\n",
              "      <td>14</td>\n",
              "      <td>2016-09-21</td>\n",
              "      <td>PT5M18S</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14987</th>\n",
              "      <td>VID_18084</td>\n",
              "      <td>1</td>\n",
              "      <td>303879</td>\n",
              "      <td>481</td>\n",
              "      <td>51</td>\n",
              "      <td>76</td>\n",
              "      <td>2014-06-12</td>\n",
              "      <td>PT2M17S</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14988</th>\n",
              "      <td>VID_22836</td>\n",
              "      <td>2</td>\n",
              "      <td>214596</td>\n",
              "      <td>1431</td>\n",
              "      <td>44</td>\n",
              "      <td>67</td>\n",
              "      <td>2015-08-06</td>\n",
              "      <td>PT3M32S</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14989</th>\n",
              "      <td>VID_20638</td>\n",
              "      <td>22</td>\n",
              "      <td>1204587</td>\n",
              "      <td>10284</td>\n",
              "      <td>379</td>\n",
              "      <td>2016</td>\n",
              "      <td>2013-04-10</td>\n",
              "      <td>PT10H19S</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14992</th>\n",
              "      <td>VID_11653</td>\n",
              "      <td>1</td>\n",
              "      <td>78021</td>\n",
              "      <td>422</td>\n",
              "      <td>19</td>\n",
              "      <td>14</td>\n",
              "      <td>2016-08-12</td>\n",
              "      <td>PT2M57S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14993</th>\n",
              "      <td>VID_13909</td>\n",
              "      <td>11</td>\n",
              "      <td>96816</td>\n",
              "      <td>122</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>2014-01-30</td>\n",
              "      <td>PT2M6S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>VID_31</td>\n",
              "      <td>2</td>\n",
              "      <td>525949</td>\n",
              "      <td>1137</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>2015-05-18</td>\n",
              "      <td>PT6M10S</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>VID_5861</td>\n",
              "      <td>1</td>\n",
              "      <td>665673</td>\n",
              "      <td>3849</td>\n",
              "      <td>156</td>\n",
              "      <td>569</td>\n",
              "      <td>2015-10-20</td>\n",
              "      <td>PT3M56S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>VID_805</td>\n",
              "      <td>4</td>\n",
              "      <td>3479</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2013-08-23</td>\n",
              "      <td>PT3M13S</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>VID_19843</td>\n",
              "      <td>1</td>\n",
              "      <td>963</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2010-10-02</td>\n",
              "      <td>PT26S</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>VID_8534</td>\n",
              "      <td>1</td>\n",
              "      <td>15212</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>2016-02-19</td>\n",
              "      <td>PT1M1S</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           vidid  adview    views  ...   published  duration category\n",
              "14976   VID_2132       1    12934  ...  2016-07-06   PT2M33S        3\n",
              "14978   VID_4424       1   216284  ...  2014-04-27   PT3M49S        4\n",
              "14979   VID_1314     272  1520441  ...  2016-05-15   PT8M28S        2\n",
              "14980  VID_14670     231     1532  ...  2016-02-26    PT5M9S        4\n",
              "14981   VID_7541       1   168226  ...  2014-03-31   PT2M13S        4\n",
              "14982   VID_4956       1    45964  ...  2014-12-03   PT2M42S        4\n",
              "14983   VID_7956      12   180204  ...  2013-07-08   PT1M14S        4\n",
              "14984  VID_13145       1     1511  ...  2014-01-20   PT1M27S        4\n",
              "14985    VID_566       1    32631  ...  2014-01-03  PT13M24S        2\n",
              "14986  VID_17470    3207    22237  ...  2016-09-21   PT5M18S        5\n",
              "14987  VID_18084       1   303879  ...  2014-06-12   PT2M17S        6\n",
              "14988  VID_22836       2   214596  ...  2015-08-06   PT3M32S        7\n",
              "14989  VID_20638      22  1204587  ...  2013-04-10  PT10H19S        7\n",
              "14992  VID_11653       1    78021  ...  2016-08-12   PT2M57S        4\n",
              "14993  VID_13909      11    96816  ...  2014-01-30    PT2M6S        4\n",
              "14994     VID_31       2   525949  ...  2015-05-18   PT6M10S        1\n",
              "14995   VID_5861       1   665673  ...  2015-10-20   PT3M56S        4\n",
              "14996    VID_805       4     3479  ...  2013-08-23   PT3M13S        2\n",
              "14997  VID_19843       1      963  ...  2010-10-02     PT26S        7\n",
              "14998   VID_8534       1    15212  ...  2016-02-19    PT1M1S        4\n",
              "\n",
              "[20 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BAF877F6i3gP"
      },
      "source": [
        "##4.Transforming the attributes into numerical values and other necessary transformations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kK1yVHrtclea"
      },
      "source": [
        "#Converting all floating point numbers to integers for views, likes, comments, dislikes, and adviews\n",
        "\n",
        "data_train[\"views\"] = pd.to_numeric(data_train[\"views\"])\n",
        "data_train[\"comment\"] = pd.to_numeric(data_train[\"comment\"])\n",
        "data_train[\"likes\"] = pd.to_numeric(data_train[\"likes\"])\n",
        "data_train[\"dislikes\"] = pd.to_numeric(data_train[\"dislikes\"])\n",
        "data_train[\"adview\"] = pd.to_numeric(data_train[\"adview\"])\n",
        "\n",
        "column_vidid = data_train[\"vidid\"]"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p5hjOijqclg1",
        "outputId": "d45263e8-d1ea-4f4a-c80c-993262e7f090",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "#Encoding features like category, duration and vidid\n",
        "\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "data_train['duration'] = LabelEncoder().fit_transform(data_train['duration'])\n",
        "data_train['vidid'] = LabelEncoder().fit_transform(data_train['vidid'])\n",
        "data_train['published'] = LabelEncoder().fit_transform(data_train['published'])\n",
        "\n",
        "data_train.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5912</td>\n",
              "      <td>40</td>\n",
              "      <td>1031602</td>\n",
              "      <td>8523</td>\n",
              "      <td>363</td>\n",
              "      <td>1095</td>\n",
              "      <td>2168</td>\n",
              "      <td>2925</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2741</td>\n",
              "      <td>2</td>\n",
              "      <td>1707</td>\n",
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              "      <td>2</td>\n",
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              "      <td>2185</td>\n",
              "      <td>3040</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8138</td>\n",
              "      <td>1</td>\n",
              "      <td>2023</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2094</td>\n",
              "      <td>1863</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9004</td>\n",
              "      <td>6</td>\n",
              "      <td>620860</td>\n",
              "      <td>777</td>\n",
              "      <td>161</td>\n",
              "      <td>153</td>\n",
              "      <td>2119</td>\n",
              "      <td>2546</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>122</td>\n",
              "      <td>1</td>\n",
              "      <td>666</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2091</td>\n",
              "      <td>1963</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vidid  adview    views  likes  ...  comment  published  duration  category\n",
              "0   5912      40  1031602   8523  ...     1095       2168      2925         6\n",
              "1   2741       2     1707     56  ...        6       2185      3040         4\n",
              "2   8138       1     2023     25  ...        2       2094      1863         3\n",
              "3   9004       6   620860    777  ...      153       2119      2546         8\n",
              "4    122       1      666      1  ...        0       2091      1963         4\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C0NNPGrAYyYJ",
        "outputId": "c28a0d15-e23e-46df-dee3-e809d7ccf2ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "data_train.tail(10)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
              "      <th>published</th>\n",
              "      <th>duration</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14987</th>\n",
              "      <td>5510</td>\n",
              "      <td>1</td>\n",
              "      <td>303879</td>\n",
              "      <td>481</td>\n",
              "      <td>51</td>\n",
              "      <td>76</td>\n",
              "      <td>1345</td>\n",
              "      <td>1864</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14988</th>\n",
              "      <td>8829</td>\n",
              "      <td>2</td>\n",
              "      <td>214596</td>\n",
              "      <td>1431</td>\n",
              "      <td>44</td>\n",
              "      <td>67</td>\n",
              "      <td>1763</td>\n",
              "      <td>2231</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14989</th>\n",
              "      <td>7286</td>\n",
              "      <td>22</td>\n",
              "      <td>1204587</td>\n",
              "      <td>10284</td>\n",
              "      <td>379</td>\n",
              "      <td>2016</td>\n",
              "      <td>921</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14992</th>\n",
              "      <td>1105</td>\n",
              "      <td>1</td>\n",
              "      <td>78021</td>\n",
              "      <td>422</td>\n",
              "      <td>19</td>\n",
              "      <td>14</td>\n",
              "      <td>2135</td>\n",
              "      <td>1908</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14993</th>\n",
              "      <td>2591</td>\n",
              "      <td>11</td>\n",
              "      <td>96816</td>\n",
              "      <td>122</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>1212</td>\n",
              "      <td>1912</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>9868</td>\n",
              "      <td>2</td>\n",
              "      <td>525949</td>\n",
              "      <td>1137</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>1683</td>\n",
              "      <td>2833</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>11757</td>\n",
              "      <td>1</td>\n",
              "      <td>665673</td>\n",
              "      <td>3849</td>\n",
              "      <td>156</td>\n",
              "      <td>569</td>\n",
              "      <td>1838</td>\n",
              "      <td>2257</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>13262</td>\n",
              "      <td>4</td>\n",
              "      <td>3479</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1053</td>\n",
              "      <td>2210</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>6736</td>\n",
              "      <td>1</td>\n",
              "      <td>963</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "      <td>1364</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>13591</td>\n",
              "      <td>1</td>\n",
              "      <td>15212</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1960</td>\n",
              "      <td>1050</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       vidid  adview    views  likes  ...  comment  published  duration  category\n",
              "14987   5510       1   303879    481  ...       76       1345      1864         6\n",
              "14988   8829       2   214596   1431  ...       67       1763      2231         7\n",
              "14989   7286      22  1204587  10284  ...     2016        921         2         7\n",
              "14992   1105       1    78021    422  ...       14       2135      1908         4\n",
              "14993   2591      11    96816    122  ...       11       1212      1912         4\n",
              "14994   9868       2   525949   1137  ...       86       1683      2833         1\n",
              "14995  11757       1   665673   3849  ...      569       1838      2257         4\n",
              "14996  13262       4     3479     16  ...        1       1053      2210         2\n",
              "14997   6736       1      963      0  ...        0        222      1364         7\n",
              "14998  13591       1    15212     22  ...        4       1960      1050         4\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VsGWNKaQYycr",
        "outputId": "b5f60a84-9fcc-4a4e-df26-8c0db5ff7583",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "#Converting Time_in_sec for duration\n",
        "import datetime\n",
        "import time\n",
        "\n",
        "#defining a functionc\"checki\" to check the duration of the adds\n",
        "def checki(x):\n",
        "  y = x[2:]\n",
        "  h = ''\n",
        "  m = ''\n",
        "  s = ''\n",
        "  mm = ''\n",
        "  p = ['H' , 'M' , 'S']\n",
        "  \n",
        "  for i in y:\n",
        "    if i not in p:\n",
        "      mm+=i\n",
        "    else:\n",
        "      if (i == \"H\"):\n",
        "        h = mm\n",
        "        mm = ''\n",
        "      elif (i == \"M\"):\n",
        "        m = mm\n",
        "        mm = ''\n",
        "      else:\n",
        "        s = mm\n",
        "        mm = ''\n",
        "  \n",
        "  if (h == ''):\n",
        "    h = '00'\n",
        "  if (m == ''):\n",
        "    m = '00'\n",
        "  if (s == ''):\n",
        "    s = '00'\n",
        "\n",
        "  bp = h+':'+m+':'+s\n",
        "  return bp\n",
        "\n",
        "#reloading the csv file to the project and assiging it to train\n",
        "train = pd.read_csv(\"train.csv\")\n",
        "mp = pd.read_csv(\"train.csv\")[\"duration\"]\n",
        "\n",
        "#applying the \"checki\" function to the csv file\n",
        "time = mp.apply(checki)\n",
        "\n",
        "#defining a function \"func_sec\" to transform the time into seconds\n",
        "def func_sec(time_string):\n",
        "  h, m, s =time_string.split(':')\n",
        "  return int(h) * 3600 + int(m) * 60 + int(s)\n",
        "\n",
        "#calling the function \"func_sec\" and applying to the above function variable _time_\n",
        "time1 = time.apply(func_sec)\n",
        "\n",
        "#applying the above all functions to the the dataset (\"data_train\")\n",
        "data_train[\"duration\"] = time1\n",
        "\n",
        "#displaying the newly modified dataset\n",
        "data_train.head()\n"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
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              "      <td>8138</td>\n",
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              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2094</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9004</td>\n",
              "      <td>6</td>\n",
              "      <td>620860</td>\n",
              "      <td>777</td>\n",
              "      <td>161</td>\n",
              "      <td>153</td>\n",
              "      <td>2119</td>\n",
              "      <td>262</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>122</td>\n",
              "      <td>1</td>\n",
              "      <td>666</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2091</td>\n",
              "      <td>31</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   vidid  adview    views  likes  ...  comment  published  duration  category\n",
              "0   5912      40  1031602   8523  ...     1095       2168       457         6\n",
              "1   2741       2     1707     56  ...        6       2185       570         4\n",
              "2   8138       1     2023     25  ...        2       2094       136         3\n",
              "3   9004       6   620860    777  ...      153       2119       262         8\n",
              "4    122       1      666      1  ...        0       2091        31         4\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RAidux1YYyfl",
        "outputId": "6ec44463-793b-404d-c9fa-555e4fbd2755",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "data_train.tail(10)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vidid</th>\n",
              "      <th>adview</th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
              "      <th>published</th>\n",
              "      <th>duration</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>14987</th>\n",
              "      <td>5510</td>\n",
              "      <td>1</td>\n",
              "      <td>303879</td>\n",
              "      <td>481</td>\n",
              "      <td>51</td>\n",
              "      <td>76</td>\n",
              "      <td>1345</td>\n",
              "      <td>137</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14988</th>\n",
              "      <td>8829</td>\n",
              "      <td>2</td>\n",
              "      <td>214596</td>\n",
              "      <td>1431</td>\n",
              "      <td>44</td>\n",
              "      <td>67</td>\n",
              "      <td>1763</td>\n",
              "      <td>212</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14989</th>\n",
              "      <td>7286</td>\n",
              "      <td>22</td>\n",
              "      <td>1204587</td>\n",
              "      <td>10284</td>\n",
              "      <td>379</td>\n",
              "      <td>2016</td>\n",
              "      <td>921</td>\n",
              "      <td>36019</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14992</th>\n",
              "      <td>1105</td>\n",
              "      <td>1</td>\n",
              "      <td>78021</td>\n",
              "      <td>422</td>\n",
              "      <td>19</td>\n",
              "      <td>14</td>\n",
              "      <td>2135</td>\n",
              "      <td>177</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14993</th>\n",
              "      <td>2591</td>\n",
              "      <td>11</td>\n",
              "      <td>96816</td>\n",
              "      <td>122</td>\n",
              "      <td>9</td>\n",
              "      <td>11</td>\n",
              "      <td>1212</td>\n",
              "      <td>126</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14994</th>\n",
              "      <td>9868</td>\n",
              "      <td>2</td>\n",
              "      <td>525949</td>\n",
              "      <td>1137</td>\n",
              "      <td>83</td>\n",
              "      <td>86</td>\n",
              "      <td>1683</td>\n",
              "      <td>370</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14995</th>\n",
              "      <td>11757</td>\n",
              "      <td>1</td>\n",
              "      <td>665673</td>\n",
              "      <td>3849</td>\n",
              "      <td>156</td>\n",
              "      <td>569</td>\n",
              "      <td>1838</td>\n",
              "      <td>236</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14996</th>\n",
              "      <td>13262</td>\n",
              "      <td>4</td>\n",
              "      <td>3479</td>\n",
              "      <td>16</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1053</td>\n",
              "      <td>193</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14997</th>\n",
              "      <td>6736</td>\n",
              "      <td>1</td>\n",
              "      <td>963</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>222</td>\n",
              "      <td>26</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14998</th>\n",
              "      <td>13591</td>\n",
              "      <td>1</td>\n",
              "      <td>15212</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>1960</td>\n",
              "      <td>61</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       vidid  adview    views  likes  ...  comment  published  duration  category\n",
              "14987   5510       1   303879    481  ...       76       1345       137         6\n",
              "14988   8829       2   214596   1431  ...       67       1763       212         7\n",
              "14989   7286      22  1204587  10284  ...     2016        921     36019         7\n",
              "14992   1105       1    78021    422  ...       14       2135       177         4\n",
              "14993   2591      11    96816    122  ...       11       1212       126         4\n",
              "14994   9868       2   525949   1137  ...       86       1683       370         1\n",
              "14995  11757       1   665673   3849  ...      569       1838       236         4\n",
              "14996  13262       4     3479     16  ...        1       1053       193         2\n",
              "14997   6736       1      963      0  ...        0        222        26         7\n",
              "14998  13591       1    15212     22  ...        4       1960        61         4\n",
              "\n",
              "[10 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i5MULie9A_AW",
        "outputId": "272f9eb6-06d6-4e0c-c8d5-5e7a6879377b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        }
      },
      "source": [
        "#once again plotting the graph and checking the dataset\n",
        "#ploting the 2D graph to check the removal of the outlier\n",
        "plt.plot(data_train[\"adview\"])\n",
        "plt.show()"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cVj4zhCUBMV6",
        "outputId": "1f6c134c-df26-4785-9e10-1703e03bcedb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        }
      },
      "source": [
        "#Ploting Heatmap after succefully cleaning the dataset and transforming the necessary attributes\n",
        "f, ax = plt.subplots(figsize=(10,8))\n",
        "corr = data_train.corr()\n",
        "sns.heatmap(corr, mask=np.zeros_like(corr, dtype=np.bool), cmap=sns.diverging_palette(220, 10, as_cmap=True), square=True, ax=ax, annot=True)\n",
        "plt.show()"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1XLo6uutdU4"
      },
      "source": [
        "##5.Normalizing the data and spliting the data into training, validation and testset in the appropriate ratio\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AvGBLKk6Yymv",
        "outputId": "c1cc3b2b-2dcc-4008-93d9-7e6e8dad8220",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "#Spliting the data into 80:20 ratio\n",
        "\n",
        "Y_train = pd.DataFrame(data = data_train.iloc[: , 1].values , columns = ['target'])\n",
        "data_train = data_train.drop([\"adview\"] , axis = 1)\n",
        "data_train = data_train.drop([\"vidid\"] , axis = 1)\n",
        "data_train.head()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
              "      <th>published</th>\n",
              "      <th>duration</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1031602</td>\n",
              "      <td>8523</td>\n",
              "      <td>363</td>\n",
              "      <td>1095</td>\n",
              "      <td>2168</td>\n",
              "      <td>457</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1707</td>\n",
              "      <td>56</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>2185</td>\n",
              "      <td>570</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2094</td>\n",
              "      <td>136</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>620860</td>\n",
              "      <td>777</td>\n",
              "      <td>161</td>\n",
              "      <td>153</td>\n",
              "      <td>2119</td>\n",
              "      <td>262</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>666</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2091</td>\n",
              "      <td>31</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     views  likes  dislikes  comment  published  duration  category\n",
              "0  1031602   8523       363     1095       2168       457         6\n",
              "1     1707     56         2        6       2185       570         4\n",
              "2     2023     25         0        2       2094       136         3\n",
              "3   620860    777       161      153       2119       262         8\n",
              "4      666      1         0        0       2091        31         4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GS0W6WHcYypY",
        "outputId": "979782eb-99a5-47a9-8d94-e0fd28de8573",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "Y_train"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
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              "      <td>2</td>\n",
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              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14631</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14632</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14633</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14634</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14635</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14636 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       target\n",
              "0          40\n",
              "1           2\n",
              "2           1\n",
              "3           6\n",
              "4           1\n",
              "...       ...\n",
              "14631       2\n",
              "14632       1\n",
              "14633       4\n",
              "14634       1\n",
              "14635       1\n",
              "\n",
              "[14636 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xrGxDuS2YyuN"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train , x_test , y_train , y_test = train_test_split ( data_train , Y_train , test_size = 0.2 , random_state = 42)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NHXA1iejXyDt",
        "outputId": "585b8759-5ab0-4414-ec0b-01440f350926",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "x_train"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
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              "        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>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
              "      <th>published</th>\n",
              "      <th>duration</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13440</th>\n",
              "      <td>86739</td>\n",
              "      <td>117</td>\n",
              "      <td>9</td>\n",
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              "      <td>178</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1888</th>\n",
              "      <td>305445</td>\n",
              "      <td>3662</td>\n",
              "      <td>45</td>\n",
              "      <td>555</td>\n",
              "      <td>1484</td>\n",
              "      <td>257</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11378</th>\n",
              "      <td>737649</td>\n",
              "      <td>1702</td>\n",
              "      <td>96</td>\n",
              "      <td>53</td>\n",
              "      <td>1127</td>\n",
              "      <td>895</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5316</th>\n",
              "      <td>31648</td>\n",
              "      <td>140</td>\n",
              "      <td>24</td>\n",
              "      <td>14</td>\n",
              "      <td>1925</td>\n",
              "      <td>231</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13748</th>\n",
              "      <td>1804</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1049</td>\n",
              "      <td>232</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5522</th>\n",
              "      <td>204817</td>\n",
              "      <td>86</td>\n",
              "      <td>35</td>\n",
              "      <td>11</td>\n",
              "      <td>1237</td>\n",
              "      <td>218</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>883</th>\n",
              "      <td>1361366</td>\n",
              "      <td>1384</td>\n",
              "      <td>757</td>\n",
              "      <td>185</td>\n",
              "      <td>2145</td>\n",
              "      <td>240</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7445</th>\n",
              "      <td>39758</td>\n",
              "      <td>122</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>224</td>\n",
              "      <td>354</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11708 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         views  likes  dislikes  comment  published  duration  category\n",
              "13440    86739    117         9        4       1217       180         4\n",
              "9523   1419766    887       362      232       1547      5811         7\n",
              "3012     26086     19         0        9        966       178         6\n",
              "1888    305445   3662        45      555       1484       257         7\n",
              "11378   737649   1702        96       53       1127       895         4\n",
              "...        ...    ...       ...      ...        ...       ...       ...\n",
              "5316     31648    140        24       14       1925       231         7\n",
              "13748     1804      4         1        0       1049       232         4\n",
              "5522    204817     86        35       11       1237       218         4\n",
              "883    1361366   1384       757      185       2145       240         7\n",
              "7445     39758    122         6        3        224       354         4\n",
              "\n",
              "[11708 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-iefx7TXyGX",
        "outputId": "1e206e54-6247-4359-e52c-8fc685ed44b7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11708, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1Kjoj4g4XyRn",
        "outputId": "8ff054b0-1db4-4128-8973-19405a295f23",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "y_train"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13119</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9293</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2937</th>\n",
              "      <td>18782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1839</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11099</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5191</th>\n",
              "      <td>21127</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13418</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5390</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>860</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7270</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>11708 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       target\n",
              "13119      39\n",
              "9293        4\n",
              "2937    18782\n",
              "1839        2\n",
              "11099      20\n",
              "...       ...\n",
              "5191    21127\n",
              "13418       1\n",
              "5390        1\n",
              "860         2\n",
              "7270        1\n",
              "\n",
              "[11708 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CmnORktSXyfw",
        "outputId": "cc08bbb7-c804-41ed-ee74-6ef9e33fb768",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "y_train.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11708, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "txdOl1S2Xytc"
      },
      "source": [
        "#Normaliseing the data\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "X_train = scaler.fit_transform(x_train)\n",
        "X_test = scaler.fit_transform(x_test)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pF2N2A82Xyw_",
        "outputId": "116a131d-784f-4a79-be5a-fde32b6348f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.mean()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.1739096800320488"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XzBoNNAVXyq_",
        "outputId": "88141066-f75d-4830-be2e-3dabd71230f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "x_train , y_train"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(         views  likes  dislikes  comment  published  duration  category\n",
              " 13440    86739    117         9        4       1217       180         4\n",
              " 9523   1419766    887       362      232       1547      5811         7\n",
              " 3012     26086     19         0        9        966       178         6\n",
              " 1888    305445   3662        45      555       1484       257         7\n",
              " 11378   737649   1702        96       53       1127       895         4\n",
              " ...        ...    ...       ...      ...        ...       ...       ...\n",
              " 5316     31648    140        24       14       1925       231         7\n",
              " 13748     1804      4         1        0       1049       232         4\n",
              " 5522    204817     86        35       11       1237       218         4\n",
              " 883    1361366   1384       757      185       2145       240         7\n",
              " 7445     39758    122         6        3        224       354         4\n",
              " \n",
              " [11708 rows x 7 columns],        target\n",
              " 13119      39\n",
              " 9293        4\n",
              " 2937    18782\n",
              " 1839        2\n",
              " 11099      20\n",
              " ...       ...\n",
              " 5191    21127\n",
              " 13418       1\n",
              " 5390        1\n",
              " 860         2\n",
              " 7270        1\n",
              " \n",
              " [11708 rows x 1 columns])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AzTK-S1vXyou",
        "outputId": "158e304a-ff99-4d7d-95b2-7ca00d120b0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        }
      },
      "source": [
        "x_test , y_test"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(        views  likes  dislikes  comment  published  duration  category\n",
              " 4912   630898   1625       117       80        370       365         4\n",
              " 4921      384      2         0        0       2047        15         5\n",
              " 3813    36772    138         4        7       1281       904         2\n",
              " 9091    73482     74        21        4        345      1308         4\n",
              " 8485    86827    257         5      128       1999       481         4\n",
              " ...       ...    ...       ...      ...        ...       ...       ...\n",
              " 3788    40389    250         7        9       2230       189         4\n",
              " 4993   127443     61        10        0        501       143         4\n",
              " 6953    85311     25        12        1        599       408         5\n",
              " 769     67541    691        19       66       2130       363         4\n",
              " 10392  231471    226        29       31        387       683         5\n",
              " \n",
              " [2928 rows x 7 columns],        target\n",
              " 4791        5\n",
              " 4800        2\n",
              " 3717        5\n",
              " 8876        1\n",
              " 8288        2\n",
              " ...       ...\n",
              " 3694        1\n",
              " 4872        9\n",
              " 6791        1\n",
              " 750         2\n",
              " 10144      11\n",
              " \n",
              " [2928 rows x 1 columns])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Uhl89ruXymS"
      },
      "source": [
        "EDA_train = pd.read_csv('train.csv')"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-VKSXwoXykl",
        "outputId": "05713d7e-7ca2-414d-d94b-891cc388b3dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print(train.columns)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Index(['vidid', 'adview', 'views', 'likes', 'dislikes', 'comment', 'published',\n",
            "       'duration', 'category'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RGgh9_T7XyjK",
        "outputId": "43345f77-6121-4ecb-d7fb-dae1e798b186",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 218
        }
      },
      "source": [
        "EDA_train['adview'].value_counts()"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1         6397\n",
              "2         2232\n",
              "3         1106\n",
              "4          769\n",
              "5          509\n",
              "          ... \n",
              "200815       1\n",
              "28683        1\n",
              "26600        1\n",
              "2020         1\n",
              "20330        1\n",
              "Name: adview, Length: 999, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7vc2aQ3AXydS",
        "outputId": "ae67cbea-ef89-4e12-b2dd-20b9c8c2c52d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        }
      },
      "source": [
        "EDA_train.plot(kind=\"scatter\",x=\"adview\",y=\"likes\")\n",
        "plt.show()"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "erPYqm6_sGgO"
      },
      "source": [
        "##6.Using Linear regression, Support vector Regressor for training and get errors"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z3gnb0JbXybJ"
      },
      "source": [
        "#Evaluation metrics\n",
        "from sklearn import  metrics\n",
        "\n",
        "def print_error(x_test, y_test, model_name):\n",
        "    prediction = model_name.predict(x_test)\n",
        "    \n",
        "    print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, prediction))\n",
        "    print('Mean Squared Error:', metrics.mean_squared_error(y_test, prediction))\n",
        "    print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, prediction)))"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MIeIJrqOXyZK",
        "outputId": "e059cc0b-3c63-4d40-e381-43c8358056fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "#Linear Regression\n",
        "\n",
        "from sklearn import linear_model\n",
        "linear_regression = linear_model.LinearRegression()\n",
        "linear_regression.fit(x_train, y_train)\n",
        "print_error(x_test, y_test, linear_regression)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 3555.8238945996677\n",
            "Mean Squared Error: 831728534.4570614\n",
            "Root Mean Squared Error: 28839.70413262004\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uogLPhLNXyXk",
        "outputId": "992825f9-5f13-435c-fd3d-8900a540ff1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        }
      },
      "source": [
        "data_train.describe()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>views</th>\n",
              "      <th>likes</th>\n",
              "      <th>dislikes</th>\n",
              "      <th>comment</th>\n",
              "      <th>published</th>\n",
              "      <th>duration</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.463600e+04</td>\n",
              "      <td>14636.000000</td>\n",
              "      <td>14636.000000</td>\n",
              "      <td>14636.000000</td>\n",
              "      <td>14636.000000</td>\n",
              "      <td>14636.000000</td>\n",
              "      <td>14636.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.107934e+05</td>\n",
              "      <td>2784.093946</td>\n",
              "      <td>254.150724</td>\n",
              "      <td>409.035597</td>\n",
              "      <td>1547.316617</td>\n",
              "      <td>1040.232441</td>\n",
              "      <td>4.607065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.731062e+06</td>\n",
              "      <td>8936.295816</td>\n",
              "      <td>1029.257991</td>\n",
              "      <td>1511.180179</td>\n",
              "      <td>582.891902</td>\n",
              "      <td>2316.888900</td>\n",
              "      <td>1.576242</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.900000e+01</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.151425e+04</td>\n",
              "      <td>88.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>1135.000000</td>\n",
              "      <td>187.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.586610e+05</td>\n",
              "      <td>450.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>46.000000</td>\n",
              "      <td>1699.500000</td>\n",
              "      <td>325.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.829575e+05</td>\n",
              "      <td>1861.500000</td>\n",
              "      <td>166.250000</td>\n",
              "      <td>224.000000</td>\n",
              "      <td>2064.000000</td>\n",
              "      <td>723.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.380479e+08</td>\n",
              "      <td>283824.000000</td>\n",
              "      <td>49449.000000</td>\n",
              "      <td>75045.000000</td>\n",
              "      <td>2383.000000</td>\n",
              "      <td>42317.000000</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              views          likes  ...      duration      category\n",
              "count  1.463600e+04   14636.000000  ...  14636.000000  14636.000000\n",
              "mean   7.107934e+05    2784.093946  ...   1040.232441      4.607065\n",
              "std    2.731062e+06    8936.295816  ...   2316.888900      1.576242\n",
              "min    4.900000e+01       0.000000  ...      0.000000      1.000000\n",
              "25%    3.151425e+04      88.000000  ...    187.000000      4.000000\n",
              "50%    1.586610e+05     450.000000  ...    325.000000      4.000000\n",
              "75%    5.829575e+05    1861.500000  ...    723.000000      6.000000\n",
              "max    1.380479e+08  283824.000000  ...  42317.000000      8.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zXnmHI6nXyVp",
        "outputId": "47eab6a0-c0cd-4bef-8fe8-79a93636f5da",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "data_train.isnull().any()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "views        False\n",
              "likes        False\n",
              "dislikes     False\n",
              "comment      False\n",
              "published    False\n",
              "duration     False\n",
              "category     False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jthc3LlZXyM4",
        "outputId": "6df68bba-7bb5-45d7-d47b-55eb42f09a51",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "linear_regression = linear_model.LinearRegression()\n",
        "linear_regression.fit(x_train, y_train)\n",
        "print_error(x_test, y_test, linear_regression)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 3555.8238945996677\n",
            "Mean Squared Error: 831728534.4570614\n",
            "Root Mean Squared Error: 28839.70413262004\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FYTkK58H74oE",
        "outputId": "03e603af-1d2e-4ca2-e6b3-9d1e17e23687",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "#Support vector Regressioin\n",
        "from sklearn.svm import SVR\n",
        "supportvector_regressor = SVR()\n",
        "supportvector_regressor.fit(x_train, y_train)\n",
        "print_error(x_test , y_test , supportvector_regressor)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/validation.py:760: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 3555.8238945996677\n",
            "Mean Squared Error: 831728534.4570614\n",
            "Root Mean Squared Error: 28839.70413262004\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pVPXedcV7w4I"
      },
      "source": [
        "##7.Using Decision Tree Regressor and Random Forest Regressors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CNdJF0_NXyBa",
        "outputId": "4e19477c-d837-46e5-d880-7108e8f36fed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "#Decision Tree Regressor\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "decision_tree = DecisionTreeRegressor()\n",
        "decision_tree.fit(x_train ,y_train)\n",
        "print_error(x_test , y_test , decision_tree)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 2869.2206284153003\n",
            "Mean Squared Error: 1543602842.146858\n",
            "Root Mean Squared Error: 39288.71138312961\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dvC21Yt-X1A",
        "outputId": "d374ec7d-894e-4cb4-f50c-2f901cd1e746",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "#Random Forest Regressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "n_estimators = 200\n",
        "max_depth = 25\n",
        "min_samples_split = 15\n",
        "min_samples_leaf = 2\n",
        "random_forest = RandomForestRegressor(n_estimators = n_estimators , max_depth = max_depth, min_samples_split =  min_samples_split  , min_samples_leaf = min_samples_leaf )\n",
        "random_forest.fit(x_train,y_train)\n",
        "print_error(x_test , y_test , random_forest)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 2893.01891668815\n",
            "Mean Squared Error: 666623665.5219337\n",
            "Root Mean Squared Error: 25819.056247700722\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PlucTuOR_ILp"
      },
      "source": [
        "##8.Building An Artificial Neural Network & Training it With Different Layers & Hyperparameteres using Keras"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yf1aXCIV-X4K",
        "outputId": "ac1a4eca-4fdd-4a79-8a3f-156cc03f8f75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "#Artiificial Neural Network\n",
        "\n",
        "import keras\n",
        "from keras.layers import Dense\n",
        "\n",
        "ann = keras.models.Sequential([\n",
        "                              Dense(6,activation = \"relu\", \n",
        "                              input_shape = x_train.shape[1:]),\n",
        "                              Dense(6, activation = \"relu\"), \n",
        "                              Dense(1)\n",
        "                             ])\n",
        "\n",
        "optimizer = keras.optimizers.Adam()\n",
        "loss = keras.losses.mean_squared_error\n",
        "ann.compile(optimizer = optimizer , loss = loss , metrics = [\"mean_squared_error\"])\n",
        "\n",
        "history = ann.fit(x_train , y_train , epochs = 100)\n"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "366/366 [==============================] - 0s 906us/step - loss: 88570314752.0000 - mean_squared_error: 88570314752.0000\n",
            "Epoch 2/100\n",
            "366/366 [==============================] - 0s 956us/step - loss: 1494068864.0000 - mean_squared_error: 1494068864.0000\n",
            "Epoch 3/100\n",
            "366/366 [==============================] - 0s 939us/step - loss: 771080960.0000 - mean_squared_error: 771080960.0000\n",
            "Epoch 4/100\n",
            "366/366 [==============================] - 0s 939us/step - loss: 770014208.0000 - mean_squared_error: 770014208.0000\n",
            "Epoch 5/100\n",
            "366/366 [==============================] - 0s 952us/step - loss: 776379904.0000 - mean_squared_error: 776379904.0000\n",
            "Epoch 6/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 775708992.0000 - mean_squared_error: 775708992.0000\n",
            "Epoch 7/100\n",
            "366/366 [==============================] - 0s 909us/step - loss: 771384640.0000 - mean_squared_error: 771384640.0000\n",
            "Epoch 8/100\n",
            "366/366 [==============================] - 0s 930us/step - loss: 770485184.0000 - mean_squared_error: 770485184.0000\n",
            "Epoch 9/100\n",
            "366/366 [==============================] - 0s 988us/step - loss: 779768320.0000 - mean_squared_error: 779768320.0000\n",
            "Epoch 10/100\n",
            "366/366 [==============================] - 0s 950us/step - loss: 778507648.0000 - mean_squared_error: 778507648.0000\n",
            "Epoch 11/100\n",
            "366/366 [==============================] - 0s 953us/step - loss: 775333120.0000 - mean_squared_error: 775333120.0000\n",
            "Epoch 12/100\n",
            "366/366 [==============================] - 0s 911us/step - loss: 770689536.0000 - mean_squared_error: 770689536.0000\n",
            "Epoch 13/100\n",
            "366/366 [==============================] - 0s 963us/step - loss: 775651328.0000 - mean_squared_error: 775651328.0000\n",
            "Epoch 14/100\n",
            "366/366 [==============================] - 0s 997us/step - loss: 824919168.0000 - mean_squared_error: 824919168.0000\n",
            "Epoch 15/100\n",
            "366/366 [==============================] - 0s 909us/step - loss: 778497920.0000 - mean_squared_error: 778497920.0000\n",
            "Epoch 16/100\n",
            "366/366 [==============================] - 0s 927us/step - loss: 814006848.0000 - mean_squared_error: 814006848.0000\n",
            "Epoch 17/100\n",
            "366/366 [==============================] - 0s 928us/step - loss: 796937472.0000 - mean_squared_error: 796937472.0000\n",
            "Epoch 18/100\n",
            "366/366 [==============================] - 0s 984us/step - loss: 820423936.0000 - mean_squared_error: 820423936.0000\n",
            "Epoch 19/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 790282240.0000 - mean_squared_error: 790282240.0000\n",
            "Epoch 20/100\n",
            "366/366 [==============================] - 0s 945us/step - loss: 821464448.0000 - mean_squared_error: 821464448.0000\n",
            "Epoch 21/100\n",
            "366/366 [==============================] - 0s 897us/step - loss: 808069312.0000 - mean_squared_error: 808069312.0000\n",
            "Epoch 22/100\n",
            "366/366 [==============================] - 0s 957us/step - loss: 1028242496.0000 - mean_squared_error: 1028242496.0000\n",
            "Epoch 23/100\n",
            "366/366 [==============================] - 0s 952us/step - loss: 909309120.0000 - mean_squared_error: 909309120.0000\n",
            "Epoch 24/100\n",
            "366/366 [==============================] - 0s 932us/step - loss: 853569856.0000 - mean_squared_error: 853569856.0000\n",
            "Epoch 25/100\n",
            "366/366 [==============================] - 0s 910us/step - loss: 936481472.0000 - mean_squared_error: 936481472.0000\n",
            "Epoch 26/100\n",
            "366/366 [==============================] - 0s 920us/step - loss: 902646528.0000 - mean_squared_error: 902646528.0000\n",
            "Epoch 27/100\n",
            "366/366 [==============================] - 0s 914us/step - loss: 790256768.0000 - mean_squared_error: 790256768.0000\n",
            "Epoch 28/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 810106624.0000 - mean_squared_error: 810106624.0000\n",
            "Epoch 29/100\n",
            "366/366 [==============================] - 0s 952us/step - loss: 1112053248.0000 - mean_squared_error: 1112053248.0000\n",
            "Epoch 30/100\n",
            "366/366 [==============================] - 0s 926us/step - loss: 936227520.0000 - mean_squared_error: 936227520.0000\n",
            "Epoch 31/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 779623168.0000 - mean_squared_error: 779623168.0000\n",
            "Epoch 32/100\n",
            "366/366 [==============================] - 0s 917us/step - loss: 802906752.0000 - mean_squared_error: 802906752.0000\n",
            "Epoch 33/100\n",
            "366/366 [==============================] - 0s 913us/step - loss: 1441936768.0000 - mean_squared_error: 1441936768.0000\n",
            "Epoch 34/100\n",
            "366/366 [==============================] - 0s 928us/step - loss: 786180864.0000 - mean_squared_error: 786180864.0000\n",
            "Epoch 35/100\n",
            "366/366 [==============================] - 0s 934us/step - loss: 885366528.0000 - mean_squared_error: 885366528.0000\n",
            "Epoch 36/100\n",
            "366/366 [==============================] - 0s 924us/step - loss: 772611712.0000 - mean_squared_error: 772611712.0000\n",
            "Epoch 37/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 793704704.0000 - mean_squared_error: 793704704.0000\n",
            "Epoch 38/100\n",
            "366/366 [==============================] - 0s 899us/step - loss: 793576768.0000 - mean_squared_error: 793576768.0000\n",
            "Epoch 39/100\n",
            "366/366 [==============================] - 0s 915us/step - loss: 754806336.0000 - mean_squared_error: 754806336.0000\n",
            "Epoch 40/100\n",
            "366/366 [==============================] - 0s 949us/step - loss: 865173824.0000 - mean_squared_error: 865173824.0000\n",
            "Epoch 41/100\n",
            "366/366 [==============================] - 0s 917us/step - loss: 1023374528.0000 - mean_squared_error: 1023374528.0000\n",
            "Epoch 42/100\n",
            "366/366 [==============================] - 0s 944us/step - loss: 1160840576.0000 - mean_squared_error: 1160840576.0000\n",
            "Epoch 43/100\n",
            "366/366 [==============================] - 0s 993us/step - loss: 782671680.0000 - mean_squared_error: 782671680.0000\n",
            "Epoch 44/100\n",
            "366/366 [==============================] - 0s 904us/step - loss: 1016576512.0000 - mean_squared_error: 1016576512.0000\n",
            "Epoch 45/100\n",
            "366/366 [==============================] - 0s 911us/step - loss: 799910336.0000 - mean_squared_error: 799910336.0000\n",
            "Epoch 46/100\n",
            "366/366 [==============================] - 0s 995us/step - loss: 777626688.0000 - mean_squared_error: 777626688.0000\n",
            "Epoch 47/100\n",
            "366/366 [==============================] - 0s 931us/step - loss: 805460416.0000 - mean_squared_error: 805460416.0000\n",
            "Epoch 48/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 778972864.0000 - mean_squared_error: 778972864.0000\n",
            "Epoch 49/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 792567424.0000 - mean_squared_error: 792567424.0000\n",
            "Epoch 50/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 775266368.0000 - mean_squared_error: 775266368.0000\n",
            "Epoch 51/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 783839104.0000 - mean_squared_error: 783839104.0000\n",
            "Epoch 52/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 976758336.0000 - mean_squared_error: 976758336.0000\n",
            "Epoch 53/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 781297024.0000 - mean_squared_error: 781297024.0000\n",
            "Epoch 54/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 763913600.0000 - mean_squared_error: 763913600.0000\n",
            "Epoch 55/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 792499584.0000 - mean_squared_error: 792499584.0000\n",
            "Epoch 56/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 852964928.0000 - mean_squared_error: 852964928.0000\n",
            "Epoch 57/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 782036352.0000 - mean_squared_error: 782036352.0000\n",
            "Epoch 58/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 871620224.0000 - mean_squared_error: 871620224.0000\n",
            "Epoch 59/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 789996672.0000 - mean_squared_error: 789996672.0000\n",
            "Epoch 60/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 779478592.0000 - mean_squared_error: 779478592.0000\n",
            "Epoch 61/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 794622528.0000 - mean_squared_error: 794622528.0000\n",
            "Epoch 62/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 813562944.0000 - mean_squared_error: 813562944.0000\n",
            "Epoch 63/100\n",
            "366/366 [==============================] - 0s 947us/step - loss: 770400640.0000 - mean_squared_error: 770400640.0000\n",
            "Epoch 64/100\n",
            "366/366 [==============================] - 0s 956us/step - loss: 776831040.0000 - mean_squared_error: 776831040.0000\n",
            "Epoch 65/100\n",
            "366/366 [==============================] - 0s 973us/step - loss: 784093952.0000 - mean_squared_error: 784093952.0000\n",
            "Epoch 66/100\n",
            "366/366 [==============================] - 0s 959us/step - loss: 772694272.0000 - mean_squared_error: 772694272.0000\n",
            "Epoch 67/100\n",
            "366/366 [==============================] - 0s 928us/step - loss: 767665920.0000 - mean_squared_error: 767665920.0000\n",
            "Epoch 68/100\n",
            "366/366 [==============================] - 0s 925us/step - loss: 766949312.0000 - mean_squared_error: 766949312.0000\n",
            "Epoch 69/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 766112192.0000 - mean_squared_error: 766112192.0000\n",
            "Epoch 70/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 764876800.0000 - mean_squared_error: 764876800.0000\n",
            "Epoch 71/100\n",
            "366/366 [==============================] - 0s 983us/step - loss: 771774976.0000 - mean_squared_error: 771774976.0000\n",
            "Epoch 72/100\n",
            "366/366 [==============================] - 0s 951us/step - loss: 770630976.0000 - mean_squared_error: 770630976.0000\n",
            "Epoch 73/100\n",
            "366/366 [==============================] - 0s 956us/step - loss: 764853440.0000 - mean_squared_error: 764853440.0000\n",
            "Epoch 74/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 764572992.0000 - mean_squared_error: 764572992.0000\n",
            "Epoch 75/100\n",
            "366/366 [==============================] - 0s 963us/step - loss: 767603584.0000 - mean_squared_error: 767603584.0000\n",
            "Epoch 76/100\n",
            "366/366 [==============================] - 0s 977us/step - loss: 766518912.0000 - mean_squared_error: 766518912.0000\n",
            "Epoch 77/100\n",
            "366/366 [==============================] - 0s 994us/step - loss: 764056512.0000 - mean_squared_error: 764056512.0000\n",
            "Epoch 78/100\n",
            "366/366 [==============================] - 0s 988us/step - loss: 763902848.0000 - mean_squared_error: 763902848.0000\n",
            "Epoch 79/100\n",
            "366/366 [==============================] - 0s 1ms/step - loss: 763831104.0000 - mean_squared_error: 763831104.0000\n",
            "Epoch 80/100\n",
            "366/366 [==============================] - 0s 931us/step - loss: 765573312.0000 - mean_squared_error: 765573312.0000\n",
            "Epoch 81/100\n",
            "366/366 [==============================] - 0s 944us/step - loss: 768976064.0000 - mean_squared_error: 768976064.0000\n",
            "Epoch 82/100\n",
            "366/366 [==============================] - 0s 946us/step - loss: 765099264.0000 - mean_squared_error: 765099264.0000\n",
            "Epoch 83/100\n",
            "366/366 [==============================] - 0s 989us/step - loss: 767061760.0000 - mean_squared_error: 767061760.0000\n",
            "Epoch 84/100\n",
            "366/366 [==============================] - 0s 913us/step - loss: 766704128.0000 - mean_squared_error: 766704128.0000\n",
            "Epoch 85/100\n",
            "366/366 [==============================] - 0s 916us/step - loss: 766623808.0000 - mean_squared_error: 766623808.0000\n",
            "Epoch 86/100\n",
            "366/366 [==============================] - 0s 941us/step - loss: 771779072.0000 - mean_squared_error: 771779072.0000\n",
            "Epoch 87/100\n",
            "366/366 [==============================] - 0s 956us/step - loss: 766575424.0000 - mean_squared_error: 766575424.0000\n",
            "Epoch 88/100\n",
            "366/366 [==============================] - 0s 947us/step - loss: 766332416.0000 - mean_squared_error: 766332416.0000\n",
            "Epoch 89/100\n",
            "366/366 [==============================] - 0s 931us/step - loss: 765475776.0000 - mean_squared_error: 765475776.0000\n",
            "Epoch 90/100\n",
            "366/366 [==============================] - 0s 941us/step - loss: 765697984.0000 - mean_squared_error: 765697984.0000\n",
            "Epoch 91/100\n",
            "366/366 [==============================] - 0s 927us/step - loss: 770212288.0000 - mean_squared_error: 770212288.0000\n",
            "Epoch 92/100\n",
            "366/366 [==============================] - 0s 990us/step - loss: 765673024.0000 - mean_squared_error: 765673024.0000\n",
            "Epoch 93/100\n",
            "366/366 [==============================] - 0s 928us/step - loss: 766649152.0000 - mean_squared_error: 766649152.0000\n",
            "Epoch 94/100\n",
            "366/366 [==============================] - 0s 948us/step - loss: 765323968.0000 - mean_squared_error: 765323968.0000\n",
            "Epoch 95/100\n",
            "366/366 [==============================] - 0s 929us/step - loss: 766557312.0000 - mean_squared_error: 766557312.0000\n",
            "Epoch 96/100\n",
            "366/366 [==============================] - 0s 954us/step - loss: 763995200.0000 - mean_squared_error: 763995200.0000\n",
            "Epoch 97/100\n",
            "366/366 [==============================] - 0s 939us/step - loss: 766431744.0000 - mean_squared_error: 766431744.0000\n",
            "Epoch 98/100\n",
            "366/366 [==============================] - 0s 943us/step - loss: 764826560.0000 - mean_squared_error: 764826560.0000\n",
            "Epoch 99/100\n",
            "366/366 [==============================] - 0s 941us/step - loss: 765446080.0000 - mean_squared_error: 765446080.0000\n",
            "Epoch 100/100\n",
            "366/366 [==============================] - 0s 975us/step - loss: 763812096.0000 - mean_squared_error: 763812096.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y8s1RfYe-X8O",
        "outputId": "677709fa-194f-412d-b44d-55d0699aecd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "ann.summary()"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 6)                 48        \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 6)                 42        \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 7         \n",
            "=================================================================\n",
            "Total params: 97\n",
            "Trainable params: 97\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7HydQ3jM-X-0",
        "outputId": "64766b16-483d-4726-f540-1b720038a502",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "print_error(x_test , y_test , ann)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 3093.9100317188786\n",
            "Mean Squared Error: 845877255.4358001\n",
            "Root Mean Squared Error: 29083.969045434635\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qw4oDnqnIc6I"
      },
      "source": [
        "##9.Picking the best Model based on error as well as generalisation.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PQtz0WyM-YDd",
        "outputId": "64a2b2ba-2b40-425f-e0c7-574eb6d73bcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "# 1. Lineae Regression\n",
        "print_error(x_test, y_test, linear_regression)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 3555.8238945996677\n",
            "Mean Squared Error: 831728534.4570614\n",
            "Root Mean Squared Error: 28839.70413262004\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4vMvfFU_-YIh",
        "outputId": "cadea253-92c1-4560-eda1-5c944bc485ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "# 2. Support Vector Regressor\n",
        "print_error(x_test , y_test , supportvector_regressor)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 1696.9072023240883\n",
            "Mean Squared Error: 833685358.450164\n",
            "Root Mean Squared Error: 28873.610069580216\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aqXJwa0u-YNr",
        "outputId": "5b5ecb55-0d45-473a-c60f-ffb494435563",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "# 3. Decision Tree Regressor\n",
        "print_error(x_test , y_test , decision_tree)"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 2869.2206284153003\n",
            "Mean Squared Error: 1543602842.146858\n",
            "Root Mean Squared Error: 39288.71138312961\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQqB6afV-YQL",
        "outputId": "c89f33b2-7fdd-414f-88e1-f99ea18d7150",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "# 4. Random Forest Regressor\n",
        "print_error(x_test , y_test , random_forest)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean Absolute Error: 2893.01891668815\n",
            "Mean Squared Error: 666623665.5219337\n",
            "Root Mean Squared Error: 25819.056247700722\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yPw4rv8FKxS2"
      },
      "source": [
        "Decision tree regression is best bodel based on errors and also by gerneralization.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aZECoot6Lz_C"
      },
      "source": [
        "##10.Saving the model and predicting on test set.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4na5cpr-YUv",
        "outputId": "068f4546-b202-4adf-f9ff-b95e2ac6a48a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Saving Scikitlearn models\n",
        "import joblib\n",
        "joblib.dump(decision_tree , \"decisiontree_youtubeadview.pkl\")\n"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['decisiontree_youtubeadview.pkl']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b5H59dnc-YX9"
      },
      "source": [
        "#Saving keras Artificial Neural Network Model\n",
        "ann.save(\"ann_youtubeadview.h5\")"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NyAosCSj-Yck"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kee6xCDW-YgF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CjiO9aCo-Yas"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gVej87a0-YTA"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WrNqJrOw-YLj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XwXQPEon-YGJ"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jjIzraqq-YBX"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ntJvwNY-X6n"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}