Social_Media.ipynb

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        "<a href=\"https://colab.research.google.com/github/er-prateek-tripathi/Social_Media_Analysis/blob/master/Social_Media.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# Social Media Analysis and Visualization"
      ],
      "metadata": {
        "id": "V1zwxtaBrM5k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing Related Libraries"
      ],
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        "id": "3zX9u8r0qd0w"
      }
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      "cell_type": "code",
      "execution_count": 29,
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reading Dataset"
      ],
      "metadata": {
        "id": "_QJZ049BqmsE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('Twitter_Analysis.csv')\n",
        "df"
      ],
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      "execution_count": 3,
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              "             Date Category  Likes  Shares  Reposts  Comments\n",
              "0      2015-01-01  Fashion   7495     379      186      9386\n",
              "1      2015-01-01   Health   1434     741       53      9397\n",
              "2      2015-01-01   Family   6412     686       55      9525\n",
              "3      2015-01-01  Culture   3894     914      127      9728\n",
              "4      2015-01-01   Travel   9825     614       29      4575\n",
              "...           ...      ...    ...     ...      ...       ...\n",
              "89995  2023-12-30     Food   4435     553      165      8142\n",
              "89996  2023-12-30  Fitness   9157     846       84      7711\n",
              "89997  2023-12-30    Music   8197     714       97      5392\n",
              "89998  2023-12-30  Fashion   6662     119      165      3477\n",
              "89999  2023-12-31   Travel   8747     154       74      9426\n",
              "\n",
              "[90000 rows x 6 columns]"
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            "text/plain": [
              "              Likes        Shares       Reposts      Comments\n",
              "count  90000.000000  90000.000000  90000.000000  90000.000000\n",
              "mean    4988.509044    500.666144     99.790622   5008.241367\n",
              "std     2886.904448    288.712012     57.751705   2887.542860\n",
              "min        0.000000      0.000000      0.000000      0.000000\n",
              "25%     2467.000000    250.000000     50.000000   2509.000000\n",
              "50%     4996.000000    502.000000    100.000000   5018.500000\n",
              "75%     7485.000000    750.000000    150.000000   7509.000000\n",
              "max     9999.000000    999.000000    199.000000   9999.000000"
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              "    .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>Likes</th>\n",
              "      <th>Shares</th>\n",
              "      <th>Reposts</th>\n",
              "      <th>Comments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>90000.000000</td>\n",
              "      <td>90000.000000</td>\n",
              "      <td>90000.000000</td>\n",
              "      <td>90000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4988.509044</td>\n",
              "      <td>500.666144</td>\n",
              "      <td>99.790622</td>\n",
              "      <td>5008.241367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2886.904448</td>\n",
              "      <td>288.712012</td>\n",
              "      <td>57.751705</td>\n",
              "      <td>2887.542860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2467.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>2509.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4996.000000</td>\n",
              "      <td>502.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>5018.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7485.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>7509.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9999.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>9999.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c6c4366-bd2d-4054-aefc-52da227508d1')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9c6c4366-bd2d-4054-aefc-52da227508d1 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9c6c4366-bd2d-4054-aefc-52da227508d1');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-df2b2aaf-4620-4420-a98d-21173aad6602\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-df2b2aaf-4620-4420-a98d-21173aad6602')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-df2b2aaf-4620-4420-a98d-21173aad6602 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## In Depth Analysis"
      ],
      "metadata": {
        "id": "OWDei-8Qqsa7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyMYLNV7eANy",
        "outputId": "d8bbe9d7-2b8e-4955-952b-50a0a6ec3563"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of              Date Category  Likes  Shares  Reposts  Comments\n",
              "0      2015-01-01  Fashion   7495     379      186      9386\n",
              "1      2015-01-01   Health   1434     741       53      9397\n",
              "2      2015-01-01   Family   6412     686       55      9525\n",
              "3      2015-01-01  Culture   3894     914      127      9728\n",
              "4      2015-01-01   Travel   9825     614       29      4575\n",
              "...           ...      ...    ...     ...      ...       ...\n",
              "89995  2023-12-30     Food   4435     553      165      8142\n",
              "89996  2023-12-30  Fitness   9157     846       84      7711\n",
              "89997  2023-12-30    Music   8197     714       97      5392\n",
              "89998  2023-12-30  Fashion   6662     119      165      3477\n",
              "89999  2023-12-31   Travel   8747     154       74      9426\n",
              "\n",
              "[90000 rows x 6 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpX5oSCaeT0J",
        "outputId": "a5385966-9103-446e-beb4-5af1f1f0539d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.tail of              Date Category  Likes  Shares  Reposts  Comments\n",
              "0      2015-01-01  Fashion   7495     379      186      9386\n",
              "1      2015-01-01   Health   1434     741       53      9397\n",
              "2      2015-01-01   Family   6412     686       55      9525\n",
              "3      2015-01-01  Culture   3894     914      127      9728\n",
              "4      2015-01-01   Travel   9825     614       29      4575\n",
              "...           ...      ...    ...     ...      ...       ...\n",
              "89995  2023-12-30     Food   4435     553      165      8142\n",
              "89996  2023-12-30  Fitness   9157     846       84      7711\n",
              "89997  2023-12-30    Music   8197     714       97      5392\n",
              "89998  2023-12-30  Fashion   6662     119      165      3477\n",
              "89999  2023-12-31   Travel   8747     154       74      9426\n",
              "\n",
              "[90000 rows x 6 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5iQmVoEiJRE",
        "outputId": "98c8ec6b-be27-4d88-c090-dde74527e30e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date        object\n",
              "Category    object\n",
              "Likes        int64\n",
              "Shares       int64\n",
              "Reposts      int64\n",
              "Comments     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "e7rjw-v_jGla",
        "outputId": "3aea9307-1a6f-423b-9c03-6077f20dca5b"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Likes    Shares   Reposts  Comments\n",
              "Likes     1.000000 -0.001874 -0.005995 -0.000187\n",
              "Shares   -0.001874  1.000000  0.000171 -0.006051\n",
              "Reposts  -0.005995  0.000171  1.000000 -0.003912\n",
              "Comments -0.000187 -0.006051 -0.003912  1.000000"
            ],
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              "\n",
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              "      <th>Shares</th>\n",
              "      <td>-0.001874</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>-0.006051</td>\n",
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              "    </button>\n",
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              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b73e53d8-a140-418c-93d7-49d0d4f53736');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6db25b23-0029-4dd7-8e7b-ff4e49fadfed\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6db25b23-0029-4dd7-8e7b-ff4e49fadfed')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6db25b23-0029-4dd7-8e7b-ff4e49fadfed button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_features = df.select_dtypes(include=[np.int64])\n",
        "\n",
        "numeric_features.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AinacuxLeU7a",
        "outputId": "2e1c276a-9d21-4ba4-c361-4d48e4d17345"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Likes', 'Shares', 'Reposts', 'Comments'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_features = df.select_dtypes(include=[object])\n",
        "\n",
        "categorical_features.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EmVF9qHted-o",
        "outputId": "30d3cb18-6cf2-4763-ed93-39621322b708"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'Category'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.skew"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C8fs61ECekf3",
        "outputId": "ee18dc9e-23ea-42a8-cd50-e8d4021ab3e8"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame._add_numeric_operations.<locals>.skew of              Date Category  Likes  Shares  Reposts  Comments\n",
              "0      2015-01-01  Fashion   7495     379      186      9386\n",
              "1      2015-01-01   Health   1434     741       53      9397\n",
              "2      2015-01-01   Family   6412     686       55      9525\n",
              "3      2015-01-01  Culture   3894     914      127      9728\n",
              "4      2015-01-01   Travel   9825     614       29      4575\n",
              "...           ...      ...    ...     ...      ...       ...\n",
              "89995  2023-12-30     Food   4435     553      165      8142\n",
              "89996  2023-12-30  Fitness   9157     846       84      7711\n",
              "89997  2023-12-30    Music   8197     714       97      5392\n",
              "89998  2023-12-30  Fashion   6662     119      165      3477\n",
              "89999  2023-12-31   Travel   8747     154       74      9426\n",
              "\n",
              "[90000 rows x 6 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=['object'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "w_zrBOg4lOBN",
        "outputId": "131acb2c-ce24-49b7-ce46-d63b75fa170b"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Date Category\n",
              "count        90000    90000\n",
              "unique        3287        8\n",
              "top     2015-01-01     Food\n",
              "freq            28    11360"
            ],
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              "\n",
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              "        document.querySelector('#df-cec2b27b-0fec-4093-a6e5-dc1d2da582dd button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cec2b27b-0fec-4093-a6e5-dc1d2da582dd');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe(include=['int64'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "B1qVlKQNlN91",
        "outputId": "8c3f77f2-9215-4463-c098-b1811a0fa8a4"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Likes        Shares       Reposts      Comments\n",
              "count  90000.000000  90000.000000  90000.000000  90000.000000\n",
              "mean    4988.509044    500.666144     99.790622   5008.241367\n",
              "std     2886.904448    288.712012     57.751705   2887.542860\n",
              "min        0.000000      0.000000      0.000000      0.000000\n",
              "25%     2467.000000    250.000000     50.000000   2509.000000\n",
              "50%     4996.000000    502.000000    100.000000   5018.500000\n",
              "75%     7485.000000    750.000000    150.000000   7509.000000\n",
              "max     9999.000000    999.000000    199.000000   9999.000000"
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              "      <th>count</th>\n",
              "      <td>90000.000000</td>\n",
              "      <td>90000.000000</td>\n",
              "      <td>90000.000000</td>\n",
              "      <td>90000.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>4988.509044</td>\n",
              "      <td>500.666144</td>\n",
              "      <td>99.790622</td>\n",
              "      <td>5008.241367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2886.904448</td>\n",
              "      <td>288.712012</td>\n",
              "      <td>57.751705</td>\n",
              "      <td>2887.542860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2467.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>2509.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>4996.000000</td>\n",
              "      <td>502.000000</td>\n",
              "      <td>100.000000</td>\n",
              "      <td>5018.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>7485.000000</td>\n",
              "      <td>750.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>7509.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9999.000000</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>199.000000</td>\n",
              "      <td>9999.000000</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "Rh-uUI1Zq0C9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Category'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tq_8GjE1lN7S",
        "outputId": "c22c41d6-8c19-4359-c735-5a1b2da60558"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Food       11360\n",
              "Culture    11329\n",
              "Music      11287\n",
              "Fitness    11258\n",
              "Health     11247\n",
              "Fashion    11237\n",
              "Family     11186\n",
              "Travel     11096\n",
              "Name: Category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_group = df[['Category','Likes','Reposts']]"
      ],
      "metadata": {
        "id": "nGV8eDHLlcGV"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_group = df_group.groupby(['Category'],as_index=False).mean()\n",
        "df_group"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "e855jD74lcC1",
        "outputId": "1d616e9f-d82a-4e2b-fcad-1cdbc0e1d8c0"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Category        Likes     Reposts\n",
              "0  Culture  4981.837055   99.366140\n",
              "1   Family  4992.789648   99.588235\n",
              "2  Fashion  5013.476907   99.657560\n",
              "3  Fitness  4981.561379   99.605525\n",
              "4     Food  4977.206954   98.776320\n",
              "5   Health  5018.536410  100.090424\n",
              "6    Music  4979.910960  101.226012\n",
              "7   Travel  4962.650865  100.025054"
            ],
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fashion</td>\n",
              "      <td>5013.476907</td>\n",
              "      <td>99.657560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>4981.561379</td>\n",
              "      <td>99.605525</td>\n",
              "    </tr>\n",
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              "      <th>4</th>\n",
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              "      <th>5</th>\n",
              "      <td>Health</td>\n",
              "      <td>5018.536410</td>\n",
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              "      <td>4979.910960</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
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              "      <td>4962.650865</td>\n",
              "      <td>100.025054</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
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              "        element.appendChild(docLink);\n",
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              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-a299c50e-9bf4-407c-8c88-ecd936c2031b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a299c50e-9bf4-407c-8c88-ecd936c2031b')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-a299c50e-9bf4-407c-8c88-ecd936c2031b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_34819d88-ce4a-4102-892c-428ddddc0535\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_group')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_34819d88-ce4a-4102-892c-428ddddc0535 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_group');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# grouping results\n",
        "df_gptest = df[['Category','Likes','Reposts']]\n",
        "grouped_test1 = df_gptest.groupby(['Category','Reposts'],as_index=False).mean()\n",
        "grouped_test1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "ngZhB-8Xlb__",
        "outputId": "eb911edd-f9f3-4ec3-e6c5-e64ea666b0a3"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Category  Reposts        Likes\n",
              "0     Culture        0  5023.735849\n",
              "1     Culture        1  5129.054545\n",
              "2     Culture        2  5005.032787\n",
              "3     Culture        3  5111.735849\n",
              "4     Culture        4  4769.700000\n",
              "...       ...      ...          ...\n",
              "1595   Travel      195  5015.836735\n",
              "1596   Travel      196  5386.581818\n",
              "1597   Travel      197  5111.531915\n",
              "1598   Travel      198  4435.719298\n",
              "1599   Travel      199  4402.603448\n",
              "\n",
              "[1600 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-23cc0ffc-dcf1-4058-9763-39c71d482d74\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Category</th>\n",
              "      <th>Reposts</th>\n",
              "      <th>Likes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Culture</td>\n",
              "      <td>0</td>\n",
              "      <td>5023.735849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Culture</td>\n",
              "      <td>1</td>\n",
              "      <td>5129.054545</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Culture</td>\n",
              "      <td>2</td>\n",
              "      <td>5005.032787</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Culture</td>\n",
              "      <td>3</td>\n",
              "      <td>5111.735849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Culture</td>\n",
              "      <td>4</td>\n",
              "      <td>4769.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>Travel</td>\n",
              "      <td>195</td>\n",
              "      <td>5015.836735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1596</th>\n",
              "      <td>Travel</td>\n",
              "      <td>196</td>\n",
              "      <td>5386.581818</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>Travel</td>\n",
              "      <td>197</td>\n",
              "      <td>5111.531915</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>Travel</td>\n",
              "      <td>198</td>\n",
              "      <td>4435.719298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1599</th>\n",
              "      <td>Travel</td>\n",
              "      <td>199</td>\n",
              "      <td>4402.603448</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1600 rows × 3 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23cc0ffc-dcf1-4058-9763-39c71d482d74')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-23cc0ffc-dcf1-4058-9763-39c71d482d74 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-23cc0ffc-dcf1-4058-9763-39c71d482d74');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ade03d79-a875-4410-9c74-ce88540441f1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_73e61fc3-6953-4bd5-a8b0-807007c0f109\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('grouped_test1')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_73e61fc3-6953-4bd5-a8b0-807007c0f109 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('grouped_test1');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pearson Correlation helps in defining Linear Correlation between two variables."
      ],
      "metadata": {
        "id": "NF14p3uqrrgh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy import stats\n",
        "#Let's calculate the Pearson Correlation Coefficient and P-value of different variables.\n",
        "\n",
        "cols = ['Likes', 'Shares', 'Reposts', 'Comments']\n",
        "\n",
        "for col in cols:\n",
        "    pearson_coef, p_value = stats.pearsonr(df[col], df['Likes'])\n",
        "    print(\"The PearsonR between {} and Likes is {} with a P-value of P = {}\".format(\n",
        "          col, round(pearson_coef,4), round(p_value,4) ))\n",
        "\n",
        "    if p_value < 0.001:\n",
        "        print('Correlation between {} and Likes is statistically significant..'.format(col))\n",
        "    elif p_value < 0.05:\n",
        "        print('Correlation between {} and Likes is statistically moderate..'.format(col))\n",
        "    elif p_value < 0.1:\n",
        "        print('Correlation between {} and Likes is statistically weak..'.format(col))\n",
        "    else:\n",
        "        print('Correlation between {} and Likes is statistically not significant..'.format(col))\n",
        "\n",
        "    if pearson_coef > 0 :\n",
        "        if pearson_coef > 0.85:\n",
        "            print('Coeff ~{} shows that the relationship is positive and very strong.\\n'.format(round(pearson_coef,4)))\n",
        "        elif pearson_coef > 0.75 :\n",
        "            print('Coeff ~{} shows that the relationship is positive and quite strong.\\n'.format(round(pearson_coef,4)))\n",
        "        elif pearson_coef > 0.60:\n",
        "            print('Coeff ~{} shows that the relationship is positive and moderately strong.\\n'.format(round(pearson_coef,4)))\n",
        "        elif pearson_coef > 0.50 :\n",
        "            print('Coeff ~{} shows that the relationship is positive and only moderate.\\n'.format(round(pearson_coef,4)))\n",
        "        else:\n",
        "            print('Coefficient ~{} shows that the relationship is positive and weak.\\n'.format(round(pearson_coef,4)))\n",
        "    else:\n",
        "        if abs(pearson_coef) > 0.85:\n",
        "            print('Coeff ~{} shows that the relationship is negative and very strong.\\n'.format(round(pearson_coef,4)))\n",
        "        elif abs(pearson_coef) > 0.75 :\n",
        "            print('Coeff ~{} shows that the relationship is negative and quite strong.\\n'.format(round(pearson_coef,4)))\n",
        "        elif abs(pearson_coef) > 0.60:\n",
        "            print('Coeff ~{} shows that the relationship is negative and moderately strong.\\n'.format(round(pearson_coef,4)))\n",
        "        elif abs(pearson_coef) > 0.50 :\n",
        "            print('Coeff ~{} shows that the relationship is negative and only moderate.\\n'.format(round(pearson_coef,4)))\n",
        "        else:\n",
        "            print('Coefficient ~{} shows that the relationship is negative and weak.\\n'.format(round(pearson_coef,4)))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVkpyi11mgqK",
        "outputId": "fb0ca24b-4b7a-4d33-ceb8-ea2ad118b18d"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The PearsonR between Likes and Likes is 1.0 with a P-value of P = 0.0\n",
            "Correlation between Likes and Likes is statistically significant..\n",
            "Coeff ~1.0 shows that the relationship is positive and very strong.\n",
            "\n",
            "The PearsonR between Shares and Likes is -0.0019 with a P-value of P = 0.574\n",
            "Correlation between Shares and Likes is statistically not significant..\n",
            "Coefficient ~-0.0019 shows that the relationship is negative and weak.\n",
            "\n",
            "The PearsonR between Reposts and Likes is -0.006 with a P-value of P = 0.0721\n",
            "Correlation between Reposts and Likes is statistically weak..\n",
            "Coefficient ~-0.006 shows that the relationship is negative and weak.\n",
            "\n",
            "The PearsonR between Comments and Likes is -0.0002 with a P-value of P = 0.9552\n",
            "Correlation between Comments and Likes is statistically not significant..\n",
            "Coefficient ~-0.0002 shows that the relationship is negative and weak.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Graphical Analysis"
      ],
      "metadata": {
        "id": "l-BQ-0r_q8Jx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df.skew(),color='blue',axlabel ='Skewness')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ByS52IltfMdH",
        "outputId": "ecfda5e4-125a-49ce-f42d-a5b0bfd2fe7f"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Skewness', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A distplot infers the distribution of a variable."
      ],
      "metadata": {
        "id": "QxbWFwIurjHz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize = (5,5))\n",
        "sns.distplot(df.kurt(),color='r',axlabel ='Kurtosis',norm_hist= False, kde = True,rug = False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 465
        },
        "id": "F7KJ2SFpfbFR",
        "outputId": "069eef11-75f4-4ae8-99f7-7e86312872ac"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### A heatmap shows the variation of a continuous value across two dimensions."
      ],
      "metadata": {
        "id": "dOynuteDrcjt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "k= 11\n",
        "correlation = numeric_features.corr()\n",
        "cols = correlation.nlargest(k,'Likes')['Likes'].index\n",
        "print(cols)\n",
        "cm = np.corrcoef(df[cols].values.T)\n",
        "f , ax = plt.subplots(figsize = (14,12))\n",
        "sns.heatmap(cm, vmax=.8, linewidths=0.01,square=True,annot=True,cmap='viridis',\n",
        "            linecolor=\"white\",xticklabels = cols.values ,annot_kws = {'size':12},yticklabels = cols.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "fXqpZ1IKgESz",
        "outputId": "7a63c98b-6ed7-4996-a637-83d56405f515"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['Likes', 'Comments', 'Shares', 'Reposts'], dtype='object')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x1200 with 2 Axes>"
            ],
            "image/png": 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1QkJCrpuRGh8fryFDhmjkyJH64YcfNGDAAA0YMKBQMdUboWbCX9iFCxeUmJhosa9mzZrXbf/EE08oNzdXoaGhWr9+ve644w5FR0crNDRUDRo00P333y87Ozvt3r1be/fu1YsvvqhFixYpNzdXnTp1UtWqVfXhhx/K2dnZPBcbuFXMnTtXQUFB6tixo6ZPn65WrVopJydHGzdu1DvvvKOkpCQFBARo6NChmj17tnJycjRmzBh169bNPH/zZipu7JWEj4+PDAaD1q5dqz59+sjZ2Vn79u1TbGys7rrrLtWpU0fbtm3T6dOnmQuOcnHmzBmFhYXpwQcfVKtWreTm5qYdO3Zo5syZ6t+/f4nO0bhxYy1ZskRfffWVGjZsqA8++EDff/+9GjZseMPjhg4dqldffVX9+/fX9OnTVb9+fR07dkyfffaZJk6cqOzsbL377rvq16+fbrvtNiUnJ+vgwYMaPnx4Wbx14KYKCwvThAkTNH/+fI0fP17jxo2TyWTSHXfcoQsXLmjLli1yd3c31+mRpOnTp6tmzZry9PTUc889p1q1amnAgAGSpKefflodOnTQCy+8oMGDByshIUFvvfWW3n77bUn5NRuOHDmirl27qnr16vryyy9lMpnMK6n4+vpq27ZtSklJMU8xmjp1qgIDA9WiRQtlZmZq7dq1fDcB13j99dc1atQoc42tefPmad26dVqwYIGeffbZQu3nzJmj3r17a8KECZLyi4Fv3LhRb731lubNm1eiaxJM+AuLi4szF7spMHLkyBse89RTT8lkMqlPnz7asGGDQkJCtHbtWk2fPl0zZsyQg4ODmjVrpoceekhSfhX8V155RZGRkcrNzVVAQIDWrFlzw6AFUB78/Py0a9cuvfTSS3r66aeVlpam2rVrKzAwUO+8844MBoM+//xzPfHEE+ratavs7OzUu3dv/etf/yqX/hY39kqiXr165kKOI0aM0PDhw/XMM8/o22+/1ezZs3Xx4kX5+Pho1qxZuvvuu234boCiubq6qlOnTnrjjTd0+PBhZWdny9vbW6NGjdKkSZNKdI7Ro0frhx9+0ODBg2UwGDRkyBCNGTNG69evv+FxVatW1bfffqtnnnlG9913n3777TfVq1dPPXv2lLu7u65evaqffvpJixcv1pkzZ1S3bl099thjGj16dFm8deCmqlKlih5//HHNnDlTR48eVe3atRUTE6MjR47Iw8ND7dq1KzTmXnnlFY0dO1YHDx5UmzZttGbNGnPGW7t27bR8+XJFR0frhRdeUN26dTV9+nTzEsQeHh767LPPNHXqVGVkZKhx48b65JNP1KJFC0nS+PHjFR4eLn9/f129elVHjx6Vo6OjoqKilJKSImdnZ915551aunTpTb1PwK0qKytLO3fuVFRUlHmfnZ2dgoODC01TL5CQkKDIyEiLfSEhIVq9enWJr2vIY70vAAAAACUQFxenHj166Ny5c+aVgIBbWS/7wcU3ugWtvbKk0LLIRqNRRqOxUNuTJ0+qXr16io+PtyiOOnHiRG3atKnQkuRS/nSixYsXm1dDkqS3335b06ZNU3p6eon6SM0EAAAAAABuITExMapWrZrFFhMTU97dssA0BwAAAAAAbiFRUVGFpiEUlZUgSbVq1ZK9vX2hjIL09HR5eXkVeYyXl5dV7YtCZgIAAACAEunevbvy8vKY4gDYmNFolLu7u8V2vWCCo6OjAgMDFRsba95nMpkUGxtrMe3hWp07d7ZoL0kbN268bvuikJkAAAAAAKicSrHMYkUUGRmp8PBwtW/fXh07dtTs2bN1+fJl8+oOw4cPV7169cxTJcaOHatu3bpp1qxZuueee7R06VLt2LHDqiVXCSYAAAAAAFCBDR48WKdPn1Z0dLROnTqlNm3aaMOGDfL09JQkpaamys7u/xMTunTpoo8//ljPP/+8Jk2apMaNG2v16tVq2bJlia/Jag4AAAAAgEqpl11YeXehVDaaPi3vLhSLYAIAAAAAALAKBRgBAAAAAIBVCCYAAAAAAACrEEwAAAAAAABWIZgAAAAAAACsQjABAAAAAABYhWACAAAAAACwCsEEAAAAAABgFYIJAAAAAADAKgQTAAAAAACAVQgmAAAAAAAAqxBMAAAAAAAAVvkfx0WApbVoH6sAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "2vgBus8DlB88"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}