diff --git "a/_1787(monthly_screentime_analysis).ipynb" "b/_1787(monthly_screentime_analysis).ipynb"
new file mode 100644--- /dev/null
+++ "b/_1787(monthly_screentime_analysis).ipynb"
@@ -0,0 +1,1366 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "OQ08JOS-TyGb"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "import os\n",
+        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
+        "  for filename in filenames:\n",
+        "    print(os.path.join(dirname, filename))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = pd.read_csv(\"/content/screentime_analysis.csv\")"
+      ],
+      "metadata": {
+        "id": "miJTbGSlUF_w"
+      },
+      "execution_count": 2,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = data.sample(frac=1).reset_index(drop=True)\n",
+        "data = data.sample(frac=1).reset_index(drop=True)\n",
+        "data.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "WEoNYpd0UMPT",
+        "outputId": "cd4224ed-b02d-4367-a86d-6b3114ceb790"
+      },
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         Date        App  Usage (minutes)  Notifications  Times Opened\n",
+              "0  2024-08-21  Instagram              109             62            38\n",
+              "1  2024-08-25   Facebook               30             45            32\n",
+              "2  2024-08-17     Safari               19              1             7\n",
+              "3  2024-08-16   LinkedIn               25             12             5\n",
+              "4  2024-08-30          X               19             37            23"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-442da123-b222-4160-83c8-29267b06fb67\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Date</th>\n",
+              "      <th>App</th>\n",
+              "      <th>Usage (minutes)</th>\n",
+              "      <th>Notifications</th>\n",
+              "      <th>Times Opened</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>2024-08-21</td>\n",
+              "      <td>Instagram</td>\n",
+              "      <td>109</td>\n",
+              "      <td>62</td>\n",
+              "      <td>38</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>2024-08-25</td>\n",
+              "      <td>Facebook</td>\n",
+              "      <td>30</td>\n",
+              "      <td>45</td>\n",
+              "      <td>32</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>2024-08-17</td>\n",
+              "      <td>Safari</td>\n",
+              "      <td>19</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>2024-08-16</td>\n",
+              "      <td>LinkedIn</td>\n",
+              "      <td>25</td>\n",
+              "      <td>12</td>\n",
+              "      <td>5</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2024-08-30</td>\n",
+              "      <td>X</td>\n",
+              "      <td>19</td>\n",
+              "      <td>37</td>\n",
+              "      <td>23</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-442da123-b222-4160-83c8-29267b06fb67')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\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",
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+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .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-442da123-b222-4160-83c8-29267b06fb67 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-442da123-b222-4160-83c8-29267b06fb67');\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-d6c2a616-0c54-4485-a4f6-d17f44b9e97e\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d6c2a616-0c54-4485-a4f6-d17f44b9e97e')\"\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",
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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",
+              "    }\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",
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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",
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+              "      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",
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+              "        document.querySelector('#df-d6c2a616-0c54-4485-a4f6-d17f44b9e97e button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "data",
+              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"2024-08-02\",\n          \"2024-08-23\",\n          \"2024-08-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"App\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Facebook\",\n          \"8 Ball Pool\",\n          \"Instagram\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage (minutes)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1,\n        \"max\": 119,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          50,\n          109,\n          90\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Notifications\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35,\n        \"min\": 0,\n        \"max\": 147,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          37,\n          120,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Times Opened\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          18,\n          25,\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 3
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.rename(columns={'Usage (minutes)': 'Usage_minutes', 'Times Opened': 'Times_Opened'}, inplace=True)\n",
+        "\n",
+        "data.describe()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "2oXnjpQ8UWGu",
+        "outputId": "632138e2-72a7-4a90-cae7-af54e3f24f1d"
+      },
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "       Usage_minutes  Notifications  Times_Opened\n",
+              "count     200.000000     200.000000     200.00000\n",
+              "mean       37.750000      28.735000      21.63000\n",
+              "std        29.711299      35.801726      24.27301\n",
+              "min         1.000000       0.000000       1.00000\n",
+              "25%        17.000000       1.750000       4.00000\n",
+              "50%        26.000000      11.500000      10.00000\n",
+              "75%        51.250000      47.000000      32.00000\n",
+              "max       119.000000     147.000000      99.00000"
+            ],
+            "text/html": [
+              "\n",
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+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Usage_minutes</th>\n",
+              "      <th>Notifications</th>\n",
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+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>200.000000</td>\n",
+              "      <td>200.000000</td>\n",
+              "      <td>200.00000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>37.750000</td>\n",
+              "      <td>28.735000</td>\n",
+              "      <td>21.63000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>29.711299</td>\n",
+              "      <td>35.801726</td>\n",
+              "      <td>24.27301</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>1.000000</td>\n",
+              "      <td>0.000000</td>\n",
+              "      <td>1.00000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>17.000000</td>\n",
+              "      <td>1.750000</td>\n",
+              "      <td>4.00000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>26.000000</td>\n",
+              "      <td>11.500000</td>\n",
+              "      <td>10.00000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>51.250000</td>\n",
+              "      <td>47.000000</td>\n",
+              "      <td>32.00000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>119.000000</td>\n",
+              "      <td>147.000000</td>\n",
+              "      <td>99.00000</td>\n",
+              "    </tr>\n",
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+              "  </svg>\n",
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+              "  <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-95aaa111-649e-47b8-87b3-81df1992546e 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-95aaa111-649e-47b8-87b3-81df1992546e');\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-e707f319-82f9-410b-b15b-e7548cbd9b0c\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e707f319-82f9-410b-b15b-e7548cbd9b0c')\"\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-e707f319-82f9-410b-b15b-e7548cbd9b0c button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Usage_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 66.60723289926901,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          37.75,\n          26.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Notifications\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 73.909149781942,\n        \"min\": 0.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.735,\n          11.5,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Times_Opened\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68.45284067821282,\n        \"min\": 1.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          21.63,\n          10.0,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data.isnull().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 241
+        },
+        "id": "LDYbJR5-Uqp-",
+        "outputId": "d8ca4387-a6ee-46ad-c987-519328102d14"
+      },
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Date             0\n",
+              "App              0\n",
+              "Usage_minutes    0\n",
+              "Notifications    0\n",
+              "Times_Opened     0\n",
+              "dtype: int64"
+            ],
+            "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>0</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>Date</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>App</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Usage_minutes</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Notifications</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Times_Opened</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def detect_outliers(df):\n",
+        "  for column in df.columns:\n",
+        "    if df[column].dtype in ['int64', 'int32', 'float32', 'float64']:\n",
+        "      df[f'z_score_{column}'] = (df[column] - df[column].mean()) / df[column].std()\n",
+        "      outliers = df[np.abs(data[f'z_score_{column}']) > 3]\n",
+        "      df.drop(f'z_score_{column}', axis=1, inplace=True)\n",
+        "      return outliers\n",
+        "\n",
+        "outliers = detect_outliers(data)\n",
+        "print(outliers)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UoK_OqMpUysG",
+        "outputId": "6511728c-b9b0-4e80-9b85-8beb00e99e29"
+      },
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Empty DataFrame\n",
+            "Columns: [Date, App, Usage_minutes, Notifications, Times_Opened, z_score_Usage_minutes]\n",
+            "Index: []\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data['Date'] = pd.to_datetime(data['Date'])\n",
+        "\n",
+        "data['day'] = data['Date'].dt.day\n",
+        "\n",
+        "data.drop(['Date'], inplace=True, axis=1)\n",
+        "\n",
+        "data.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "EX7SWyTZVd7n",
+        "outputId": "b83bb886-a3cb-4623-9476-598d0168cada"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "         App  Usage_minutes  Notifications  Times_Opened  day\n",
+              "0  Instagram            109             62            38   21\n",
+              "1   Facebook             30             45            32   25\n",
+              "2     Safari             19              1             7   17\n",
+              "3   LinkedIn             25             12             5   16\n",
+              "4          X             19             37            23   30"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b2740865-5537-4e6a-ac1e-01ec30f95a99\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>App</th>\n",
+              "      <th>Usage_minutes</th>\n",
+              "      <th>Notifications</th>\n",
+              "      <th>Times_Opened</th>\n",
+              "      <th>day</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Instagram</td>\n",
+              "      <td>109</td>\n",
+              "      <td>62</td>\n",
+              "      <td>38</td>\n",
+              "      <td>21</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Facebook</td>\n",
+              "      <td>30</td>\n",
+              "      <td>45</td>\n",
+              "      <td>32</td>\n",
+              "      <td>25</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Safari</td>\n",
+              "      <td>19</td>\n",
+              "      <td>1</td>\n",
+              "      <td>7</td>\n",
+              "      <td>17</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>LinkedIn</td>\n",
+              "      <td>25</td>\n",
+              "      <td>12</td>\n",
+              "      <td>5</td>\n",
+              "      <td>16</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>X</td>\n",
+              "      <td>19</td>\n",
+              "      <td>37</td>\n",
+              "      <td>23</td>\n",
+              "      <td>30</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-b2740865-5537-4e6a-ac1e-01ec30f95a99')\"\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",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </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-b2740865-5537-4e6a-ac1e-01ec30f95a99 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-b2740865-5537-4e6a-ac1e-01ec30f95a99');\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-022bdd9d-55b8-4578-8495-b075fc42be27\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-022bdd9d-55b8-4578-8495-b075fc42be27')\"\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-022bdd9d-55b8-4578-8495-b075fc42be27 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "data",
+              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"App\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"Facebook\",\n          \"8 Ball Pool\",\n          \"Instagram\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage_minutes\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1,\n        \"max\": 119,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          50,\n          109,\n          90\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Notifications\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 35,\n        \"min\": 0,\n        \"max\": 147,\n        \"num_unique_values\": 76,\n        \"samples\": [\n          37,\n          120,\n          22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Times_Opened\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          18,\n          25,\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"day\",\n      \"properties\": {\n        \"dtype\": \"int32\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          2,\n          23,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "apps = data['App'].unique()\n",
+        "apps"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5-6wRtGqVpwv",
+        "outputId": "2d7f4e76-3388-4b56-e7c2-1a1e76278bf9"
+      },
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array(['Instagram', 'Facebook', 'Safari', 'LinkedIn', 'X', '8 Ball Pool',\n",
+              "       'Netflix', 'WhatsApp'], dtype=object)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "entry_counts = data.groupby('App').count()\n",
+        "print(entry_counts)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "c0N4Po1UVuS0",
+        "outputId": "7e6b3615-ec37-4db6-fd0d-f7578400ae29"
+      },
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "             Usage_minutes  Notifications  Times_Opened  day\n",
+            "App                                                         \n",
+            "8 Ball Pool             25             25            25   25\n",
+            "Facebook                25             25            25   25\n",
+            "Instagram               25             25            25   25\n",
+            "LinkedIn                25             25            25   25\n",
+            "Netflix                 25             25            25   25\n",
+            "Safari                  25             25            25   25\n",
+            "WhatsApp                25             25            25   25\n",
+            "X                       25             25            25   25\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "usage = data.groupby('App')['Usage_minutes'].sum()\n",
+        "\n",
+        "plt.figure(figsize=(8, 6), label='Total usage by app')\n",
+        "plt.title('Total monthly usage by app')\n",
+        "plt.xlabel('App')\n",
+        "plt.ylabel('Usage')\n",
+        "plt.bar(apps, usage)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 523
+        },
+        "id": "psztcGEkV5ol",
+        "outputId": "7bc6c964-f2ff-4ccb-8dc6-108637937316"
+      },
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<BarContainer object of 8 artists>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 11
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "notifications_by_app = data.groupby('App')['Notifications'].sum()\n",
+        "\n",
+        "plt.figure(figsize=(8, 6), label='Notifications by app')\n",
+        "plt.title('Notifications by app')\n",
+        "plt.xlabel('App')\n",
+        "plt.ylabel('Notifications')\n",
+        "plt.bar(apps, notifications_by_app)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 523
+        },
+        "id": "uw3pUqj5WLDt",
+        "outputId": "c0d3a1bd-d178-4dc5-eadd-92bf9f0689c2"
+      },
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<BarContainer object of 8 artists>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 12
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "times_opened_by_app = data.groupby('App')['Times_Opened'].sum()\n",
+        "plt.figure(figsize=(8, 6), label='Times opened by app')\n",
+        "plt.title('Times opened by app')\n",
+        "plt.xlabel('App')\n",
+        "plt.ylabel('Times Opened')\n",
+        "plt.bar(apps, times_opened_by_app)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 523
+        },
+        "id": "74QKYQPXWdST",
+        "outputId": "ab947126-cdd0-452f-d685-a168da5a7ce9"
+      },
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<BarContainer object of 8 artists>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 13
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "usage_by_date = data.groupby('day')['Usage_minutes'].sum()\n",
+        "plt.figure(figsize=(8, 6), label='Screentime by date')\n",
+        "plt.title('Screentime by date')\n",
+        "plt.xlabel('Day')\n",
+        "plt.ylabel('Screentime')\n",
+        "plt.plot([i for i in range(max(data['day']))], usage_by_date, color='red')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 529
+        },
+        "id": "16UqtBoKWzLp",
+        "outputId": "35faa1f9-1b13-46e2-9128-2b824521ee62"
+      },
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7af2ede85690>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 14
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 800x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file