diff --git a/EDA_Notebook.ipynb b/EDA_Notebook.ipynb
new file mode 100644
index 0000000..62e6e74
--- /dev/null
+++ b/EDA_Notebook.ipynb
@@ -0,0 +1,3632 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "include_colab_link": true
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "view-in-github",
+        "colab_type": "text"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/manas5110/batch2/blob/main/EDA_Notebook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1AnZy4hTKVz0"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Data Collection\n",
+        "sns.get_dataset_names()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "963pZr3KLQWF",
+        "outputId": "86bc2189-f06f-4165-e130-2c65f8956640"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "['anagrams',\n",
+              " 'anscombe',\n",
+              " 'attention',\n",
+              " 'brain_networks',\n",
+              " 'car_crashes',\n",
+              " 'diamonds',\n",
+              " 'dots',\n",
+              " 'dowjones',\n",
+              " 'exercise',\n",
+              " 'flights',\n",
+              " 'fmri',\n",
+              " 'geyser',\n",
+              " 'glue',\n",
+              " 'healthexp',\n",
+              " 'iris',\n",
+              " 'mpg',\n",
+              " 'penguins',\n",
+              " 'planets',\n",
+              " 'seaice',\n",
+              " 'taxis',\n",
+              " 'tips',\n",
+              " 'titanic']"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 2
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = sns.load_dataset('titanic')"
+      ],
+      "metadata": {
+        "id": "tAZMr7d8L7cn"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 444
+        },
+        "id": "69LVxeYbMBSk",
+        "outputId": "352442be-d916-4ecc-e7d2-1ccea8a71b04"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     survived  pclass     sex   age  sibsp  parch     fare embarked   class  \\\n",
+              "0           0       3    male  22.0      1      0   7.2500        S   Third   \n",
+              "1           1       1  female  38.0      1      0  71.2833        C   First   \n",
+              "2           1       3  female  26.0      0      0   7.9250        S   Third   \n",
+              "3           1       1  female  35.0      1      0  53.1000        S   First   \n",
+              "4           0       3    male  35.0      0      0   8.0500        S   Third   \n",
+              "..        ...     ...     ...   ...    ...    ...      ...      ...     ...   \n",
+              "886         0       2    male  27.0      0      0  13.0000        S  Second   \n",
+              "887         1       1  female  19.0      0      0  30.0000        S   First   \n",
+              "888         0       3  female   NaN      1      2  23.4500        S   Third   \n",
+              "889         1       1    male  26.0      0      0  30.0000        C   First   \n",
+              "890         0       3    male  32.0      0      0   7.7500        Q   Third   \n",
+              "\n",
+              "       who  adult_male deck  embark_town alive  alone  \n",
+              "0      man        True  NaN  Southampton    no  False  \n",
+              "1    woman       False    C    Cherbourg   yes  False  \n",
+              "2    woman       False  NaN  Southampton   yes   True  \n",
+              "3    woman       False    C  Southampton   yes  False  \n",
+              "4      man        True  NaN  Southampton    no   True  \n",
+              "..     ...         ...  ...          ...   ...    ...  \n",
+              "886    man        True  NaN  Southampton    no   True  \n",
+              "887  woman       False    B  Southampton   yes   True  \n",
+              "888  woman       False  NaN  Southampton    no  False  \n",
+              "889    man        True    C    Cherbourg   yes   True  \n",
+              "890    man        True  NaN   Queenstown    no   True  \n",
+              "\n",
+              "[891 rows x 15 columns]"
+            ],
+            "text/html": [
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+              "      <th></th>\n",
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+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>female</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7.9250</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Third</td>\n",
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+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>True</td>\n",
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+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>female</td>\n",
+              "      <td>35.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>53.1000</td>\n",
+              "      <td>S</td>\n",
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+              "      <td>Southampton</td>\n",
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+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>0</td>\n",
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+              "      <td>8.0500</td>\n",
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+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
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+              "      <td>no</td>\n",
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+              "    <tr>\n",
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+              "      <td>13.0000</td>\n",
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+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
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+              "<p>891 rows × 15 columns</p>\n",
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+              "        google.colab.notebook.generateWithVariable('df');\n",
+              "      }\n",
+              "      })();\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 4
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "LoJ6a4poMrVB",
+        "outputId": "7c75c5b3-d4fb-40fc-e47c-475b68875d5d"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
+              "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
+              "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
+              "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
+              "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
+              "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
+              "\n",
+              "     who  adult_male deck  embark_town alive  alone  \n",
+              "0    man        True  NaN  Southampton    no  False  \n",
+              "1  woman       False    C    Cherbourg   yes  False  \n",
+              "2  woman       False  NaN  Southampton   yes   True  \n",
+              "3  woman       False    C  Southampton   yes  False  \n",
+              "4    man        True  NaN  Southampton    no   True  "
+            ],
+            "text/html": [
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+              "      <td>26.0</td>\n",
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+              "      <td>female</td>\n",
+              "      <td>35.0</td>\n",
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+              "      <td>False</td>\n",
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+              "    <tr>\n",
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+              "      <td>0</td>\n",
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+              "      <td>0</td>\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
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+              "\n",
+              "    .colab-df-convert {\n",
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+              "      width: 32px;\n",
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+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
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+              "      fill: #174EA6;\n",
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+              "\n",
+              "    .colab-df-buttons div {\n",
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+              "\n",
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+              "      fill: #D2E3FC;\n",
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+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
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+              "      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-43d46505-6015-4336-8d10-e67c4f43c334 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-43d46505-6015-4336-8d10-e67c4f43c334');\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",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14.526497332334044,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 88,\n        \"samples\": [\n          0.75,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.693428597180905,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"man\",\n          \"woman\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"C\",\n          \"E\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 5
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.tail()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "iTtZcV3UM1Up",
+        "outputId": "bc5b0e00-c7d8-46b0-ff99-b910b9ed5f66"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     survived  pclass     sex   age  sibsp  parch   fare embarked   class  \\\n",
+              "886         0       2    male  27.0      0      0  13.00        S  Second   \n",
+              "887         1       1  female  19.0      0      0  30.00        S   First   \n",
+              "888         0       3  female   NaN      1      2  23.45        S   Third   \n",
+              "889         1       1    male  26.0      0      0  30.00        C   First   \n",
+              "890         0       3    male  32.0      0      0   7.75        Q   Third   \n",
+              "\n",
+              "       who  adult_male deck  embark_town alive  alone  \n",
+              "886    man        True  NaN  Southampton    no   True  \n",
+              "887  woman       False    B  Southampton   yes   True  \n",
+              "888  woman       False  NaN  Southampton    no  False  \n",
+              "889    man        True    C    Cherbourg   yes   True  \n",
+              "890    man        True  NaN   Queenstown    no   True  "
+            ],
+            "text/html": [
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+              "      <th></th>\n",
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+              "      <th>class</th>\n",
+              "      <th>who</th>\n",
+              "      <th>adult_male</th>\n",
+              "      <th>deck</th>\n",
+              "      <th>embark_town</th>\n",
+              "      <th>alive</th>\n",
+              "      <th>alone</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>886</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>male</td>\n",
+              "      <td>27.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13.00</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Second</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>887</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>female</td>\n",
+              "      <td>19.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>30.00</td>\n",
+              "      <td>S</td>\n",
+              "      <td>First</td>\n",
+              "      <td>woman</td>\n",
+              "      <td>False</td>\n",
+              "      <td>B</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>888</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>23.45</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>woman</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>no</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>889</th>\n",
+              "      <td>1</td>\n",
+              "      <td>1</td>\n",
+              "      <td>male</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>30.00</td>\n",
+              "      <td>C</td>\n",
+              "      <td>First</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>C</td>\n",
+              "      <td>Cherbourg</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>890</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>male</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7.75</td>\n",
+              "      <td>Q</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Queenstown</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</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-93d75e04-8bbd-4592-b0dc-aaedb5496e85')\"\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-93d75e04-8bbd-4592-b0dc-aaedb5496e85 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-93d75e04-8bbd-4592-b0dc-aaedb5496e85');\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>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.354126134736337,\n        \"min\": 19.0,\n        \"max\": 32.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          19.0,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.09253436952285,\n        \"min\": 7.75,\n        \"max\": 30.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          30.0,\n          7.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"S\",\n          \"C\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Second\",\n          \"First\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"woman\",\n          \"man\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"C\",\n          \"B\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Southampton\",\n          \"Cherbourg\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\",\n          \"no\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          false,\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 6
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.sample(5)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 226
+        },
+        "id": "LScTn9fTM8RH",
+        "outputId": "d5d76e73-a694-4e91-f0f1-37e318613a45"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     survived  pclass   sex   age  sibsp  parch     fare embarked   class  \\\n",
+              "704         0       3  male  26.0      1      0   7.8542        S   Third   \n",
+              "718         0       3  male   NaN      0      0  15.5000        Q   Third   \n",
+              "429         1       3  male  32.0      0      0   8.0500        S   Third   \n",
+              "344         0       2  male  36.0      0      0  13.0000        S  Second   \n",
+              "625         0       1  male  61.0      0      0  32.3208        S   First   \n",
+              "\n",
+              "     who  adult_male deck  embark_town alive  alone  \n",
+              "704  man        True  NaN  Southampton    no  False  \n",
+              "718  man        True  NaN   Queenstown    no   True  \n",
+              "429  man        True    E  Southampton   yes   True  \n",
+              "344  man        True  NaN  Southampton    no   True  \n",
+              "625  man        True    D  Southampton    no   True  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-41ad6df4-b691-4eb3-81e4-711262c2c7d8\" 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",
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+              "    .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>survived</th>\n",
+              "      <th>pclass</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>embarked</th>\n",
+              "      <th>class</th>\n",
+              "      <th>who</th>\n",
+              "      <th>adult_male</th>\n",
+              "      <th>deck</th>\n",
+              "      <th>embark_town</th>\n",
+              "      <th>alive</th>\n",
+              "      <th>alone</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>704</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>male</td>\n",
+              "      <td>26.0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7.8542</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>no</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>718</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
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+              "      <td>15.5000</td>\n",
+              "      <td>Q</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Queenstown</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>429</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>male</td>\n",
+              "      <td>32.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8.0500</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>E</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>344</th>\n",
+              "      <td>0</td>\n",
+              "      <td>2</td>\n",
+              "      <td>male</td>\n",
+              "      <td>36.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13.0000</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Second</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>625</th>\n",
+              "      <td>0</td>\n",
+              "      <td>1</td>\n",
+              "      <td>male</td>\n",
+              "      <td>61.0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>32.3208</td>\n",
+              "      <td>S</td>\n",
+              "      <td>First</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>D</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</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-41ad6df4-b691-4eb3-81e4-711262c2c7d8')\"\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-41ad6df4-b691-4eb3-81e4-711262c2c7d8 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-41ad6df4-b691-4eb3-81e4-711262c2c7d8');\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>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15.392097539538485,\n        \"min\": 26.0,\n        \"max\": 61.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.03780286317678,\n        \"min\": 7.8542,\n        \"max\": 32.3208,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          15.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Q\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Third\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"man\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"D\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Queenstown\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"yes\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 7
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Fo8d7nCKNHln",
+        "outputId": "87931ddb-3f76-4a0d-c645-ad6b633d9ef3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
+              "       'embarked', 'class', 'who', 'adult_male', 'deck', 'embark_town',\n",
+              "       'alive', 'alone'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 8
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isna().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 554
+        },
+        "id": "VYIJU4WWNS8b",
+        "outputId": "c24fc71b-fc15-46f9-9195-2540f233cf94"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "survived         0\n",
+              "pclass           0\n",
+              "sex              0\n",
+              "age            177\n",
+              "sibsp            0\n",
+              "parch            0\n",
+              "fare             0\n",
+              "embarked         2\n",
+              "class            0\n",
+              "who              0\n",
+              "adult_male       0\n",
+              "deck           688\n",
+              "embark_town      2\n",
+              "alive            0\n",
+              "alone            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>survived</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>pclass</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sex</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>177</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sibsp</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>parch</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fare</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embarked</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>class</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>who</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>adult_male</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>deck</th>\n",
+              "      <td>688</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embark_town</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alive</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alone</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 9
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cHJJ0_tVOH5f",
+        "outputId": "86337356-fb9b-4075-f85e-2142a0b932a0"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 891 entries, 0 to 890\n",
+            "Data columns (total 15 columns):\n",
+            " #   Column       Non-Null Count  Dtype   \n",
+            "---  ------       --------------  -----   \n",
+            " 0   survived     891 non-null    int64   \n",
+            " 1   pclass       891 non-null    int64   \n",
+            " 2   sex          891 non-null    object  \n",
+            " 3   age          714 non-null    float64 \n",
+            " 4   sibsp        891 non-null    int64   \n",
+            " 5   parch        891 non-null    int64   \n",
+            " 6   fare         891 non-null    float64 \n",
+            " 7   embarked     889 non-null    object  \n",
+            " 8   class        891 non-null    category\n",
+            " 9   who          891 non-null    object  \n",
+            " 10  adult_male   891 non-null    bool    \n",
+            " 11  deck         203 non-null    category\n",
+            " 12  embark_town  889 non-null    object  \n",
+            " 13  alive        891 non-null    object  \n",
+            " 14  alone        891 non-null    bool    \n",
+            "dtypes: bool(2), category(2), float64(2), int64(4), object(5)\n",
+            "memory usage: 80.7+ KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[df['age'].isna()]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 444
+        },
+        "id": "ZV4aNXYHNjQv",
+        "outputId": "d8271c76-7e97-4e6c-a657-1e84c1d9b399"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     survived  pclass     sex  age  sibsp  parch     fare embarked   class  \\\n",
+              "5           0       3    male  NaN      0      0   8.4583        Q   Third   \n",
+              "17          1       2    male  NaN      0      0  13.0000        S  Second   \n",
+              "19          1       3  female  NaN      0      0   7.2250        C   Third   \n",
+              "26          0       3    male  NaN      0      0   7.2250        C   Third   \n",
+              "28          1       3  female  NaN      0      0   7.8792        Q   Third   \n",
+              "..        ...     ...     ...  ...    ...    ...      ...      ...     ...   \n",
+              "859         0       3    male  NaN      0      0   7.2292        C   Third   \n",
+              "863         0       3  female  NaN      8      2  69.5500        S   Third   \n",
+              "868         0       3    male  NaN      0      0   9.5000        S   Third   \n",
+              "878         0       3    male  NaN      0      0   7.8958        S   Third   \n",
+              "888         0       3  female  NaN      1      2  23.4500        S   Third   \n",
+              "\n",
+              "       who  adult_male deck  embark_town alive  alone  \n",
+              "5      man        True  NaN   Queenstown    no   True  \n",
+              "17     man        True  NaN  Southampton   yes   True  \n",
+              "19   woman       False  NaN    Cherbourg   yes   True  \n",
+              "26     man        True  NaN    Cherbourg    no   True  \n",
+              "28   woman       False  NaN   Queenstown   yes   True  \n",
+              "..     ...         ...  ...          ...   ...    ...  \n",
+              "859    man        True  NaN    Cherbourg    no   True  \n",
+              "863  woman       False  NaN  Southampton    no  False  \n",
+              "868    man        True  NaN  Southampton    no   True  \n",
+              "878    man        True  NaN  Southampton    no   True  \n",
+              "888  woman       False  NaN  Southampton    no  False  \n",
+              "\n",
+              "[177 rows x 15 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-28d52221-6446-4a08-9d12-c5825e236018\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
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+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>survived</th>\n",
+              "      <th>pclass</th>\n",
+              "      <th>sex</th>\n",
+              "      <th>age</th>\n",
+              "      <th>sibsp</th>\n",
+              "      <th>parch</th>\n",
+              "      <th>fare</th>\n",
+              "      <th>embarked</th>\n",
+              "      <th>class</th>\n",
+              "      <th>who</th>\n",
+              "      <th>adult_male</th>\n",
+              "      <th>deck</th>\n",
+              "      <th>embark_town</th>\n",
+              "      <th>alive</th>\n",
+              "      <th>alone</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>8.4583</td>\n",
+              "      <td>Q</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Queenstown</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>17</th>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>13.0000</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Second</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>19</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>C</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>woman</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Cherbourg</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>26</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7.2250</td>\n",
+              "      <td>C</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Cherbourg</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>28</th>\n",
+              "      <td>1</td>\n",
+              "      <td>3</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7.8792</td>\n",
+              "      <td>Q</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>woman</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Queenstown</td>\n",
+              "      <td>yes</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
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+              "      <td>...</td>\n",
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+              "      <td>...</td>\n",
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+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>859</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7.2292</td>\n",
+              "      <td>C</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Cherbourg</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>863</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>8</td>\n",
+              "      <td>2</td>\n",
+              "      <td>69.5500</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>woman</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>no</td>\n",
+              "      <td>False</td>\n",
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+              "    <tr>\n",
+              "      <th>868</th>\n",
+              "      <td>0</td>\n",
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+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
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+              "      <td>0</td>\n",
+              "      <td>9.5000</td>\n",
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+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>878</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>male</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>0</td>\n",
+              "      <td>0</td>\n",
+              "      <td>7.8958</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>man</td>\n",
+              "      <td>True</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>no</td>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>888</th>\n",
+              "      <td>0</td>\n",
+              "      <td>3</td>\n",
+              "      <td>female</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>1</td>\n",
+              "      <td>2</td>\n",
+              "      <td>23.4500</td>\n",
+              "      <td>S</td>\n",
+              "      <td>Third</td>\n",
+              "      <td>woman</td>\n",
+              "      <td>False</td>\n",
+              "      <td>NaN</td>\n",
+              "      <td>Southampton</td>\n",
+              "      <td>no</td>\n",
+              "      <td>False</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>177 rows × 15 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-28d52221-6446-4a08-9d12-c5825e236018')\"\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-28d52221-6446-4a08-9d12-c5825e236018 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-28d52221-6446-4a08-9d12-c5825e236018');\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>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df[df['age']\",\n  \"rows\": 177,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.874608202349055,\n        \"min\": 0.0,\n        \"max\": 227.525,\n        \"num_unique_values\": 73,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 11
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['age'].isna().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Qrm9-uCTOZrz",
+        "outputId": "e501c038-f1ae-4610-ee25-58e85eee5af3"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(177)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.boxplot(df['age'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 429
+        },
+        "id": "4G1kyFZhPvHU",
+        "outputId": "e1397159-54f9-47b5-c3af-2f0755f381ec"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: ylabel='age'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['age'].fillna(df['age'].median(), inplace=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "W2nHOnOIPmCB",
+        "outputId": "439a7329-a47f-4e9c-ee50-ba2ffcb577e9"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/tmp/ipykernel_7780/1654433547.py:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.\n",
+            "The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.\n",
+            "\n",
+            "For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.\n",
+            "\n",
+            "\n",
+            "  df['age'].fillna(df['age'].median(), inplace=True)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.iloc[17,:]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 554
+        },
+        "id": "F9pvQl27Rzfe",
+        "outputId": "832fe263-a98d-402c-9422-fb7a581a400f"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "survived                 1\n",
+              "pclass                   2\n",
+              "sex                   male\n",
+              "age                   28.0\n",
+              "sibsp                    0\n",
+              "parch                    0\n",
+              "fare                  13.0\n",
+              "embarked                 S\n",
+              "class               Second\n",
+              "who                    man\n",
+              "adult_male            True\n",
+              "deck                   NaN\n",
+              "embark_town    Southampton\n",
+              "alive                  yes\n",
+              "alone                 True\n",
+              "Name: 17, dtype: object"
+            ],
+            "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>17</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>survived</th>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>pclass</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sex</th>\n",
+              "      <td>male</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>28.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sibsp</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>parch</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fare</th>\n",
+              "      <td>13.0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embarked</th>\n",
+              "      <td>S</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>class</th>\n",
+              "      <td>Second</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>who</th>\n",
+              "      <td>man</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>adult_male</th>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>deck</th>\n",
+              "      <td>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embark_town</th>\n",
+              "      <td>Southampton</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alive</th>\n",
+              "      <td>yes</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alone</th>\n",
+              "      <td>True</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> object</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['age'].median()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lmIWMoL1RsWV",
+        "outputId": "db336e61-ccb0-41d3-a606-74ba58c80e32"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "28.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[df['age'].isna()]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 444
+        },
+        "id": "Tyq1Ql05QEIq",
+        "outputId": "8e20a3be-bae0-479b-d0f7-5225dd6bde47"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "     survived  pclass     sex  age  sibsp  parch     fare embarked   class  \\\n",
+              "5           0       3    male  NaN      0      0   8.4583        Q   Third   \n",
+              "17          1       2    male  NaN      0      0  13.0000        S  Second   \n",
+              "19          1       3  female  NaN      0      0   7.2250        C   Third   \n",
+              "26          0       3    male  NaN      0      0   7.2250        C   Third   \n",
+              "28          1       3  female  NaN      0      0   7.8792        Q   Third   \n",
+              "..        ...     ...     ...  ...    ...    ...      ...      ...     ...   \n",
+              "859         0       3    male  NaN      0      0   7.2292        C   Third   \n",
+              "863         0       3  female  NaN      8      2  69.5500        S   Third   \n",
+              "868         0       3    male  NaN      0      0   9.5000        S   Third   \n",
+              "878         0       3    male  NaN      0      0   7.8958        S   Third   \n",
+              "888         0       3  female  NaN      1      2  23.4500        S   Third   \n",
+              "\n",
+              "       who  adult_male deck  embark_town alive  alone  \n",
+              "5      man        True  NaN   Queenstown    no   True  \n",
+              "17     man        True  NaN  Southampton   yes   True  \n",
+              "19   woman       False  NaN    Cherbourg   yes   True  \n",
+              "26     man        True  NaN    Cherbourg    no   True  \n",
+              "28   woman       False  NaN   Queenstown   yes   True  \n",
+              "..     ...         ...  ...          ...   ...    ...  \n",
+              "859    man        True  NaN    Cherbourg    no   True  \n",
+              "863  woman       False  NaN  Southampton    no  False  \n",
+              "868    man        True  NaN  Southampton    no   True  \n",
+              "878    man        True  NaN  Southampton    no   True  \n",
+              "888  woman       False  NaN  Southampton    no  False  \n",
+              "\n",
+              "[177 rows x 15 columns]"
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+              "      <th>19</th>\n",
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+              "<p>177 rows × 15 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
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+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a00afda7-7ede-490d-9d54-dabe247199d2')\"\n",
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+              "  </svg>\n",
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+              "  <style>\n",
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+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
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+              "      background-color: #E8F0FE;\n",
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+              "      fill: #174EA6;\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",
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+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a00afda7-7ede-490d-9d54-dabe247199d2 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-a00afda7-7ede-490d-9d54-dabe247199d2');\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",
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+              "\n",
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+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df[df['age']\",\n  \"rows\": 177,\n  \"fields\": [\n    {\n      \"column\": \"survived\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pclass\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 3,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"female\",\n          \"male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sibsp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 5,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parch\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31.874608202349055,\n        \"min\": 0.0,\n        \"max\": 227.525,\n        \"num_unique_values\": 73,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embarked\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"who\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"adult_male\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"deck\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 6,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"embark_town\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alive\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"alone\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isna().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 554
+        },
+        "id": "0JHbYsO-Sa9R",
+        "outputId": "7d1c6b9b-23a5-452f-d1c8-93a1b40766e8"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "survived         0\n",
+              "pclass           0\n",
+              "sex              0\n",
+              "age              0\n",
+              "sibsp            0\n",
+              "parch            0\n",
+              "fare             0\n",
+              "embarked         2\n",
+              "class            0\n",
+              "who              0\n",
+              "adult_male       0\n",
+              "deck           688\n",
+              "embark_town      2\n",
+              "alive            0\n",
+              "alone            0\n",
+              "dtype: int64"
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+              "      <th>embarked</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>class</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>who</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>adult_male</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>deck</th>\n",
+              "      <td>688</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embark_town</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alive</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alone</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 27
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "mode = df['embarked'].mode()[0]"
+      ],
+      "metadata": {
+        "id": "_l2lf72yS3Yi"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['embarked'].fillna(mode, inplace=True)"
+      ],
+      "metadata": {
+        "id": "IDdDSDPSTmSN"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isna().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 554
+        },
+        "id": "ZcUqKP_zTra3",
+        "outputId": "159d18d5-f96c-4767-bef1-c14a33350368"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "survived         0\n",
+              "pclass           0\n",
+              "sex              0\n",
+              "age              0\n",
+              "sibsp            0\n",
+              "parch            0\n",
+              "fare             0\n",
+              "embarked         0\n",
+              "class            0\n",
+              "who              0\n",
+              "adult_male       0\n",
+              "deck           688\n",
+              "embark_town      2\n",
+              "alive            0\n",
+              "alone            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>survived</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>pclass</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sex</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sibsp</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>parch</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fare</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embarked</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>class</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>who</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>adult_male</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>deck</th>\n",
+              "      <td>688</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embark_town</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alive</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alone</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.shape"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vBimRzoUT2bQ",
+        "outputId": "da2394d3-08a2-4fac-9104-b9667c931319"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(891, 15)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 37
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Pandas\n",
+        "# Series, Dataframe\n",
+        "li = [1,2,3,4]\n",
+        "pd.Series(li)[0]"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "1OarbhijTF9F",
+        "outputId": "45ceeb6a-a292-4166-9278-f9dbd2dd5d3a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "np.int64(1)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df['embarked'].value_counts()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "wjqpRERMS_pf",
+        "outputId": "8b7bfb68-c79b-4e93-e371-28e9455410fb"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "embarked\n",
+              "S    644\n",
+              "C    168\n",
+              "Q     77\n",
+              "Name: count, 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>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embarked</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>S</th>\n",
+              "      <td>644</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>C</th>\n",
+              "      <td>168</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>Q</th>\n",
+              "      <td>77</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 30
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.drop('deck', axis=1, inplace=True)"
+      ],
+      "metadata": {
+        "id": "BbjqhBNrT7_e"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.isna().sum()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 523
+        },
+        "id": "jOYPo1YhUJho",
+        "outputId": "76057395-2366-4857-bd32-8355856ce04a"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "survived       0\n",
+              "pclass         0\n",
+              "sex            0\n",
+              "age            0\n",
+              "sibsp          0\n",
+              "parch          0\n",
+              "fare           0\n",
+              "embarked       0\n",
+              "class          0\n",
+              "who            0\n",
+              "adult_male     0\n",
+              "embark_town    2\n",
+              "alive          0\n",
+              "alone          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>survived</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>pclass</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sex</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>age</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>sibsp</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>parch</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>fare</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embarked</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>class</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>who</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>adult_male</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>embark_town</th>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alive</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>alone</th>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 40
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Univariate -> single feature (num, cat)\n",
+        "# histogram, countplot"
+      ],
+      "metadata": {
+        "id": "FAPII8zpVp6H"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "sns.histplot(df['age'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 467
+        },
+        "id": "6aNFN4zRV1mp",
+        "outputId": "fc8e9b52-207f-45e4-b302-c981fa511844"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='age', ylabel='Count'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 41
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.figure(figsize=(4,3))\n",
+        "sns.countplot(df['embarked'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 329
+        },
+        "id": "s14VeHeaWvi6",
+        "outputId": "72beb796-81b8-4b8f-8b66-7a38761690ce"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: xlabel='count', ylabel='embarked'>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 43
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 400x300 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Pie chart\n",
+        "labels = ['male', 'female']\n",
+        "plt.pie(df['sex'].value_counts(), labels=labels, autopct='%1.1f%%')"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 512
+        },
+        "id": "xzeL_gaGXEY3",
+        "outputId": "d2563bd8-2a6d-48b3-f293-b15d919bc7db"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "([<matplotlib.patches.Wedge at 0x7f89b8e70320>,\n",
+              "  <matplotlib.patches.Wedge at 0x7f89b8e2b9b0>],\n",
+              " [Text(-0.4919454315896098, 0.9838646717603559, 'male'),\n",
+              "  Text(0.49194551760179267, -0.9838646287531149, 'female')],\n",
+              " [Text(-0.26833387177615076, 0.5366534573238304, '64.8%'),\n",
+              "  Text(0.2683339186918869, -0.5366534338653353, '35.2%')])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Bivariate analysis\n",
+        "# num vs num, cat vs num, cat vs cat\n",
+        "# scatter plot, line plot"
+      ],
+      "metadata": {
+        "id": "J4AkNFLnX0Tq"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "data = pd.DataFrame({\n",
+        "    'Months':['jan', 'Feb', 'March', 'April', 'may'],\n",
+        "    'Sales':[200, 300, 350, 400, 370]\n",
+        "})\n",
+        "data"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "uUqUnMTlYPSs",
+        "outputId": "edd6fbc7-d3c2-4496-c8f9-c632474b9a76"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "  Months  Sales\n",
+              "0    jan    200\n",
+              "1    Feb    300\n",
+              "2  March    350\n",
+              "3  April    400\n",
+              "4    may    370"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-4f16331a-165b-424b-9f6e-44bfba4d0a11\" 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>Months</th>\n",
+              "      <th>Sales</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>jan</td>\n",
+              "      <td>200</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Feb</td>\n",
+              "      <td>300</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>March</td>\n",
+              "      <td>350</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>April</td>\n",
+              "      <td>400</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>may</td>\n",
+              "      <td>370</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-4f16331a-165b-424b-9f6e-44bfba4d0a11')\"\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-4f16331a-165b-424b-9f6e-44bfba4d0a11 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-4f16331a-165b-424b-9f6e-44bfba4d0a11');\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=\"id_7aa840bd-7918-42da-9db2-7b00399b2145\">\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('data')\"\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_7aa840bd-7918-42da-9db2-7b00399b2145 button.colab-df-generate');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      buttonEl.onclick = () => {\n",
+              "        google.colab.notebook.generateWithVariable('data');\n",
+              "      }\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\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Months\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Feb\",\n          \"may\",\n          \"March\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78,\n        \"min\": 200,\n        \"max\": 400,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          300,\n          370,\n          350\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 50
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(data['Months'], data['Sales'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        },
+        "id": "l8YsSQwmYkpc",
+        "outputId": "bee72163-2ece-42f2-fe0f-0bd68cd89833"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[<matplotlib.lines.Line2D at 0x7f89bb3237a0>]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 51
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Scatter Plot\n",
+        "df.columns"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "J9fuIPENY36d",
+        "outputId": "06b13fa1-20f9-4072-aa3b-fc571148d8ba"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Index(['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare',\n",
+              "       'embarked', 'class', 'who', 'adult_male', 'embark_town', 'alive',\n",
+              "       'alone'],\n",
+              "      dtype='object')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 52
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.scatter(df['fare'], df['age'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 448
+        },
+        "id": "cyeZ1okOZVQT",
+        "outputId": "48d7658a-65c0-4301-c82f-f61af8e77c47"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<matplotlib.collections.PathCollection at 0x7f89b8d38200>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 53
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Multivariate analysis\n",
+        "num_col = df.select_dtypes(include = ['int', 'float']).columns\n",
+        "sns.heatmap(df[num_col].corr(), annot=True)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 453
+        },
+        "id": "bPbEf25SZs-D",
+        "outputId": "93a69da6-5379-444b-de71-4f9bf9218f74"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 60
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fdtOs0YcZ2UC",
+        "outputId": "d5d8a19a-d9d7-4b61-8523-3010acd09141"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 891 entries, 0 to 890\n",
+            "Data columns (total 14 columns):\n",
+            " #   Column       Non-Null Count  Dtype   \n",
+            "---  ------       --------------  -----   \n",
+            " 0   survived     891 non-null    int64   \n",
+            " 1   pclass       891 non-null    int64   \n",
+            " 2   sex          891 non-null    object  \n",
+            " 3   age          891 non-null    float64 \n",
+            " 4   sibsp        891 non-null    int64   \n",
+            " 5   parch        891 non-null    int64   \n",
+            " 6   fare         891 non-null    float64 \n",
+            " 7   embarked     891 non-null    object  \n",
+            " 8   class        891 non-null    category\n",
+            " 9   who          891 non-null    object  \n",
+            " 10  adult_male   891 non-null    bool    \n",
+            " 11  embark_town  889 non-null    object  \n",
+            " 12  alive        891 non-null    object  \n",
+            " 13  alone        891 non-null    bool    \n",
+            "dtypes: bool(2), category(1), float64(2), int64(4), object(5)\n",
+            "memory usage: 79.4+ KB\n"
+          ]
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/manas.py b/manas.py
new file mode 100644
index 0000000..f7ffd91
--- /dev/null
+++ b/manas.py
@@ -0,0 +1 @@
+# Kuch likh diya hai
\ No newline at end of file