EDA_PROJECT_BY_Mauzum shamil.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PROJECT DONE BY MAUZUM SHAMIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "YQmw-0DmVViH"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "9-XtWdz0bbMB"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('/content/titanic_dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 492
    },
    "id": "vDk6812HbjNX",
    "outputId": "b16931dd-ea0c-47e0-d896-4995555b2068"
   },
   "outputs": [
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       "        fill: #1967D2;\n",
       "        height: 32px;\n",
       "        padding: 0 0 0 0;\n",
       "        width: 32px;\n",
       "      }\n",
       "\n",
       "      .colab-df-generate:hover {\n",
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       "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
       "            title=\"Generate code using this dataframe.\"\n",
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       "\n",
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       "\n",
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       "        google.colab.notebook.generateWithVariable('df');\n",
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       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name     Sex   Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                               Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                             Allen, Mr. William Henry    male  35.0      0   \n",
       "..                                                 ...     ...   ...    ...   \n",
       "886                              Montvila, Rev. Juozas    male  27.0      0   \n",
       "887                       Graham, Miss. Margaret Edith  female  19.0      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"  female   NaN      1   \n",
       "889                              Behr, Mr. Karl Howell    male  26.0      0   \n",
       "890                                Dooley, Mr. Patrick    male  32.0      0   \n",
       "\n",
       "     Parch            Ticket     Fare Cabin Embarked  \n",
       "0        0         A/5 21171   7.2500   NaN        S  \n",
       "1        0          PC 17599  71.2833   C85        C  \n",
       "2        0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3        0            113803  53.1000  C123        S  \n",
       "4        0            373450   8.0500   NaN        S  \n",
       "..     ...               ...      ...   ...      ...  \n",
       "886      0            211536  13.0000   NaN        S  \n",
       "887      0            112053  30.0000   B42        S  \n",
       "888      2        W./C. 6607  23.4500   NaN        S  \n",
       "889      0            111369  30.0000  C148        C  \n",
       "890      0            370376   7.7500   NaN        Q  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 258
    },
    "id": "xez69czcb2p7",
    "outputId": "b6cb65e7-ef1d-45c4-cd29-6396c6f85910"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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       "type": "dataframe",
       "variable_name": "df"
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       "\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
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       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
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       "\n",
       "  .colab-df-quickchart:hover {\n",
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       "  .colab-df-quickchart-complete:disabled,\n",
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       "\n",
       "  .colab-df-spinner {\n",
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       "    border-color: transparent;\n",
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       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
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       "\n",
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       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
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       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
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       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
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       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-e0307342-ea7b-4882-b82d-8221fb4a762e button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 223
    },
    "id": "eoQVK6MncGe5",
    "outputId": "db956512-a18c-4613-bf45-fd2c9f912d28"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 887,\n        \"max\": 891,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          888,\n          891,\n          889\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\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\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Graham, Miss. Margaret Edith\",\n          \"Dooley, Mr. Patrick\"\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\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"112053\",\n          \"370376\"\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\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"C148\",\n          \"B42\"\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}",
       "type": "dataframe"
      },
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       "\n",
       "  <div id=\"df-82d291b4-32fe-4414-b057-272e2a77439d\" class=\"colab-df-container\">\n",
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       "        vertical-align: middle;\n",
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      <th>Name</th>\n",
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       "      <td>Montvila, Rev. Juozas</td>\n",
       "      <td>male</td>\n",
       "      <td>27.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <td>23.45</td>\n",
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       "    </tr>\n",
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       "      <th>889</th>\n",
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       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>111369</td>\n",
       "      <td>30.00</td>\n",
       "      <td>C148</td>\n",
       "      <td>C</td>\n",
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       "      <td>891</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>32.0</td>\n",
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       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
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       "        const element = document.querySelector('#df-82d291b4-32fe-4414-b057-272e2a77439d');\n",
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       "          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",
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       "\n",
       "\n",
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       "            title=\"Suggest charts\"\n",
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       "\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
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       "      --hover-bg-color: #434B5C;\n",
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       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
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       "    cursor: pointer;\n",
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       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
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       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
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       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
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       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
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       "      border-left-color: var(--fill-color);\n",
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       "      border-right-color: var(--fill-color);\n",
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       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-29addc22-3beb-4514-8848-a7c61ef34f45 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "N51ZlVRMeVuJ",
    "outputId": "01a2eeab-f88d-4b6a-de0f-ea110742a65e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
       "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 448
    },
    "id": "_6zO_AGbewpb",
    "outputId": "3f57aace-68c3-4087-bc2e-a6a6c07c367d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7d0643bcc340>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot graph\n",
    "plt.plot(df['Age'],df['Fare'],'r*',linestyle='dashed',linewidth=2, markersize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 490
    },
    "id": "02M7wu_bfZwF",
    "outputId": "d6735aca-71d5-4f51-8a04-66316e5584de"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'age')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar graph\n",
    "plt.bar(df['Pclass'],df['Age'],color='m')\n",
    "plt.title('bar graph')\n",
    "plt.xlabel('pclass')\n",
    "plt.ylabel('age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 324
    },
    "id": "ymMYcG7Sg4FU",
    "outputId": "d7da96c7-4b37-4d35-b2f6-94d0f48ae091"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-2f6f6606aa2c>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  df.corr()\n"
     ]
    },
    {
     "data": {
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       "type": "dataframe"
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       "    async function quickchart(key) {\n",
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       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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       "            'suggestCharts', [key], {});\n",
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       "\n",
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      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 579
    },
    "id": "vh_odYPRhSI5",
    "outputId": "63baf348-975e-4aeb-c799-6f3ed77402c4"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-22-d56b9e5b7e7f>:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
      "  sns.heatmap(df.corr(),cmap='coolwarm')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(df.corr(),cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 471
    },
    "id": "QnL4MLLziBFq",
    "outputId": "5ab46008-a599-4c5c-87dc-6bd57b6de37d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Pclass', ylabel='Fare'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot\n",
    "sns.violinplot(x='Pclass',y='Fare',data=df,hue='Sex',split=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 467
    },
    "id": "mdRIZBCzjYGH",
    "outputId": "15f9d04d-2970-4d5e-e4c6-4b9b8b9ecc4b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Sex', ylabel='Fare'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.stripplot(x=\"Sex\",y=\"Fare\",hue=\"Pclass\",palette=\"Set1\",data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uo1Uwo4Fj6VV"
   },
   "source": [
    "# Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 503
    },
    "id": "Q8uRahCAkCGC",
    "outputId": "0ced1fb6-61c5-433e-a2c2-1839d084597d"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\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\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\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\": 14.526497332334042,\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\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\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\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"B49\"\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}",
       "type": "dataframe",
       "variable_name": "df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-3f59cf7f-7da2-41c6-934a-e3a3d93dec52\" 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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</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-3f59cf7f-7da2-41c6-934a-e3a3d93dec52')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http:https://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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       "    </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-3f59cf7f-7da2-41c6-934a-e3a3d93dec52 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-3f59cf7f-7da2-41c6-934a-e3a3d93dec52');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
       "    </script>\n",
       "  </div>\n",
       "\n",
       "\n",
       "<div id=\"df-ffd88d7a-5309-4cd2-bc8b-96045650a0b2\">\n",
       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ffd88d7a-5309-4cd2-bc8b-96045650a0b2')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "<svg xmlns=\"http:https://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "     width=\"24px\">\n",
       "    <g>\n",
       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-ffd88d7a-5309-4cd2-bc8b-96045650a0b2 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "nuHmrruAknnk",
    "outputId": "b1affc1a-1045-42d4-859d-516cdc3f5c6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "asG4sS8tks6w",
    "outputId": "d2a58293-87b4-45d9-842a-e7de84e63503"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oDfuKvNmkzlo",
    "outputId": "9c7af8a3-c7e8-4c08-f55f-566138d97acb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-Z4TjEgMlBen"
   },
   "outputs": [],
   "source": [
    "# age = float\n",
    "# cabin and Embarked are object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 470
    },
    "id": "rZvLB_uElagy",
    "outputId": "d7665f29-825c-41cc-adad-46e4783876aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'histogram of age')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Age'])\n",
    "plt.title(\"histogram of age\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "id": "erpvQyGdlp_T"
   },
   "outputs": [],
   "source": [
    "# according to the graph its show it doesnt have any skewness therefore its mean()\n",
    "\n",
    "df['Age']=df['Age'].fillna(df['Age'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HUuo400rmJ2T",
    "outputId": "54d77c13-fe6b-4735-9f7b-94b72f738519"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Age'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "zgTlqX3UmPDt",
    "outputId": "29326db5-e02f-4ee9-f502-e655432090ae"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      S\n",
       "1      C\n",
       "2      S\n",
       "3      S\n",
       "4      S\n",
       "      ..\n",
       "886    S\n",
       "887    S\n",
       "888    S\n",
       "889    C\n",
       "890    Q\n",
       "Name: Embarked, Length: 891, dtype: object"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as for the objects its mode()[0]\n",
    "\n",
    "for i in ['Cabin','Embarked']:\n",
    "  df[i]=df[i].fillna(df[i].mode()[0])\n",
    "df[i]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "u9ue0_FWm0lL",
    "outputId": "197a4e5d-e03f-410f-f922-0f60231a7ea8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Cabin          0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "H5TNbCGNm4DV"
   },
   "outputs": [],
   "source": [
    "# we cleared all the null spots in the data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5BQPWiUEm-kX"
   },
   "source": [
    "# outlier detection and removal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 503
    },
    "id": "SAnTyG5fnEcu",
    "outputId": "0bd9e52e-c922-41ef-a1da-9232cac826cd"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\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\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\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\": 13.002015226002882,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5\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\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\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\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"A19\"\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}",
       "type": "dataframe",
       "variable_name": "df"
      },
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       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>38.0</td>\n",
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       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
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       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71405267-dbb4-4acc-bc5b-a7a106a9a959')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
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       "\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",
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       "\n",
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       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-71405267-dbb4-4acc-bc5b-a7a106a9a959 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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       "        const element = document.querySelector('#df-71405267-dbb4-4acc-bc5b-a7a106a9a959');\n",
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       "                                                    [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",
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       "        await google.colab.output.renderOutput(dataTable, element);\n",
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       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "\n",
       "\n",
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       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
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       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
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       "\n",
       "  .colab-df-quickchart {\n",
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       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
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       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
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       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-93aa1066-2cfd-4612-8ae4-47b949e84bfc button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare    Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500  B96 B98        S  \n",
       "1      0          PC 17599  71.2833      C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250  B96 B98        S  \n",
       "3      0            113803  53.1000     C123        S  \n",
       "4      0            373450   8.0500  B96 B98        S  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6EO40-ewnUVU",
    "outputId": "3f19b881-bd9e-4df6-bd93-11359abe7c19"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      int64\n",
       "Survived         int64\n",
       "Pclass           int64\n",
       "Name            object\n",
       "Sex             object\n",
       "Age            float64\n",
       "SibSp            int64\n",
       "Parch            int64\n",
       "Ticket          object\n",
       "Fare           float64\n",
       "Cabin           object\n",
       "Embarked        object\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "_dG-sVObnX9R"
   },
   "outputs": [],
   "source": [
    "flt = df.select_dtypes(include=['float','int'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
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    },
    "id": "qF75jF7rnl7-",
    "outputId": "eb1fff4f-2132-448e-9d32-52d3e3b4868f"
   },
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       "\n",
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      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
       "0            1         0       3  22.0      1      0   7.2500\n",
       "1            2         1       1  38.0      1      0  71.2833\n",
       "2            3         1       3  26.0      0      0   7.9250\n",
       "3            4         1       1  35.0      1      0  53.1000\n",
       "4            5         0       3  35.0      0      0   8.0500"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 420
    },
    "id": "I-kCiNJ1nuYn",
    "outputId": "3388a12b-75ed-4111-9dce-8c6d3c8fc743"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'boxplot')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flt.boxplot(color='m',figsize=(20,10))\n",
    "plt.title('boxplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Xw3uO8LKoeRm",
    "outputId": "b33b9d06-f5f9-445f-a1fa-b0d1d5e8cbc7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.9104\n",
      "14.4542\n",
      "31.0\n",
      "[71.2833, 263.0, 146.5208, 82.1708, 76.7292, 80.0, 83.475, 73.5, 263.0, 77.2875, 247.5208, 73.5, 77.2875, 79.2, 66.6, 69.55, 69.55, 146.5208, 69.55, 113.275, 76.2917, 90.0, 83.475, 90.0, 79.2, 86.5, 512.3292, 79.65, 153.4625, 135.6333, 77.9583, 78.85, 91.0792, 151.55, 247.5208, 151.55, 110.8833, 108.9, 83.1583, 262.375, 164.8667, 134.5, 69.55, 135.6333, 153.4625, 133.65, 66.6, 134.5, 263.0, 75.25, 69.3, 135.6333, 82.1708, 211.5, 227.525, 73.5, 120.0, 113.275, 90.0, 120.0, 263.0, 81.8583, 89.1042, 91.0792, 90.0, 78.2667, 151.55, 86.5, 108.9, 93.5, 221.7792, 106.425, 71.0, 106.425, 110.8833, 227.525, 79.65, 110.8833, 79.65, 79.2, 78.2667, 153.4625, 77.9583, 69.3, 76.7292, 73.5, 113.275, 133.65, 73.5, 512.3292, 76.7292, 211.3375, 110.8833, 227.525, 151.55, 227.525, 211.3375, 512.3292, 78.85, 262.375, 71.0, 86.5, 120.0, 77.9583, 211.3375, 79.2, 69.55, 120.0, 93.5, 80.0, 83.1583, 69.55, 89.1042, 164.8667, 69.55, 83.1583]\n"
     ]
    }
   ],
   "source": [
    "Q1=np.percentile(df['Fare'],25,interpolation='midpoint')\n",
    "Q2=np.percentile(df['Fare'],50,interpolation='midpoint')\n",
    "Q3=np.percentile(df['Fare'],75,interpolation='midpoint')\n",
    "\n",
    "print(Q1)\n",
    "print(Q2)\n",
    "print(Q3)\n",
    "\n",
    "IQR=Q3-Q1\n",
    "IQR\n",
    "\n",
    "upper_limit=Q3+1.5*IQR\n",
    "lower_limit=Q1-1.5*IQR\n",
    "\n",
    "outlier=[]\n",
    "for x in df['Fare']:\n",
    "  if((x>upper_limit) or (x<lower_limit)):\n",
    "    outlier.append(x)\n",
    "\n",
    "print(outlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "NTo929dCo9iM"
   },
   "outputs": [],
   "source": [
    "df1=df['Fare']>upper_limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Qu254ItipIJg",
    "outputId": "7d9c2899-04c3-43e0-a134-754c42b1c016"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Series name: Fare\n",
      "Non-Null Count  Dtype\n",
      "--------------  -----\n",
      "891 non-null    bool \n",
      "dtypes: bool(1)\n",
      "memory usage: 1019.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ydr6P0XrpkGs",
    "outputId": "404d02be-0b29-4a53-cb08-7eccb06ffe35"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    False\n",
       "1     True\n",
       "2    False\n",
       "3    False\n",
       "4    False\n",
       "Name: Fare, dtype: bool"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "d_mhUivxpBFg",
    "outputId": "b1443c94-3f82-4d7a-b145-58fdb5d56e2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  1,  27,  31,  34,  52,  61,  62,  72,  88, 102,\n",
       "            ...\n",
       "            792, 802, 820, 829, 835, 846, 849, 856, 863, 879],\n",
       "           dtype='int64', length=116)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df1].index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "pNQPsM2FpgrT"
   },
   "source": [
    "due to high amount of row it cant be removed because it will decrease the density of the dataset so we are avoiding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J3FTD_Sep570"
   },
   "source": [
    " # Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "T-ToyCI4p90T"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "lG2VSf7LqNVr"
   },
   "outputs": [],
   "source": [
    "le = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "id": "vwH6uZ6pqRy2",
    "outputId": "c0a45917-83b7-4762-e453-c65f4e6c7fef"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
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       "type": "dataframe",
       "variable_name": "df"
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       "      border-right-color: var(--fill-color);\n",
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       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-6b52f062-6dcb-445b-bff7-75e67aa72ca0 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare    Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500  B96 B98        S  \n",
       "1      0          PC 17599  71.2833      C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250  B96 B98        S  \n",
       "3      0            113803  53.1000     C123        S  \n",
       "4      0            373450   8.0500  B96 B98        S  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "64qqJyi3qrKy",
    "outputId": "1d849d53-6740-4d28-ef31-1f765296a911"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "147"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cabin.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "s6sWIkAsqwlY",
    "outputId": "20b7a835-5e76-4f31-8ff0-1bb3f34177e6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Embarked.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "As2z3jtssiMb",
    "outputId": "8cf489ef-ca26-417b-902d-359fc08d852b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Pclass.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "a7tFEYy5q3WS",
    "outputId": "698b057b-c9aa-4886-9768-4ca2b31296b4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "891"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Name.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X01rOsuOq9Km",
    "outputId": "01426cfb-6f5e-4e19-ff84-07d1ea314d41"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "681"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Ticket.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "OJHVl0BTrNtd"
   },
   "outputs": [],
   "source": [
    "# according to columns we only encoding embarked and Sex and droping name ,fare and cabin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "id": "CjOPUCa_rj-X"
   },
   "outputs": [],
   "source": [
    "df['Sex']= le.fit_transform(df['Sex'])\n",
    "df['Embarked']= le.fit_transform(df['Embarked'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "id": "0nwY3O2WsV7s",
    "outputId": "96d5ad3a-697d-424a-9e3f-a5f544551bff"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\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\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.002015226002882,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5\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\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\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\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"A19\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df"
      },
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       "\n",
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
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       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-2428cab3-7044-4912-bd6c-b048066ca602 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare    Cabin  Embarked  \n",
       "0         A/5 21171   7.2500  B96 B98         2  \n",
       "1          PC 17599  71.2833      C85         0  \n",
       "2  STON/O2. 3101282   7.9250  B96 B98         2  \n",
       "3            113803  53.1000     C123         2  \n",
       "4            373450   8.0500  B96 B98         2  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "x9jjmKStst3i"
   },
   "source": [
    " # scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "id": "tsZB0eTpt50h"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "id": "7NSMrDr_t5wA"
   },
   "outputs": [],
   "source": [
    "minmax = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 292
    },
    "id": "ha1oT0LGsxbY",
    "outputId": "a1d0ae78-1eb7-4eab-d0f9-93e7b41019a3"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"df\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"PassengerId\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 257,\n        \"min\": 1,\n        \"max\": 891,\n        \"num_unique_values\": 891,\n        \"samples\": [\n          710,\n          440,\n          841\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\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\": \"Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 891,\n        \"samples\": [\n          \"Moubarek, Master. Halim Gonios (\\\"William George\\\")\",\n          \"Kvillner, Mr. Johan Henrik Johannesson\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.002015226002882,\n        \"min\": 0.42,\n        \"max\": 80.0,\n        \"num_unique_values\": 89,\n        \"samples\": [\n          59.0,\n          36.5\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\": \"Ticket\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 681,\n        \"samples\": [\n          \"11774\",\n          \"248740\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\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\": \"Cabin\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 147,\n        \"samples\": [\n          \"D45\",\n          \"A19\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Embarked\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe",
       "variable_name": "df"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-0b3da73e-86a2-4e49-aa1e-ee3348cdfe76\" 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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       "\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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>B96 B98</td>\n",
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       "      <td>2</td>\n",
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       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>B96 B98</td>\n",
       "      <td>2</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-0b3da73e-86a2-4e49-aa1e-ee3348cdfe76')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
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       "      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",
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       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-0b3da73e-86a2-4e49-aa1e-ee3348cdfe76 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-0b3da73e-86a2-4e49-aa1e-ee3348cdfe76');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
       "      }\n",
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       "\n",
       "\n",
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       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed3f8317-15a1-4939-b22e-3ac34f0f44fb')\"\n",
       "            title=\"Suggest charts\"\n",
       "            style=\"display:none;\">\n",
       "\n",
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       "    </g>\n",
       "</svg>\n",
       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
       "    0% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "      border-left-color: var(--fill-color);\n",
       "    }\n",
       "    20% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    30% {\n",
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       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-ed3f8317-15a1-4939-b22e-3ac34f0f44fb button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    1  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    0  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    1  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare    Cabin  Embarked  \n",
       "0         A/5 21171   7.2500  B96 B98         2  \n",
       "1          PC 17599  71.2833      C85         0  \n",
       "2  STON/O2. 3101282   7.9250  B96 B98         2  \n",
       "3            113803  53.1000     C123         2  \n",
       "4            373450   8.0500  B96 B98         2  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "1rHYO8jbtAcJ"
   },
   "outputs": [],
   "source": [
    "# according to the graph we only need to scale Fares only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "id": "GYQdMNMutHll"
   },
   "outputs": [],
   "source": [
    "x = df.drop(['Fare'],axis=1)\n",
    "y = df['Fare']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 562
    },
    "id": "5p2GTF75tnzs",
    "outputId": "caffe60b-89fe-4bd0-8498-f9e9b23269f5"
   },
   "outputs": [
    {
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       "      <th>3</th>\n",
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       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <th>886</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>891 rows × 11 columns</p>\n",
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       "\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
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       "890          891         0       3   \n",
       "\n",
       "                                                  Name  Sex        Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    1  22.000000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.000000      1   \n",
       "2                               Heikkinen, Miss. Laina    0  26.000000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.000000      1   \n",
       "4                             Allen, Mr. William Henry    1  35.000000      0   \n",
       "..                                                 ...  ...        ...    ...   \n",
       "886                              Montvila, Rev. Juozas    1  27.000000      0   \n",
       "887                       Graham, Miss. Margaret Edith    0  19.000000      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"    0  29.699118      1   \n",
       "889                              Behr, Mr. Karl Howell    1  26.000000      0   \n",
       "890                                Dooley, Mr. Patrick    1  32.000000      0   \n",
       "\n",
       "     Parch            Ticket    Cabin  Embarked  \n",
       "0        0         A/5 21171  B96 B98         2  \n",
       "1        0          PC 17599      C85         0  \n",
       "2        0  STON/O2. 3101282  B96 B98         2  \n",
       "3        0            113803     C123         2  \n",
       "4        0            373450  B96 B98         2  \n",
       "..     ...               ...      ...       ...  \n",
       "886      0            211536  B96 B98         2  \n",
       "887      0            112053      B42         2  \n",
       "888      2        W./C. 6607  B96 B98         2  \n",
       "889      0            111369     C148         0  \n",
       "890      0            370376  B96 B98         1  \n",
       "\n",
       "[891 rows x 11 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "5anmJ3fctzkJ"
   },
   "outputs": [],
   "source": [
    "y=pd.DataFrame(y,columns=[\"Fare\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 423
    },
    "id": "U2rQXRxIvbAR",
    "outputId": "a2b4f0bd-655b-42ff-8a40-9482dad73913"
   },
   "outputs": [
    {
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       "summary": "{\n  \"name\": \"y\",\n  \"rows\": 891,\n  \"fields\": [\n    {\n      \"column\": \"Fare\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 49.6934285971809,\n        \"min\": 0.0,\n        \"max\": 512.3292,\n        \"num_unique_values\": 248,\n        \"samples\": [\n          11.2417,\n          51.8625,\n          7.725\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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       "\n",
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       "\n",
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       "  </div>\n"
      ],
      "text/plain": [
       "        Fare\n",
       "0     7.2500\n",
       "1    71.2833\n",
       "2     7.9250\n",
       "3    53.1000\n",
       "4     8.0500\n",
       "..       ...\n",
       "886  13.0000\n",
       "887  30.0000\n",
       "888  23.4500\n",
       "889  30.0000\n",
       "890   7.7500\n",
       "\n",
       "[891 rows x 1 columns]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "RQEyyYrXvydA",
    "outputId": "a687bde5-7859-4388-e764-0936858eea92"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = minmax.fit_transform(y)\n",
    "type(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "id": "vyajvr6ewIHC"
   },
   "outputs": [],
   "source": [
    "y= pd.DataFrame(y,columns=['Fare'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "id": "hTmL-6abwcp2"
   },
   "outputs": [],
   "source": [
    "df = pd.concat([x,y],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 579
    },
    "id": "T94dhPFPwzHG",
    "outputId": "d8a42f2c-c7f4-48a4-ad3b-7259a3ac57a1"
   },
   "outputs": [
    {
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       "type": "dataframe",
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       "\n",
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       "    async function quickchart(key) {\n",
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       "            'suggestCharts', [key], {});\n",
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       "        console.error('Error during call to suggestCharts:', error);\n",
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       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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      ],
      "text/plain": [
       "     PassengerId  Survived  Pclass  \\\n",
       "0              1         0       3   \n",
       "1              2         1       1   \n",
       "2              3         1       3   \n",
       "3              4         1       1   \n",
       "4              5         0       3   \n",
       "..           ...       ...     ...   \n",
       "886          887         0       2   \n",
       "887          888         1       1   \n",
       "888          889         0       3   \n",
       "889          890         1       1   \n",
       "890          891         0       3   \n",
       "\n",
       "                                                  Name  Sex        Age  SibSp  \\\n",
       "0                              Braund, Mr. Owen Harris    1  22.000000      1   \n",
       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...    0  38.000000      1   \n",
       "2                               Heikkinen, Miss. Laina    0  26.000000      0   \n",
       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)    0  35.000000      1   \n",
       "4                             Allen, Mr. William Henry    1  35.000000      0   \n",
       "..                                                 ...  ...        ...    ...   \n",
       "886                              Montvila, Rev. Juozas    1  27.000000      0   \n",
       "887                       Graham, Miss. Margaret Edith    0  19.000000      0   \n",
       "888           Johnston, Miss. Catherine Helen \"Carrie\"    0  29.699118      1   \n",
       "889                              Behr, Mr. Karl Howell    1  26.000000      0   \n",
       "890                                Dooley, Mr. Patrick    1  32.000000      0   \n",
       "\n",
       "     Parch            Ticket    Cabin  Embarked      Fare  \n",
       "0        0         A/5 21171  B96 B98         2  0.014151  \n",
       "1        0          PC 17599      C85         0  0.139136  \n",
       "2        0  STON/O2. 3101282  B96 B98         2  0.015469  \n",
       "3        0            113803     C123         2  0.103644  \n",
       "4        0            373450  B96 B98         2  0.015713  \n",
       "..     ...               ...      ...       ...       ...  \n",
       "886      0            211536  B96 B98         2  0.025374  \n",
       "887      0            112053      B42         2  0.058556  \n",
       "888      2        W./C. 6607  B96 B98         2  0.045771  \n",
       "889      0            111369     C148         0  0.058556  \n",
       "890      0            370376  B96 B98         1  0.015127  \n",
       "\n",
       "[891 rows x 12 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4kakPchDxaPG"
   },
   "source": [
    "**therefore; the above dataset is cleaned and preprocessed by EDA**\n",
    "\n",
    "\n",
    "**END OF THE PROJECT**"
   ]
  }
 ],
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