Exploratory_Data_Analysis(EDA).ipynb

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      "metadata": {
        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/Moksh45/Machine-Learning-Code/blob/main/Exploratory_Data_Analysis(EDA).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-WQqIyMgk8Ci",
        "outputId": "83c26653-e3ad-42ba-ac56-a6d1e298d353"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n"
      ],
      "metadata": {
        "id": "9RXasDShnz9q"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"/content/drive/MyDrive/Colab CSV File/titanic_train.csv\"\n",
        "df = pd.read_csv(path,sep=\",\")"
      ],
      "metadata": {
        "id": "GOnAEynGno30"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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          "height": 548
        },
        "id": "CDzwU_HAn6h5",
        "outputId": "81ac3401-1cf2-435c-c6f1-ef23ec5c04e4"
      },
      "execution_count": 4,
      "outputs": [
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
              "1            2         1       1   \n",
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              "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",
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              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **For Find Missing Data**"
      ],
      "metadata": {
        "id": "M_O84cQfPS7t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull()"
      ],
      "metadata": {
        "id": "9MZx5vnmoSUg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "e8f3ecbc-7680-4d18-c741-028d2f1f0711"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "     PassengerId  Survived  Pclass   Name    Sex    Age  SibSp  Parch  Ticket  \\\n",
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              "      Fare  Cabin  Embarked  \n",
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              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>891 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84171a26-8067-4ad6-b651-e0ef50ccfd5d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-84171a26-8067-4ad6-b651-e0ef50ccfd5d 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-84171a26-8067-4ad6-b651-e0ef50ccfd5d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "6pVGhpRLPqT3",
        "outputId": "0c730648-c84c-446b-ef57-c0abf619686f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff536d550>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style('whitegrid')\n",
        "sns.countplot(x='Survived',data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "phoyayMiQupx",
        "outputId": "bc977cfc-ab98-4195-f021-d5a01860066f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff2ad6370>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style('whitegrid')\n",
        "sns.countplot(x='Survived',hue='Sex',data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "_gnzUDoPRSeB",
        "outputId": "7ab8c89b-3543-444a-ac6b-7a08320dc6f6"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff2659820>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='Survived',hue='Pclass'\t,data=df,palette=\"rainbow\")  #palette use for colour "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "RTjOJqpwRo5K",
        "outputId": "78a357a2-2d6b-4bbd-f585-5b65fc6e4c3f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff25bc9a0>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.distplot(df['Age'].dropna(),kde=False,color='darkred',bins=40)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "kXDoyYFsSenY",
        "outputId": "e12466b9-66d3-496c-f126-9a8a1e045bca"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff25aa6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'].hist(bins=30,color='darkred',alpha=0.3) #this and above both are doing same thing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "TDZ4uftqSJF2",
        "outputId": "7944caba-b659-466a-a6f8-b3aaeeac4c7b"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff242c940>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='SibSp',data=df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "cLD405lSTUFd",
        "outputId": "d7c9baa5-d2ee-45e0-d3bb-a8013c2642f3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff2383610>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Fare'].hist(color='green',bins=40,figsize=(8,4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "vdaJUZMqTiCh",
        "outputId": "c7d9525c-a277-4d63-8fa5-f4e0a2099609"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff22f01f0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,7))\n",
        "sns.boxplot(x='Pclass',y='Age',data=df,palette='winter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "ABFYE1xeUJmH",
        "outputId": "db205240-106b-4e27-c091-9ddeee4e874d"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff223d130>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Remove Null Value from age**"
      ],
      "metadata": {
        "id": "jQM1M9XMUjtq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def impute_age(cols):\n",
        "  Age=cols[0]\n",
        "  Pclass=cols[1]\n",
        "  if pd.isnull(Age):\n",
        "    if Pclass == 1:\n",
        "      return 37\n",
        "    elif Pclass == 2:\n",
        "      return 29\n",
        "    else:\n",
        "      return 24\n",
        "  else:\n",
        "    return Age\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "32CfGQtPUiwv"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Age'] = df[['Age','Pclass']].apply(impute_age,axis=1)"
      ],
      "metadata": {
        "id": "bmWxcmVpVhlj"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 333
        },
        "id": "L3I_m2yWV3JJ",
        "outputId": "7fd2e045-d61c-4516-b18a-c2471d5d7ee9"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff22f37c0>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "JSb1Zx9MV5OL",
        "outputId": "eb92b87c-98c8-456a-f1ba-0efc13bbf719"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
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      ],
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      "execution_count": 19,
      "outputs": []
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    {
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        "df.head()"
      ],
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      "execution_count": 20,
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              "   PassengerId  Survived  Pclass  \\\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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    {
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        "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
      ],
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      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faff2120790>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.get_dummies(df['Embarked'],drop_first=True).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZM34EaXcXh9N",
        "outputId": "254c4cb5-5907-4121-ac56-6d5148ae80ae"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Q  S\n",
              "0  0  1\n",
              "1  0  0\n",
              "2  0  1\n",
              "3  0  1\n",
              "4  0  1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cc09340b-345b-439e-bf73-d64ddd483086\">\n",
              "    <div 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>Q</th>\n",
              "      <th>S</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sex=pd.get_dummies(df['Sex'],drop_first=True)\n",
        "embar=pd.get_dummies(df['Embarked'],drop_first=True)"
      ],
      "metadata": {
        "id": "7IFc8AHrYVHV"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
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      "source": [
        "df.head()"
      ],
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        "colab": {
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          "height": 548
        },
        "id": "hleshMmzYtK6",
        "outputId": "487ddcc0-8216-4f63-d1f6-dd0fcea0eb50"
      },
      "execution_count": 24,
      "outputs": [
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              "   PassengerId  Survived  Pclass  \\\n",
              "0            1         0       3   \n",
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              "2            3         1       3   \n",
              "3            4         1       1   \n",
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              "                                                Name     Sex   Age  SibSp  \\\n",
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      "source": [
        "df.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)"
      ],
      "metadata": {
        "id": "Qqvyv8RGa0sh"
      },
      "execution_count": 25,
      "outputs": []
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    {
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          "height": 206
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        "id": "EPR7vRAXbFTX",
        "outputId": "b4c76f1e-533a-4ca9-9f5c-9a6dffead8cb"
      },
      "execution_count": 26,
      "outputs": [
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          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare\n",
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              "          document.querySelector('#df-49348d6c-d209-40e3-8045-c8634556e4af 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-49348d6c-d209-40e3-8045-c8634556e4af');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df= pd.concat([df,sex,embar],axis=1)"
      ],
      "metadata": {
        "id": "HkRdylMGbPOE"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3fhMArqEbmnW",
        "outputId": "5b760bbe-5842-4b89-ff79-64b5dd92cdaa"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
              "0            1         0       3  22.0      1      0   7.2500     1  0  1\n",
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              "          const element = document.querySelector('#df-66551749-164e-4f74-86c1-53532002d5d1');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Building a Logistic Regression Model**\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "68wyj5Njbudy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train Test Split**"
      ],
      "metadata": {
        "id": "G0E1sWcXcNKu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('Survived',axis=1).head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "JxW2ddcFbphT",
        "outputId": "1d441657-8d4b-4439-e861-5aea14811f70"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   PassengerId  Pclass   Age  SibSp  Parch     Fare  male  Q  S\n",
              "0            1       3  22.0      1      0   7.2500     1  0  1\n",
              "1            2       1  38.0      1      0  71.2833     0  0  0\n",
              "2            3       3  26.0      0      0   7.9250     0  0  1\n",
              "3            4       1  35.0      1      0  53.1000     0  0  1\n",
              "4            5       3  35.0      0      0   8.0500     1  0  1"
            ],
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              "\n",
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              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        const buttonEl =\n",
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              "          const element = document.querySelector('#df-f51cce80-3e47-40bf-b546-ecf570130f89');\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",
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              "  "
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          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Survived'].head()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "X2E-kpvtcLJn",
        "outputId": "a12b3531-4942-4939-fd07-bd6ef5a87ce3"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    0\n",
              "Name: Survived, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    }
  ]
}