CODSOFT_task_1_Titanic_Survival_Predictions.ipynb

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      "cell_type": "markdown",
      "source": [
        "Import Necessary Libraries."
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as snr\n",
        "from sklearn.preprocessing import OneHotEncoder"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Load Data and shows first 10 rows of data."
      ],
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      "source": [
        "data=pd.read_csv(\"/content/tested.csv\")\n",
        "data.head(10)"
      ],
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        "outputId": "40d3d1c7-97b7-45bf-ff3d-0d8467057e85"
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      "execution_count": null,
      "outputs": [
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              "   PassengerId  Survived  Pclass  \\\n",
              "0          892         0       3   \n",
              "1          893         1       3   \n",
              "2          894         0       2   \n",
              "3          895         0       3   \n",
              "4          896         1       3   \n",
              "5          897         0       3   \n",
              "6          898         1       3   \n",
              "7          899         0       2   \n",
              "8          900         1       3   \n",
              "9          901         0       3   \n",
              "\n",
              "                                           Name     Sex   Age  SibSp  Parch  \\\n",
              "0                              Kelly, Mr. James    male  34.5      0      0   \n",
              "1              Wilkes, Mrs. James (Ellen Needs)  female  47.0      1      0   \n",
              "2                     Myles, Mr. Thomas Francis    male  62.0      0      0   \n",
              "3                              Wirz, Mr. Albert    male  27.0      0      0   \n",
              "4  Hirvonen, Mrs. Alexander (Helga E Lindqvist)  female  22.0      1      1   \n",
              "5                    Svensson, Mr. Johan Cervin    male  14.0      0      0   \n",
              "6                          Connolly, Miss. Kate  female  30.0      0      0   \n",
              "7                  Caldwell, Mr. Albert Francis    male  26.0      1      1   \n",
              "8     Abrahim, Mrs. Joseph (Sophie Halaut Easu)  female  18.0      0      0   \n",
              "9                       Davies, Mr. John Samuel    male  21.0      2      0   \n",
              "\n",
              "      Ticket     Fare Cabin Embarked  \n",
              "0     330911   7.8292   NaN        Q  \n",
              "1     363272   7.0000   NaN        S  \n",
              "2     240276   9.6875   NaN        Q  \n",
              "3     315154   8.6625   NaN        S  \n",
              "4    3101298  12.2875   NaN        S  \n",
              "5       7538   9.2250   NaN        S  \n",
              "6     330972   7.6292   NaN        Q  \n",
              "7     248738  29.0000   NaN        S  \n",
              "8       2657   7.2292   NaN        C  \n",
              "9  A/4 48871  24.1500   NaN        S  "
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          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop Unnecessary values"
      ],
      "metadata": {
        "id": "LS2smcmvvgZw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new=data.drop(['PassengerId','Name','Cabin','Ticket'],axis=1)\n",
        "data_new.head(10)"
      ],
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          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "XYsDOnVUvf-5",
        "outputId": "ae5ae5df-77d7-4045-90f7-69246e684225"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Survived  Pclass     Sex   Age  SibSp  Parch     Fare Embarked\n",
              "0         0       3    male  34.5      0      0   7.8292        Q\n",
              "1         1       3  female  47.0      1      0   7.0000        S\n",
              "2         0       2    male  62.0      0      0   9.6875        Q\n",
              "3         0       3    male  27.0      0      0   8.6625        S\n",
              "4         1       3  female  22.0      1      1  12.2875        S\n",
              "5         0       3    male  14.0      0      0   9.2250        S\n",
              "6         1       3  female  30.0      0      0   7.6292        Q\n",
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          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculates statistical values and rounds them to 3 decimal places."
      ],
      "metadata": {
        "id": "fj1hpLd1uYjk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new.describe().round(3)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "n7Cln5o3t-CV",
        "outputId": "8e395f72-f907-4afb-e8eb-4f8ffe514b4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Survived   Pclass      Age    SibSp    Parch     Fare\n",
              "count   418.000  418.000  332.000  418.000  418.000  417.000\n",
              "mean      0.364    2.266   30.273    0.447    0.392   35.627\n",
              "std       0.482    0.842   14.181    0.897    0.981   55.908\n",
              "min       0.000    1.000    0.170    0.000    0.000    0.000\n",
              "25%       0.000    1.000   21.000    0.000    0.000    7.896\n",
              "50%       0.000    3.000   27.000    0.000    0.000   14.454\n",
              "75%       1.000    3.000   39.000    1.000    0.000   31.500\n",
              "max       1.000    3.000   76.000    8.000    9.000  512.329"
            ],
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              "      <th>mean</th>\n",
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              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>21.000</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>39.000</td>\n",
              "      <td>1.000</td>\n",
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              "      <td>31.500</td>\n",
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              "      <td>3.000</td>\n",
              "      <td>76.000</td>\n",
              "      <td>8.000</td>\n",
              "      <td>9.000</td>\n",
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              "    .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-08b3cb91-988a-4d9b-bbae-a8b1e1145936 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-08b3cb91-988a-4d9b-bbae-a8b1e1145936');\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-415dfedb-01a2-4215-95c1-d43033b6914c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-415dfedb-01a2-4215-95c1-d43033b6914c')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-415dfedb-01a2-4215-95c1-d43033b6914c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mark null values as True and returns sum of number of True values in each column"
      ],
      "metadata": {
        "id": "xSiwEGkIurX4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8sDrP4Uujjo",
        "outputId": "9da7964c-2703-4045-995f-797358f287fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived     0\n",
              "Pclass       0\n",
              "Sex          0\n",
              "Age         86\n",
              "SibSp        0\n",
              "Parch        0\n",
              "Fare         1\n",
              "Embarked     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Handling Null Values and Pre Processing the dataset."
      ],
      "metadata": {
        "id": "HpaQpSzxvDJk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new['Age']=data_new['Age'].fillna(data_new['Age'].mean())\n",
        "data_new['Fare']=data_new['Fare'].fillna(data_new['Fare'].mean())\n",
        "data_new.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E4Hog0iyvAgt",
        "outputId": "898bb057-e08f-4add-e9ed-8a82a2af1bac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Survived    0\n",
              "Pclass      0\n",
              "Sex         0\n",
              "Age         0\n",
              "SibSp       0\n",
              "Parch       0\n",
              "Fare        0\n",
              "Embarked    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data Visualization"
      ],
      "metadata": {
        "id": "CIo2nNEG__3g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ax=data_new['Sex'].value_counts().plot(kind='bar',x = 'Survived',figsize=(6,5))    # creating coundplot\n",
        "plt.title(\"Ratio of Male and Female passengers\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.xlabel(\"Sex\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "2bm0ShAHIviX",
        "outputId": "caa0e541-5f86-40b8-8a8e-5c152babaa7e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax=data_new['Survived'].value_counts().plot(kind='bar',figsize=(6,5))    # creating coundplot\n",
        "plt.title(\"Survival Distribution (0: No, 1: Yes)\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.xlabel(\"Survived\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "3P76X0hXLa5K",
        "outputId": "72ce0bde-37b0-474b-d48b-04e1493b153e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax=data_new['Embarked'].value_counts().plot(kind='pie',figsize=(6,5),autopct='%1.1f%%')\n",
        "plt.title(\"Distribution of Passengers by Embarked\")\n",
        "plt.legend([\"Q\", \"S\", \"C\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "Root1mGXw8OQ",
        "outputId": "13727773-2560-4d8f-9e26-d1b32ff94ace"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_, ax = plt.subplots(figsize = (8, 6))\n",
        "snr.countplot(data = data_new, x = \"Embarked\", hue = \"Survived\", ax = ax)\n",
        "ax.set_title(\"Countplot for Embarked with Survival\")\n",
        "ax.set_xlabel(\"Embarked\")\n",
        "ax.set_xticklabels([\"Q\", \"S\", \"C\"])\n",
        "ax.set_ylabel(\"Number of passengers\")\n",
        "ax.legend(title = \"Legends\", labels = [\"Not Survived\", \"Survived\"])\n",
        "plt.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "buknRT8R0pf3",
        "outputId": "6c518501-f8fa-453f-a2ea-10298f7160d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Change the catagorical data into numerical data by using OneHotEncoding."
      ],
      "metadata": {
        "id": "1zUiw2nLBJMA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_new['Sex']=data_new['Sex'].apply({'male':1,'female':0}.get)\n",
        "data_new['Embarked']=data_new['Embarked'].apply({'S':1,'Q':2,'C':3}.get)\n",
        "data_new.head()"
      ],
      "metadata": {
        "id": "EFyTF-nT5n8y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "9641111e-fa50-444b-d7eb-63eb5b7a9ab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Survived  Pclass  Sex   Age  SibSp  Parch     Fare  Embarked\n",
              "0         0       3    1  34.5      0      0   7.8292         2\n",
              "1         1       3    0  47.0      1      0   7.0000         1\n",
              "2         0       2    1  62.0      0      0   9.6875         2\n",
              "3         0       3    1  27.0      0      0   8.6625         1\n",
              "4         1       3    0  22.0      1      1  12.2875         1"
            ],
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-72a6ad66-40b1-40b3-8b4f-8fe9245147b3');\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",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "\n",
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              "    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-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
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              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Deviding the data into Dependent and Independent variables."
      ],
      "metadata": {
        "id": "Q1fzT10mDbYj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=data_new.drop(['Survived'],axis=1)\n",
        "y=data_new['Survived']"
      ],
      "metadata": {
        "id": "rjmOcgBnC_Qw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Deviding the cleaned data into training and testing sets."
      ],
      "metadata": {
        "id": "8p4MDe24EUX9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(x,y,train_size=0.8)"
      ],
      "metadata": {
        "id": "puDNHn3lESYA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating first machine learning model 'k-nearest neighbour'."
      ],
      "metadata": {
        "id": "z2C2RBmRFgcU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn=KNeighborsClassifier(n_neighbors=5)"
      ],
      "metadata": {
        "id": "qXdDrpNWE7Mo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Train the model"
      ],
      "metadata": {
        "id": "d7JR1i3SHjJk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "knn.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "ETn9mYoTHVpi",
        "outputId": "03e1a243-b577-4232-eb2b-f003ff3cfcbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Make predictions on model"
      ],
      "metadata": {
        "id": "lxPeGQKHIeWm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=knn.predict(x_test)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U8rJkKnbHraV",
        "outputId": "1766da1d-b70b-47dd-aab7-ba27ddb5b1ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 1 0 0 0 0 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0\n",
            " 1 0 0 1 1 0 1 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 0 0 1 1 0 0 1 1 0 0 1 0 1 1 0\n",
            " 0 0 0 0 0 1 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check the Confusion metrix and Accuracy score."
      ],
      "metadata": {
        "id": "DRHZmVgYI1xo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "ac=accuracy_score(y_test,predictions)\n",
        "cm=confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "id": "Y43-Aba3IvF4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HxJ2W0n2Jbfj",
        "outputId": "a0a30ad8-6e2b-4ee1-9a5e-fe2352fb0ea9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[44 11]\n",
            " [10 19]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ac)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ksNtI8LuJgVB",
        "outputId": "8bb7c65e-edb0-4e17-8f5c-fc82033b0f86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.75\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating Second Machine Learning Model 'Decision Tree'."
      ],
      "metadata": {
        "id": "rjE_4AcBa3M4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "tree=DecisionTreeClassifier()"
      ],
      "metadata": {
        "id": "tRG0r2mBJkIL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tree.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "BtBJdhhjbb9D",
        "outputId": "bcc30652-92bd-43a6-9b1c-aa91c35beb79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {color: black;background-color: white;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=tree.predict(x_test)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1XVNhn7cAj7",
        "outputId": "5f20c1d3-ebad-4e5c-e905-a3434437f9cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 0 1 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 1 0 0\n",
            " 1 0 0 1 1 1 1 1 1 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 0\n",
            " 0 0 0 1 0 1 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check the Confusion metrix and Accuracy score."
      ],
      "metadata": {
        "id": "uS1PN7cvd0Bd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "ac=accuracy_score(y_test,predictions)\n",
        "cm=confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "id": "T_YfvA3aceMB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yyQp75Luci3X",
        "outputId": "ac56b3d9-ff4e-4802-be2c-97a489ad8535"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[55  0]\n",
            " [ 0 29]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ac)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qKsoMI6Ycml6",
        "outputId": "0bd76b81-8ed4-4929-dd05-aae8b4a381b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating third machine learning model 'Support Vector Machine'."
      ],
      "metadata": {
        "id": "0dOTBgkEcvn5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "svm=SVC()"
      ],
      "metadata": {
        "id": "lCGzLejvcn-y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svm.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "ZIZmZFp0dHOf",
        "outputId": "50e6b6fe-057d-47f8-f4d4-b66ad584a59f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ],
            "text/html": [
              "<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions=svm.predict(x_test)\n",
        "print(predictions)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TGqYgjvdRZX",
        "outputId": "61093b4b-ba90-4a7a-b087-08443e363be9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 0 0 0 0 0 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Check the Confusion metrix and Accuracy score."
      ],
      "metadata": {
        "id": "39FaFZJbdfIN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "ac=accuracy_score(y_test,predictions)\n",
        "cm=confusion_matrix(y_test,predictions)"
      ],
      "metadata": {
        "id": "w77RW2sadYuC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pOEqdUjYdr9W",
        "outputId": "a6d917a7-fa30-4088-94da-e91bf52543c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[53  2]\n",
            " [28  1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(ac)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Yxe85U_pd54P",
        "outputId": "abfb140c-cd80-46c9-9428-9da0babf8e35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6428571428571429\n"
          ]
        }
      ]
    }
  ]
}