Titanic_Project_w_DL.ipynb

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        "**Import Data**"
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      "source": [
        "import pandas as pd\n",
        "df_test = pd.read_csv('drive/MyDrive/ML/Titanic Project/ttest.csv')"
      ],
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        "id": "P0aMJoHh--Mf"
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      "source": [
        "df_train = pd.read_csv('drive/MyDrive/ML/Titanic Project/ttrain.csv')"
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      "execution_count": 2,
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              "      <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",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>886</th>\n",
              "      <td>887</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>Montvila, Rev. Juozas</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>211536</td>\n",
              "      <td>13.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>887</th>\n",
              "      <td>888</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Graham, Miss. Margaret Edith</td>\n",
              "      <td>female</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>112053</td>\n",
              "      <td>30.0000</td>\n",
              "      <td>B42</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>888</th>\n",
              "      <td>889</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
              "      <td>female</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>W./C. 6607</td>\n",
              "      <td>23.4500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>889</th>\n",
              "      <td>890</td>\n",
              "      <td>1</td>\n",
              "      <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>30.0000</td>\n",
              "      <td>C148</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>890</th>\n",
              "      <td>891</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Dooley, Mr. Patrick</td>\n",
              "      <td>male</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>370376</td>\n",
              "      <td>7.7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</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-4afd92b5-26ff-4801-be21-4f10fb9044e5')\"\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df_train.columns)\n",
        "print(df_test.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3skOYGL_cPs",
        "outputId": "843500bd-9653-49a4-ca91-6b4e8f8307c3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
            "       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
            "      dtype='object')\n",
            "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
            "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
            "      dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Dictionary:**\n",
        "* PassengerId - A numerical id assigned to each passenger\n",
        "* Survived - Whether the passenger survived (1), or didn't (0). This is going to be the dependent variable of our study\n",
        "* Pclass - The class the passenger was in - first class (1), second class (2), or third class (3). Pclass is going to be one of the independent variables in our study\n",
        "* Name - the name of the passenger\n",
        "* Sex - The gender of the passenger - male or female. Sex is going to be one of the independent variables in our study\n",
        "* Age - The age of the passenger. Fractional. Age (of age groups) is going to be one of the independent variables in our study\n",
        "* SibSp - The number of siblings and spouses the passenger had on board\n",
        "* Parch - The number of parents and children the passenger had on board\n",
        "* Ticket - The ticket number of the passenger\n",
        "* Fare - How much the passenger paid for the ticket\n",
        "* Cabin - Which cabin the passenger was in\n",
        "* Embarked - Where the passenger boarded the Titanic"
      ],
      "metadata": {
        "id": "bj4VqtpElLCh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Loading packages**"
      ],
      "metadata": {
        "id": "l5G9woYcCMRc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "a6gYRbPuClkG"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "sns.set_style(\"whitegrid\")\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ],
      "metadata": {
        "id": "rOyF17u8dapp"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score\n",
        "from sklearn.metrics import confusion_matrix,classification_report"
      ],
      "metadata": {
        "id": "HbAnSZSlApLQ"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def classification_(train,y):\n",
        "    \n",
        "    \n",
        "    g=GaussianNB()\n",
        "    b=BernoulliNB()\n",
        "    k=KNeighborsClassifier()\n",
        "    svc=SVC()\n",
        "    d=DecisionTreeClassifier()\n",
        "    log=LogisticRegression()\n",
        "    gbc=GradientBoostingClassifier()\n",
        "    mn=MultinomialNB()\n",
        "    rf=RandomForestClassifier()\n",
        "    ab=AdaBoostClassifier()\n",
        "\n",
        "    x=train\n",
        "    y=y\n",
        "    \n",
        "    algos=[g,b,k,svc,d,log,gbc,mn,rf,ab]\n",
        "    algos_name=['Gaussian','Bernoulli','KNeigbors','SVC','DecisionTree','LogisticRegr','GradientBoosting','Multinominal','RandomForest','AdaBoost']\n",
        "    \n",
        "    accuracy = []\n",
        "    precision = []\n",
        "    recall = []\n",
        "    f1 = []\n",
        "   \n",
        "    result=pd.DataFrame(columns=['AccuracyScore','PrecisionScore','RecallScore','f1_Score'],index=algos_name)\n",
        "    \n",
        "    for i in algos:\n",
        "        \n",
        "        predict=i.fit(x,y).predict(x)\n",
        "        \n",
        "        accuracy.append(accuracy_score(y,predict))\n",
        "        precision.append(precision_score(y,predict))\n",
        "        recall.append(recall_score(y,predict))\n",
        "        f1.append(f1_score(y,predict))\n",
        "        \n",
        "\n",
        "    \n",
        "    \n",
        "    result.AccuracyScore=accuracy\n",
        "    result.PrecisionScore=precision\n",
        "    result.RecallScore=recall\n",
        "    result.f1_Score=f1\n",
        "    \n",
        "    \n",
        "    return result.sort_values('f1_Score',ascending=False)"
      ],
      "metadata": {
        "id": "_ON6Y2Pk7KGI"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**EDA - Exploratory Data Analysis**"
      ],
      "metadata": {
        "id": "_UIgS3NRBg1E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = df_train.append(df_test)"
      ],
      "metadata": {
        "id": "pPEdzT8AEPiU"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "collapsed": true,
        "id": "JP-fskKsGgle",
        "outputId": "fc0d2af1-b4cf-48e2-fe8c-0e09d78c46ab"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass                          Name     Sex  \\\n",
              "413         1305       NaN       3            Spector, Mr. Woolf    male   \n",
              "414         1306       NaN       1  Oliva y Ocana, Dona. Fermina  female   \n",
              "415         1307       NaN       3  Saether, Mr. Simon Sivertsen    male   \n",
              "416         1308       NaN       3           Ware, Mr. Frederick    male   \n",
              "417         1309       NaN       3      Peter, Master. Michael J    male   \n",
              "\n",
              "      Age  SibSp  Parch              Ticket      Fare Cabin Embarked  \n",
              "413   NaN      0      0           A.5. 3236    8.0500   NaN        S  \n",
              "414  39.0      0      0            PC 17758  108.9000  C105        C  \n",
              "415  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  \n",
              "416   NaN      0      0              359309    8.0500   NaN        S  \n",
              "417   NaN      1      1                2668   22.3583   NaN        C  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ba2da2de-c6df-4719-88f7-7180748e5110\">\n",
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              "      <th>PassengerId</th>\n",
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              "      <th>Sex</th>\n",
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              "  <tbody>\n",
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              "      <th>413</th>\n",
              "      <td>1305</td>\n",
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              "      <td>Spector, Mr. Woolf</td>\n",
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              "      <td>SOTON/O.Q. 3101262</td>\n",
              "      <td>7.2500</td>\n",
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              "    </tr>\n",
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              "      <th>416</th>\n",
              "      <td>1308</td>\n",
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              "      <td>Ware, Mr. Frederick</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>359309</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>417</th>\n",
              "      <td>1309</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3</td>\n",
              "      <td>Peter, Master. Michael J</td>\n",
              "      <td>male</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2668</td>\n",
              "      <td>22.3583</td>\n",
              "      <td>NaN</td>\n",
              "      <td>C</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\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-ba2da2de-c6df-4719-88f7-7180748e5110 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-ba2da2de-c6df-4719-88f7-7180748e5110');\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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "shr-p0LxG8nK",
        "outputId": "4e31d195-0bdb-497e-9e86-39b691ed41d4"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1309, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#information about the dataframe (column, dtypes, non-null count)\n",
        "\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wm1ehNwXH80A",
        "outputId": "2284a78e-ad0b-4827-b41e-ad944a41e460"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1309 entries, 0 to 417\n",
            "Data columns (total 12 columns):\n",
            " #   Column       Non-Null Count  Dtype  \n",
            "---  ------       --------------  -----  \n",
            " 0   PassengerId  1309 non-null   int64  \n",
            " 1   Survived     891 non-null    float64\n",
            " 2   Pclass       1309 non-null   int64  \n",
            " 3   Name         1309 non-null   object \n",
            " 4   Sex          1309 non-null   object \n",
            " 5   Age          1046 non-null   float64\n",
            " 6   SibSp        1309 non-null   int64  \n",
            " 7   Parch        1309 non-null   int64  \n",
            " 8   Ticket       1309 non-null   object \n",
            " 9   Fare         1308 non-null   float64\n",
            " 10  Cabin        295 non-null    object \n",
            " 11  Embarked     1307 non-null   object \n",
            "dtypes: float64(3), int64(4), object(5)\n",
            "memory usage: 132.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Sex.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dTfd5ChWJeUy",
        "outputId": "3a5f2c5d-912e-422c-dede-e0d9e8236d63"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "male      843\n",
              "female    466\n",
              "Name: Sex, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Parch.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "94HqG4uUKwGn",
        "outputId": "ae500ef5-383c-413e-e39d-87a39d7c1579"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1002\n",
              "1     170\n",
              "2     113\n",
              "3       8\n",
              "5       6\n",
              "4       6\n",
              "6       2\n",
              "9       2\n",
              "Name: Parch, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Embarked.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "waXRoQs3KzD1",
        "outputId": "93ce7334-a818-43ba-d127-a3e671654b7c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "S    914\n",
              "C    270\n",
              "Q    123\n",
              "Name: Embarked, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#empty values\n",
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2r6D7_E9IhnF",
        "outputId": "e791c35f-2f5a-4c00-ccc9-dc762ad79cb5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId       0\n",
              "Survived        418\n",
              "Pclass            0\n",
              "Name              0\n",
              "Sex               0\n",
              "Age             263\n",
              "SibSp             0\n",
              "Parch             0\n",
              "Ticket            0\n",
              "Fare              1\n",
              "Cabin          1014\n",
              "Embarked          2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "yxV2kX8aMGpZ",
        "outputId": "bbe81293-ec8a-4870-eeb6-caa9dddf1347"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       PassengerId    Survived       Pclass          Age        SibSp  \\\n",
              "count  1309.000000  891.000000  1309.000000  1046.000000  1309.000000   \n",
              "mean    655.000000    0.383838     2.294882    29.881138     0.498854   \n",
              "std     378.020061    0.486592     0.837836    14.413493     1.041658   \n",
              "min       1.000000    0.000000     1.000000     0.170000     0.000000   \n",
              "25%     328.000000    0.000000     2.000000    21.000000     0.000000   \n",
              "50%     655.000000    0.000000     3.000000    28.000000     0.000000   \n",
              "75%     982.000000    1.000000     3.000000    39.000000     1.000000   \n",
              "max    1309.000000    1.000000     3.000000    80.000000     8.000000   \n",
              "\n",
              "             Parch         Fare  \n",
              "count  1309.000000  1308.000000  \n",
              "mean      0.385027    33.295479  \n",
              "std       0.865560    51.758668  \n",
              "min       0.000000     0.000000  \n",
              "25%       0.000000     7.895800  \n",
              "50%       0.000000    14.454200  \n",
              "75%       0.000000    31.275000  \n",
              "max       9.000000   512.329200  "
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              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>1309.000000</td>\n",
              "      <td>891.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1046.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1308.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>655.000000</td>\n",
              "      <td>0.383838</td>\n",
              "      <td>2.294882</td>\n",
              "      <td>29.881138</td>\n",
              "      <td>0.498854</td>\n",
              "      <td>0.385027</td>\n",
              "      <td>33.295479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>378.020061</td>\n",
              "      <td>0.486592</td>\n",
              "      <td>0.837836</td>\n",
              "      <td>14.413493</td>\n",
              "      <td>1.041658</td>\n",
              "      <td>0.865560</td>\n",
              "      <td>51.758668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.170000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>328.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.895800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>655.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>14.454200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>982.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>31.275000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1309.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>512.329200</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1118a0a5-06dc-4a64-b423-b77f6ccf108d')\"\n",
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              "          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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          "metadata": {},
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      "source": [
        "df.corr()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "DJkR-pwXP9xu",
        "outputId": "109458bd-fabf-48aa-b06b-4137d8d76318"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
              "PassengerId     1.000000 -0.005007 -0.038354  0.028814 -0.055224  0.008942   \n",
              "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
              "Pclass         -0.038354 -0.338481  1.000000 -0.408106  0.060832  0.018322   \n",
              "Age             0.028814 -0.077221 -0.408106  1.000000 -0.243699 -0.150917   \n",
              "SibSp          -0.055224 -0.035322  0.060832 -0.243699  1.000000  0.373587   \n",
              "Parch           0.008942  0.081629  0.018322 -0.150917  0.373587  1.000000   \n",
              "Fare            0.031428  0.257307 -0.558629  0.178740  0.160238  0.221539   \n",
              "\n",
              "                 Fare  \n",
              "PassengerId  0.031428  \n",
              "Survived     0.257307  \n",
              "Pclass      -0.558629  \n",
              "Age          0.178740  \n",
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              "      <td>-0.077221</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.257307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>-0.038354</td>\n",
              "      <td>-0.338481</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.408106</td>\n",
              "      <td>0.060832</td>\n",
              "      <td>0.018322</td>\n",
              "      <td>-0.558629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>0.028814</td>\n",
              "      <td>-0.077221</td>\n",
              "      <td>-0.408106</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.243699</td>\n",
              "      <td>-0.150917</td>\n",
              "      <td>0.178740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>-0.055224</td>\n",
              "      <td>-0.035322</td>\n",
              "      <td>0.060832</td>\n",
              "      <td>-0.243699</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.373587</td>\n",
              "      <td>0.160238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>0.008942</td>\n",
              "      <td>0.081629</td>\n",
              "      <td>0.018322</td>\n",
              "      <td>-0.150917</td>\n",
              "      <td>0.373587</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.221539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>0.031428</td>\n",
              "      <td>0.257307</td>\n",
              "      <td>-0.558629</td>\n",
              "      <td>0.178740</td>\n",
              "      <td>0.160238</td>\n",
              "      <td>0.221539</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e4d64238-d581-4ffc-b2f6-9fe812d94bea')\"\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-e4d64238-d581-4ffc-b2f6-9fe812d94bea 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-e4d64238-d581-4ffc-b2f6-9fe812d94bea');\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": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Data Visualization**"
      ],
      "metadata": {
        "id": "8rVhZVuZtjNL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(df_train, hue ='Survived')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 888
        },
        "id": "BzMmjaD_t1kU",
        "outputId": "1eb218bc-fe0e-4716-f1af-395d0c005874"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1131.88x1080 with 42 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(data = df_train, x='Age', hue='Survived', multiple='stack');\n",
        "sns.displot(data = df_train, x='Pclass', hue='Survived', multiple='stack');\n",
        "sns.displot(data = df_train, x='Sex', hue='Survived', multiple='stack');\n",
        "sns.displot(data = df_train, x='Pclass', hue='Survived', multiple='stack');\n",
        "sns.displot(data = df_train, x='SibSp', hue='Survived', multiple='stack');\n",
        "sns.displot(data = df_train, x='Parch', hue='Survived', multiple='stack');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "76zcr36CMY_k",
        "outputId": "b0597c88-7242-415e-91be-b1a5f3e11e19"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 411.875x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 411.875x360 with 1 Axes>"
            ],
            "image/png": 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ww1q9erXfJwPYrJ/3wy5g79693p9DQkLUtWtXxcTE+LUiE/Ly8hQXF2f8OSYEQ45gyBAsOYIhQ7DkCIYM/uS4PW23sQzvTEs0tuxLiU+HzgYOHKjY2FhVVFSorKxMl112melcAIAWwqei2bJliyZOnKj3339fW7du9f4MAEB9fPqM5uWXX1Z6ero6d+4sSTpx4oQmT558zlPmAAD4JZ/2aCzL8paMJHXs2FE+fLQDAIBvezSDBg3S1KlTvV/W2bJliwYPHmw0GACgZbhg0Xz77bf68ccf9ac//UlZWVnat2+fJKl///4aM2ZMQAICAJq3Cx46W7Jkidq3by9JGjZsmObNm6d58+bJ6XRqyZIlAQkIAGgaubm5Sk5OltPpvKjbC1ywaH788Uddd911Z02/7rrr9N133zV4pQAA31W7awO+PI/Ho8WLF2v16tXKzMzU5s2b9dVXXzVofRc8dFZeXn7ex06fPt2gFQIA/BMW2qpRv5jqyxdRDx48qO7du6tbt26SpJEjRyo7O1vXXnut3+u74B5Nnz599O677541fd26derdu7ffKwMANA8ul6vOFWDsdrtcLleDlnXBPZpHH31UDzzwgN577z1vsRw6dEg1NTX6y1/+0qAVAgAuLRcsmssvv1zvvPOO9uzZ471hzpAhQ5SYyPV/AKAls9vtKiws9P7ucrlkt9sbtCyfvkeTkJCghISEBq0AAND89O3bV0eOHFFBQYHsdrsyMzP17LPPNmhZPhUNAODSEhoaqgULFuiee+6Rx+PRrbfeqh49ejRsWY2cDQDQyKrdtY16y4Jqd63CQuu/AtmQIUM0ZMiQi15fg+6wCQAIHF9KoSmXVx+KBgBgFEUDADCKogEAGEXRAACMuuSKpvuvrjG+jsa+AB4ANGeX3OnN7dqENerF6c6lMU9DBICmMG/ePOXk5Khz587avHnzRS3rktujAYBmx10d8OWNHz9eq1evbpTVXXJ7NADQ7ISGSX8b1XjLm1z/HsqAAQN07NixRlkdezQAAKMoGgCAUcaK5vjx45o0aZJGjBihkSNHas2aNZKk0tJSTZkyRcOGDdOUKVN08uRJSZJlWXrqqafkdDo1evRoHT582FQ0AEAAGSuakJAQzZ07V1u2bNHatWv11ltv6auvvlJaWpoSExOVlZWlxMREpaWlSZJyc3N15MgRZWVl6cknn9TChQtNRQMABJCxoomOjvbelbN9+/aKjY2Vy+VSdna2UlJSJEkpKSnasWOHJHmn22w29e/fX2VlZSoqKjIVDwBwAQ899JBuv/12/fe//9XgwYO1bt26Bi8rIGedHTt2THl5eerXr5+Ki4sVHR0tSbriiitUXFws6ez7U8fExMjlcnnnBYBLlrvapzPF/FpeaNgFZ1m+fHmjrc540VRUVGjGjBl69NFH1b59+zqP2Ww22Wy2Bi+7qqpKeXl5fj0nLi5OlZWVDV6nr+rLdfr0ab+zN7ZgyBAsOYIhQ7DkCIYMvuYIxN9zQ/6NaXT1lEKTL6++1ZlceE1NjWbMmKHRo0dr2LBhkqTOnTurqKhI0dHRKioqUlRUlKSz709dWFhY7/2pW7du3aA3tV27dn4/x1/15crLyzMzIP0QDBmCJUcwZAiWHMGQwZ8cpv+eg2FbNHfGPqOxLEuPPfaYYmNjNWXKFO90h8OhjIwMSVJGRoaSkpLqTLcsSwcOHFBERASHzQCgBTC2R7Nv3z5t3LhRPXv21NixYyWd+XBp2rRpmjVrltLT09WlSxetXLlS0plbhu7atUtOp1Nt27bVkiVLTEUDAASQsaK58cYb9eWXX57zsZ+/U/NLNptNTzzxhKk4AIAmwpUBAABGUTQAAKMoGgCAURQNAMAoigYtkr+30/b3uxLcrhvwHTc+Q4sUFtrKr1t2V1ZW+vXFP27XDfiOPRoAgFEUDQDAKIoGAGAURQMAMIqiAQAYRdEAAIyiaAAARlE0AACjKBoAgFEUDQDAKIoGAGAURQMAMIqiAQAYRdEAAIyiaAAARlE0AACjKBoAgFEUDQDAKIoGAGAURQMAMIqiAQAYRdEAAIyiaAAARlE0AACjKBoAgFEUDQDAKIoGAGBUaFMHCLTqGo/emZZodh1uj8JCQ4yuAwCai0uuaMIuC5H+NsrsOiZvNrp8AGhOOHQGADCKogEAGEXRAACMomgAAEZRNAAAoygaAIBRFA0AwCiKBgBg1CX3hU0ALUe12+yVPqprPGe+5I2LQtEAaLbCQs1e6YOrfDQODp0BAIyiaAAARlE0AACjKBoAgFEUDQDAKIoGAGCUsaKZN2+eEhMTNWrU/z/1sLS0VFOmTNGwYcM0ZcoUnTx5UpJkWZaeeuopOZ1OjR49WocPHzYVCwAQYMaKZvz48Vq9enWdaWlpaUpMTFRWVpYSExOVlpYmScrNzdWRI0eUlZWlJ598UgsXLjQVCwAQYMaKZsCAAerQoUOdadnZ2UpJSZEkpaSkaMeOHXWm22w29e/fX2VlZSoqKjIVDQAQQAG9MkBxcbGio6MlSVdccYWKi4slSS6XSzExMd75YmJi5HK5vPOeT1VVlfLy8vzKEBcXp4rKCj+T+ydcqjfX6dOn/c7e2IIhg6kccXFxqqys9Hn+2tpav+aX6n+PGyIY3pNgyOBrDtN/z778Lf+vuLg4M2GasSa7BI3NZpPNZruoZbRu3bpBb2p4u/CLWq8v6suVl5fX5AMyGDKYzNGuXTuf562srPRrfsnMPyjB8J4EQwZ/cpj+ew6GbdHcBfSss86dO3sPiRUVFSkqKkqSZLfbVVhY6J2vsLBQdrs9kNEAAIYEtGgcDocyMjIkSRkZGUpKSqoz3bIsHThwQBEREfUeNgMANA/GDp099NBD2rt3r0pKSjR48GA9+OCDmjZtmmbNmqX09HR16dJFK1eulCQNGTJEu3btktPpVNu2bbVkyRJTsQAAAWasaJYvX37O6WvWrDlrms1m0xNPPGEqCgCgCXFlAACAURQNAMAo7rCJFsn4LX7dnjN3dwRQL4oGLZK/t/itqKzw6/sY3OIX8B2HzgAARlE0AACjKBoAgFEUDQDAKIoGAGAURQMAMIqiAQAYRdEAAIyiaAAARlE0AACjKBoAgFEUDQDAKIoGAGAURQMAMIqiAQAYRdEAAIyiaAAARlE0AACjKBoAgFEUDQDAKIoGAGAURQMAMIqiAQAYRdEAAIyiaAAARlE0AACjKBoAgFEUDQDAKIoGAGAURQMAMIqiAQAYRdEAAIyiaAAARlE0AACjKBoAgFEUDQDAKIoGAGAURQMAMIqiAQAYRdEAAIyiaAAARlE0AACjKBoAgFEUDQDAKIoGAGAURQMAMIqiAQAYFVRFk5ubq+TkZDmdTqWlpTV1HABAIwiaovF4PFq8eLFWr16tzMxMbd68WV999VVTxwIAXKSgKZqDBw+qe/fu6tatm8LCwjRy5EhlZ2c3dSwAwEWyWZZlNXUISXr//ff14YcfKjU1VZKUkZGhgwcPasGCBed9zoEDB9S6detARQSAeoWGhqpHjx5NHSOohDZ1gIvRv3//po4AAKhH0Bw6s9vtKiws9P7ucrlkt9ubMBEAoDEETdH07dtXR44cUUFBgaqrq5WZmSmHw9HUsQAAFyloDp2FhoZqwYIFuueee+TxeHTrrbdynBMAWoCgORkAANAyBc2hMwBAy0TRAACMCprPaBrDvHnzlJOTo86dO2vz5s1nPW5ZllJTU7Vr1y61adNGTz/9tHr37i1J2rBhg1atWiVJ+sMf/qBx48YZybBp0ya98sorkqTw8HAtXLhQvXr1kiQ5HA6Fh4erVatWCgkJ0fr1641k+PTTT3X//ffrqquukiQ5nU498MADks5cBig1NVW1tbWaOHGipk2b1qAMvuRYvXq13nvvPUlnrgzx9ddfa/fu3erYsWOjbYvjx49rzpw5Ki4uls1m02233abf//73deYxPS58yRCIceFLDtNjw5cMgRgXVVVVuuuuu1RdXS2Px6Pk5GTNmDGjzjzV1dWaM2eODh8+rI4dO2rFihXe7fLXv/5V6enpatWqlR5//HH99re/bVCOS4bVguzdu9c6dOiQNXLkyHM+npOTY02dOtWqra219u/fb02YMMGyLMsqKSmxHA6HVVJSYpWWlloOh8MqLS01kmHfvn3eZefk5HgzWJZlDR061CouLm7Qev3JsGfPHmvatGlnTXe73VZSUpJ19OhRq6qqyho9erSVn59vLMcvZWdnW5MmTfL+3ljbwuVyWYcOHbIsy7LKy8utYcOGnfWaTI8LXzIEYlz4ksP02PAlwy+ZGhe1tbXWqVOnLMuyrOrqamvChAnW/v3768zz97//3Zo/f75lWZa1efNma+bMmZZlWVZ+fr41evRoq6qqyjp69KiVlJRkud3ui87UkrWoQ2cDBgxQhw4dzvt4dna2UlJSZLPZ1L9/f5WVlamoqEgfffSRbr75ZnXs2FEdOnTQzTffrA8//NBIhhtuuMH7eP/+/et8d6ix1JfhfBr7MkD+5MjMzNSoUaMavK7ziY6O9u6dtG/fXrGxsXK5XHXmMT0ufMkQiHHhS47zaayx4W8GU+PCZrMpPDxckuR2u+V2u2Wz2erMs3PnTu8ebHJysnbv3i3LspSdna2RI0cqLCxM3bp1U/fu3XXw4MFGz9iStKiiqY/L5VJMTIz395iYGLlcrrOm2+12n/8AL0Z6eroGDx5cZ9rUqVM1fvx4rV271ui6Dxw4oDFjxuiee+5Rfn6+pLO3T6C2w08//aQPP/xQw4YNqzO9sbfFsWPHlJeXp379+tWZHshxcb4MvxSIcXGhHIEaG/VtC9PjwuPxaOzYsbrpppt00003nXNcXHnllZLOfP0iIiJCJSUlTfZ30py1qM9ompM9e/YoPT1db731lnfa22+/LbvdruLiYk2ZMkWxsbEaMGBAo6+7d+/e2rlzp8LDw7Vr1y5Nnz5dWVlZjb4eX33wwQe64YYb1LFjR++0xt4WFRUVmjFjhh599FG1b9++MWIbyRCIcXGhHIEaG75sC9PjIiQkRBs3blRZWZmmT5+u//znP+rZs2eDloULu6T2aP73MjeFhYWy2+0Bv/zNF198occff1wvvfSSOnXqVCefJHXu3FlOp9PY7nj79u29hw2GDBkit9utEydONNllgDIzMzVy5Mg60xpzW9TU1GjGjBkaPXr0Wf93/PO6TI+L+jJIgRkX9eUIxNjwZVtI5sfFzyIjIxUfH3/WYVG73a7jx49LOnN4rby8XJ06deJyWQ1wSRWNw+FQRkaGLMvSgQMHFBERoejoaA0aNEgfffSRTp48qZMnT+qjjz7SoEGDjGT4/vvv9eCDD2rp0qX61a9+5Z1eWVmpU6dOeX/++OOPjV0Z4YcffpD1f9/TPXjwoGpra9WpU6cmuQxQeXm5/vnPfyopKck7rTG3hWVZeuyxxxQbG6spU6accx7T48KXDIEYF77kMD02fMkgmR8XJ06cUFlZmSTp9OnT+uSTTxQbG1tnHofDoQ0bNkiStm3bpoSEBNlsNjkcDmVmZqq6uloFBQU6cuSIfvOb3zQox6WiRR06e+ihh7R3716VlJRo8ODBevDBB+V2uyVJd9xxh4YMGaJdu3bJ6XSqbdu2WrJkiSSpY8eOuv/++zVhwgRJ0vTp0+vsrjdmhhdffFGlpaVatGiRJHlP0SwuLtb06dMlnTl2PGrUqLOO0zdWhm3btuntt99WSEiI2rRpo+XLl8tmszX6ZYDqyyFJ27dv180336x27dp5n9eY22Lfvn3auHGjevbsqbFjx3pzff/9994cpseFLxkCMS58yWF6bPiSQTI/LoqKijR37lx5PB5ZlqVbbrlFQ4cO1XPPPac+ffooKSlJEyZM0COPPCKn06kOHTpoxYoVkqQePXpo+PDhGjFihEJCQrRgwQKFhIQ0KMelgkvQAACMuqQOnQEAAo+iAQAYRdEAAIyiaAAARlE0AACjWtTpzUBcXJx69uwpj8ej2NhY/fnPf1bbtm3POe8LL7ygdu3aaerUqQFOCVxa2KNBi9KmTRtt3LhRmzdv1mWXXaZ33nmnqSMBlzz2aNBi3Xjjjfryyy8lSRkZGXr11Vdls9l03XXX6ZlnnqMtmYoAAAIaSURBVKkz77vvvqu1a9eqpqZG3bt319KlS9W2bVtt3bpVL774olq1aqWIiAi9+eabys/P17x581RTU6Pa2lq98MILuvrqq5vgFQLNA0WDFsntdis3N1e//e1vlZ+fr1WrVuntt99WVFSUSktLz5rf6XTqtttukyStWLFC6enpmjRpkl566SW9+uqrstvt3kuWvPPOO7r77rs1ZswYVVdXq7a2NqCvDWhuKBq0KKdPn/Ze2uTGG2/UhAkTtHbtWt1yyy2KioqSpHNeRiY/P18rV65UeXm5KioqvNc0u/766zV37lwNHz5cTqdT0pn7xbz88ssqLCzUsGHD2JsB6kHRoEX5+TMaf82dO1cvvfSSevXqpfXr12vv3r2SpMWLF+uzzz5TTk6Obr31Vv3jH//Q6NGj1a9fP+Xk5GjatGlatGiREhMTG/ulAC0GJwOgxUtISND777+vkpISSTrnobOKigpdccUVqqmp8d6vXpKOHj2qfv36aebMmerUqZMKCwtVUFCgbt266e6771ZSUpL3cyAA58YeDVq8Hj166L777tOkSZPUqlUr/frXv9bTTz9dZ56ZM2dq4sSJioqKUr9+/VRRUSFJWrp0qb799ltZlqWEhAT16tVLr7zyijZu3KjQ0FBdfvnluvfee5viZQHNBldvBgAYxaEzAIBRFA0AwCiKBgBgFEUDADCKogEAGEXRAACMomgAAEb9P2s9wSuljMmBAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 411.875x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 411.875x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 411.875x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 411.875x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Future Engineering**"
      ],
      "metadata": {
        "id": "Y2sadeggtaix"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Name to Title"
      ],
      "metadata": {
        "id": "8x2k3QKvmPhF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# extract data from string\n",
        "df['Title'] = df['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
        "df['Title'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TwCzZzqomWCB",
        "outputId": "a15c56f7-ee19-4640-b372-42fe8fb84480"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr          757\n",
              "Miss        260\n",
              "Mrs         197\n",
              "Master       61\n",
              "Rev           8\n",
              "Dr            8\n",
              "Col           4\n",
              "Mlle          2\n",
              "Major         2\n",
              "Ms            2\n",
              "Lady          1\n",
              "Sir           1\n",
              "Mme           1\n",
              "Don           1\n",
              "Capt          1\n",
              "Countess      1\n",
              "Jonkheer      1\n",
              "Dona          1\n",
              "Name: Title, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Title = df.Title.replace([\"Mlle\",\"Ms\"],\"Miss\")\n",
        "df.Title = df.Title.replace([\"Lady\",\"Mme\",\"Countess\",\"Dona\"],\"Mrs\")\n",
        "df.Title = df.Title.replace([\"Sir\",\"Jonkheer\",\"Don\",\"Major\",\"Col\",\"Capt\"],\"Mr\")"
      ],
      "metadata": {
        "id": "w3UgLtfcbvZv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.Title.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzxuIQ0leCoI",
        "outputId": "d69f3e99-9d05-41e6-aa73-9de0d24b3e7e"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Mr        767\n",
              "Miss      264\n",
              "Mrs       201\n",
              "Master     61\n",
              "Rev         8\n",
              "Dr          8\n",
              "Name: Title, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Family size"
      ],
      "metadata": {
        "id": "R4p5j1XsnzUz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#defining FamilySize Sibling + Parent + 1(passenger), value count\n",
        "df['FamilySize'] = df['SibSp'] + df['Parch'] + 1\n",
        "df.FamilySize.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JwKPx6fjn0sj",
        "outputId": "c54af245-3ce2-4e8d-bf44-b57b95bf5010"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     790\n",
              "2     235\n",
              "3     159\n",
              "4      43\n",
              "6      25\n",
              "5      22\n",
              "7      16\n",
              "11     11\n",
              "8       8\n",
              "Name: FamilySize, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Single'] = df['FamilySize'] == 1\n",
        "df['SmallFamily'] = df['FamilySize'] >= 2\n",
        "df['MediumFamily'] = df['FamilySize'] >= 4\n",
        "df['LargeFamily'] = df['FamilySize']  >= 8"
      ],
      "metadata": {
        "id": "pxKAuqdeoFo0"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Single'] = df['Single'].astype(int)\n",
        "df['SmallFamily'] = df['SmallFamily'].astype(int)\n",
        "df['MediumFamily'] = df['MediumFamily'].astype(int)\n",
        "df['LargeFamily'] = df['LargeFamily'].astype(int)"
      ],
      "metadata": {
        "id": "AfzmzBvf1-ZE"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Age Categories"
      ],
      "metadata": {
        "id": "r7pS8ulRatRM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 922
        },
        "id": "6oN3z02Oav12",
        "outputId": "d14a6e74-9477-4a7c-872a-53802ad098f1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PassengerId  Survived  Pclass  \\\n",
              "0              1       0.0       3   \n",
              "1              2       1.0       1   \n",
              "2              3       1.0       3   \n",
              "3              4       1.0       1   \n",
              "4              5       0.0       3   \n",
              "..           ...       ...     ...   \n",
              "413         1305       NaN       3   \n",
              "414         1306       NaN       1   \n",
              "415         1307       NaN       3   \n",
              "416         1308       NaN       3   \n",
              "417         1309       NaN       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",
              "413                                 Spector, Mr. Woolf    male   NaN      0   \n",
              "414                       Oliva y Ocana, Dona. Fermina  female  39.0      0   \n",
              "415                       Saether, Mr. Simon Sivertsen    male  38.5      0   \n",
              "416                                Ware, Mr. Frederick    male   NaN      0   \n",
              "417                           Peter, Master. Michael J    male   NaN      1   \n",
              "\n",
              "     Parch              Ticket      Fare Cabin Embarked   Title  FamilySize  \\\n",
              "0        0           A/5 21171    7.2500   NaN        S      Mr           2   \n",
              "1        0            PC 17599   71.2833   C85        C     Mrs           2   \n",
              "2        0    STON/O2. 3101282    7.9250   NaN        S    Miss           1   \n",
              "3        0              113803   53.1000  C123        S     Mrs           2   \n",
              "4        0              373450    8.0500   NaN        S      Mr           1   \n",
              "..     ...                 ...       ...   ...      ...     ...         ...   \n",
              "413      0           A.5. 3236    8.0500   NaN        S      Mr           1   \n",
              "414      0            PC 17758  108.9000  C105        C     Mrs           1   \n",
              "415      0  SOTON/O.Q. 3101262    7.2500   NaN        S      Mr           1   \n",
              "416      0              359309    8.0500   NaN        S      Mr           1   \n",
              "417      1                2668   22.3583   NaN        C  Master           3   \n",
              "\n",
              "     Single  SmallFamily  MediumFamily  LargeFamily  \n",
              "0         0            1             0            0  \n",
              "1         0            1             0            0  \n",
              "2         1            0             0            0  \n",
              "3         0            1             0            0  \n",
              "4         1            0             0            0  \n",
              "..      ...          ...           ...          ...  \n",
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              "<p>1309 rows × 18 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7c12912f-3d27-4e89-9050-1cecd275bffe')\"\n",
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              "\n",
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              "          const element = document.querySelector('#df-7c12912f-3d27-4e89-9050-1cecd275bffe');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "\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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Infant'] = df['Age'] <= 2\n",
        "df['Kid'] = df['Age'] <= 12\n",
        "df['Teen'] = df['Age'] <= 19\n",
        "df['Adult'] = df['Age'] <= 64\n",
        "df['Senior'] = df['Age'] >= 65"
      ],
      "metadata": {
        "id": "3xLRzUxheQeC"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Infant'] = df['Infant'].astype(int)\n",
        "df['Kid'] = df['Kid'].astype(int)\n",
        "df['Teen'] = df['Teen'].astype(int)\n",
        "df['Adult'] = df['Adult'].astype(int)\n",
        "df['Senior'] = df['Senior'].astype(int)"
      ],
      "metadata": {
        "id": "_vnp8H81qHvs"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "id": "y0nP9berf8EL",
        "outputId": "7e473a10-6b02-4ef1-9899-b8bf60a0c5d4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   PassengerId  Survived  Pclass  \\\n",
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              "3      0            113803  53.1000  ...          2      0           1   \n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1.0</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>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1.0</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>...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.0</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>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21b0c458-f21e-4316-8aa5-b64d8fb8eba2')\"\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-21b0c458-f21e-4316-8aa5-b64d8fb8eba2 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-21b0c458-f21e-4316-8aa5-b64d8fb8eba2');\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": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Missing Value Imputation"
      ],
      "metadata": {
        "id": "7yLhQhdkRDr-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdPOZA0vqIN0",
        "outputId": "6f07918f-eb2b-44fa-fad1-263443a35bd6"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassengerId        0\n",
              "Survived         418\n",
              "Pclass             0\n",
              "Name               0\n",
              "Sex                0\n",
              "Age              263\n",
              "SibSp              0\n",
              "Parch              0\n",
              "Ticket             0\n",
              "Fare               1\n",
              "Cabin           1014\n",
              "Embarked           2\n",
              "Title              0\n",
              "FamilySize         0\n",
              "Single             0\n",
              "SmallFamily        0\n",
              "MediumFamily       0\n",
              "LargeFamily        0\n",
              "Infant             0\n",
              "Kid                0\n",
              "Teen               0\n",
              "Adult              0\n",
              "Senior             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df['Age']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "ejaTqO-sR4ak",
        "outputId": "6162b1b7-607d-41c7-ae96-c017b7b7bae2"
      },
      "execution_count": 32,
      "outputs": [
        {
          "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.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qycU7vSVR-4p",
        "outputId": "f1078239-ad8d-4d7b-8daa-1124dde8ad1d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "29.881137667304014"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Age.median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zByAWJZaR-H1",
        "outputId": "b252cd19-5787-4392-9cea-f1c5a603f086"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.0"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fill according Title mean in Age\n",
        "df.Age.fillna(df.groupby('Title')['Age'].transform('median'), inplace=True)"
      ],
      "metadata": {
        "id": "Yab6WRsaRX_R"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x=df['Fare']);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "lc4RxOVyRv-G",
        "outputId": "9ca949f1-48f3-4368-91cd-c04840f326e0"
      },
      "execution_count": 36,
      "outputs": [
        {
          "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.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-HneKKu-Txr1",
        "outputId": "d94649cc-bf8a-43ba-8699-7cf167eb9647"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "33.29547928134557"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Fare.median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0UD_RVKTzxL",
        "outputId": "bb059108-5669-42bd-94aa-525c8309f2b3"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.4542"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Fare = df.Fare.fillna(df.Fare.mean())"
      ],
      "metadata": {
        "id": "rzJEapBMT1Fs"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(data=df_train, x=\"Embarked\");"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "6uoAgaaDUekx",
        "outputId": "8032c239-4322-4859-ba04-905617553db5"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Embarked = df.Embarked.fillna('S')"
      ],
      "metadata": {
        "id": "mn8kO9-PVXRX"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n_-Iuki629zA",
        "outputId": "5dbe3ee8-11af-4ebb-c192-52e4b96c628f"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1309 entries, 0 to 417\n",
            "Data columns (total 23 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   PassengerId   1309 non-null   int64  \n",
            " 1   Survived      891 non-null    float64\n",
            " 2   Pclass        1309 non-null   int64  \n",
            " 3   Name          1309 non-null   object \n",
            " 4   Sex           1309 non-null   object \n",
            " 5   Age           1309 non-null   float64\n",
            " 6   SibSp         1309 non-null   int64  \n",
            " 7   Parch         1309 non-null   int64  \n",
            " 8   Ticket        1309 non-null   object \n",
            " 9   Fare          1309 non-null   float64\n",
            " 10  Cabin         295 non-null    object \n",
            " 11  Embarked      1309 non-null   object \n",
            " 12  Title         1309 non-null   object \n",
            " 13  FamilySize    1309 non-null   int64  \n",
            " 14  Single        1309 non-null   int64  \n",
            " 15  SmallFamily   1309 non-null   int64  \n",
            " 16  MediumFamily  1309 non-null   int64  \n",
            " 17  LargeFamily   1309 non-null   int64  \n",
            " 18  Infant        1309 non-null   int64  \n",
            " 19  Kid           1309 non-null   int64  \n",
            " 20  Teen          1309 non-null   int64  \n",
            " 21  Adult         1309 non-null   int64  \n",
            " 22  Senior        1309 non-null   int64  \n",
            "dtypes: float64(3), int64(14), object(6)\n",
            "memory usage: 245.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del df['PassengerId']\n",
        "del df['Name']\n",
        "del df['Ticket']\n",
        "del df['Cabin']"
      ],
      "metadata": {
        "id": "_rJjkhaVWP_M"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.get_dummies(df,drop_first=True)"
      ],
      "metadata": {
        "id": "61ssb-c54Y-S"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQov5tiJ8tA7",
        "outputId": "4df4a3ca-6b6b-44ee-ae87-29aab32c4bba"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 1309 entries, 0 to 417\n",
            "Data columns (total 24 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   Survived      891 non-null    float64\n",
            " 1   Pclass        1309 non-null   int64  \n",
            " 2   Age           1309 non-null   float64\n",
            " 3   SibSp         1309 non-null   int64  \n",
            " 4   Parch         1309 non-null   int64  \n",
            " 5   Fare          1309 non-null   float64\n",
            " 6   FamilySize    1309 non-null   int64  \n",
            " 7   Single        1309 non-null   int64  \n",
            " 8   SmallFamily   1309 non-null   int64  \n",
            " 9   MediumFamily  1309 non-null   int64  \n",
            " 10  LargeFamily   1309 non-null   int64  \n",
            " 11  Infant        1309 non-null   int64  \n",
            " 12  Kid           1309 non-null   int64  \n",
            " 13  Teen          1309 non-null   int64  \n",
            " 14  Adult         1309 non-null   int64  \n",
            " 15  Senior        1309 non-null   int64  \n",
            " 16  Sex_male      1309 non-null   uint8  \n",
            " 17  Embarked_Q    1309 non-null   uint8  \n",
            " 18  Embarked_S    1309 non-null   uint8  \n",
            " 19  Title_Master  1309 non-null   uint8  \n",
            " 20  Title_Miss    1309 non-null   uint8  \n",
            " 21  Title_Mr      1309 non-null   uint8  \n",
            " 22  Title_Mrs     1309 non-null   uint8  \n",
            " 23  Title_Rev     1309 non-null   uint8  \n",
            "dtypes: float64(3), int64(13), uint8(8)\n",
            "memory usage: 184.1 KB\n"
          ]
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        "data.columns"
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        "outputId": "b8e0958e-8735-4ab0-c551-d57a1e2ae96c"
      },
      "execution_count": 48,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "Index(['Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare', 'FamilySize',\n",
              "       'Single', 'SmallFamily', 'MediumFamily', 'LargeFamily', 'Infant', 'Kid',\n",
              "       'Teen', 'Adult', 'Senior', 'Sex_male', 'Embarked_Q', 'Embarked_S',\n",
              "       'Title_Master', 'Title_Miss', 'Title_Mr', 'Title_Mrs', 'Title_Rev'],\n",
              "      dtype='object')"
            ]
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          "execution_count": 48
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        "data.sample(5)"
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        "id": "tQCPD2b-49xE",
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      "execution_count": 50,
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              "     Survived  Pclass   Age  SibSp  Parch     Fare  FamilySize  Single  \\\n",
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          "metadata": {},
          "execution_count": 50
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    {
      "cell_type": "markdown",
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        "**Modelling**"
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        "id": "ryzjdtSO5cRk"
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        "test = data[891:]"
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      "outputs": []
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        "train.shape"
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        "id": "fSATpbMm5rWE",
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      "execution_count": 53,
      "outputs": [
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          "metadata": {},
          "execution_count": 53
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        "outputId": "61382c12-f082-4fde-a0a8-61cdd3e5f484"
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      "execution_count": 54,
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          "output_type": "execute_result",
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              "      <td>3</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 24 columns</p>\n",
              "</div>\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4b01508c-6b19-438e-81f8-1a56f44c7bd5');\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",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "del train['Survived']\n",
        "del test['Survived']"
      ],
      "metadata": {
        "id": "gE9dt7y65x3b"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y = df_train['Survived']"
      ],
      "metadata": {
        "id": "kRq4n4Tx6WBd"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "classification_(train,y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "s_zQ1qIj6v7U",
        "outputId": "b1b33abb-f4e1-4b75-a199-f1a30d157508"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  AccuracyScore  PrecisionScore  RecallScore  f1_Score\n",
              "RandomForest           0.983165        0.988060     0.967836  0.977843\n",
              "DecisionTree           0.983165        0.993958     0.961988  0.977712\n",
              "GradientBoosting       0.902357        0.920792     0.815789  0.865116\n",
              "AdaBoost               0.839506        0.797015     0.780702  0.788774\n",
              "LogisticRegr           0.831650        0.801887     0.745614  0.772727\n",
              "KNeigbors              0.822671        0.794872     0.725146  0.758410\n",
              "Bernoulli              0.794613        0.723944     0.751462  0.737446\n",
              "Gaussian               0.419753        0.397900     0.997076  0.568807\n",
              "Multinominal           0.703704        0.653543     0.485380  0.557047\n",
              "SVC                    0.687991        0.716216     0.309942  0.432653"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-02f73fd1-57b5-4d7b-8c3d-7d7272d6745e\">\n",
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              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AccuracyScore</th>\n",
              "      <th>PrecisionScore</th>\n",
              "      <th>RecallScore</th>\n",
              "      <th>f1_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RandomForest</th>\n",
              "      <td>0.983165</td>\n",
              "      <td>0.988060</td>\n",
              "      <td>0.967836</td>\n",
              "      <td>0.977843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTree</th>\n",
              "      <td>0.983165</td>\n",
              "      <td>0.993958</td>\n",
              "      <td>0.961988</td>\n",
              "      <td>0.977712</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoosting</th>\n",
              "      <td>0.902357</td>\n",
              "      <td>0.920792</td>\n",
              "      <td>0.815789</td>\n",
              "      <td>0.865116</td>\n",
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              "    <tr>\n",
              "      <th>AdaBoost</th>\n",
              "      <td>0.839506</td>\n",
              "      <td>0.797015</td>\n",
              "      <td>0.780702</td>\n",
              "      <td>0.788774</td>\n",
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              "    <tr>\n",
              "      <th>LogisticRegr</th>\n",
              "      <td>0.831650</td>\n",
              "      <td>0.801887</td>\n",
              "      <td>0.745614</td>\n",
              "      <td>0.772727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeigbors</th>\n",
              "      <td>0.822671</td>\n",
              "      <td>0.794872</td>\n",
              "      <td>0.725146</td>\n",
              "      <td>0.758410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Bernoulli</th>\n",
              "      <td>0.794613</td>\n",
              "      <td>0.723944</td>\n",
              "      <td>0.751462</td>\n",
              "      <td>0.737446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Gaussian</th>\n",
              "      <td>0.419753</td>\n",
              "      <td>0.397900</td>\n",
              "      <td>0.997076</td>\n",
              "      <td>0.568807</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Multinominal</th>\n",
              "      <td>0.703704</td>\n",
              "      <td>0.653543</td>\n",
              "      <td>0.485380</td>\n",
              "      <td>0.557047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.687991</td>\n",
              "      <td>0.716216</td>\n",
              "      <td>0.309942</td>\n",
              "      <td>0.432653</td>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-02f73fd1-57b5-4d7b-8c3d-7d7272d6745e 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-02f73fd1-57b5-4d7b-8c3d-7d7272d6745e');\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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d=DecisionTreeClassifier()\n",
        "predict=d.fit(train,y).predict(test)"
      ],
      "metadata": {
        "id": "dUpru3q463nk"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bEHVCysB7X2M",
        "outputId": "f4febc8d-26a3-44b1-f51a-a136417fb257"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,\n",
              "       1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1,\n",
              "       1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1,\n",
              "       1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0,\n",
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            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model=Sequential()\n",
        "\n",
        "model.add(Dense(20,activation='relu'))\n",
        "model.add(Dense(15,activation='relu'))\n",
        "model.add(Dense(13,activation='relu'))\n",
        "model.add(Dense(10,activation='relu'))\n",
        "model.add(Dense(8,activation='relu'))\n",
        "model.add(Dense(4,activation='relu'))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer=\"Adam\",metrics=[\"accuracy\"])\n",
        "\n",
        "model.fit(train,y, epochs=500,batch_size=3,verbose=1)\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "oeCE4vQI7cey",
        "outputId": "2b845e63-a3cd-4914-d0bd-5676190f5da3"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/500\n",
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            "Epoch 2/500\n",
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            "Epoch 3/500\n",
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            "Epoch 6/500\n",
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            "Epoch 7/500\n",
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            "Epoch 8/500\n",
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            "Epoch 9/500\n",
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            "Epoch 10/500\n",
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            "Epoch 11/500\n",
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            "Epoch 12/500\n",
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            "Epoch 15/500\n",
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            "Epoch 16/500\n",
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            "Epoch 21/500\n",
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            "Epoch 22/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.4161 - accuracy: 0.8171\n",
            "Epoch 23/500\n",
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            "Epoch 25/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.4098 - accuracy: 0.8204\n",
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            "Epoch 27/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.4127 - accuracy: 0.8215\n",
            "Epoch 28/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.4015 - accuracy: 0.8328\n",
            "Epoch 29/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.4066 - accuracy: 0.8272\n",
            "Epoch 30/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.4053 - accuracy: 0.8238\n",
            "Epoch 31/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3976 - accuracy: 0.8350\n",
            "Epoch 32/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.4112 - accuracy: 0.8193\n",
            "Epoch 33/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.4087 - accuracy: 0.8429\n",
            "Epoch 34/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3944 - accuracy: 0.8384\n",
            "Epoch 35/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3922 - accuracy: 0.8350\n",
            "Epoch 36/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3908 - accuracy: 0.8305\n",
            "Epoch 37/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3967 - accuracy: 0.8350\n",
            "Epoch 38/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3959 - accuracy: 0.8283\n",
            "Epoch 39/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3943 - accuracy: 0.8316\n",
            "Epoch 40/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3925 - accuracy: 0.8373\n",
            "Epoch 41/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3898 - accuracy: 0.8418\n",
            "Epoch 42/500\n",
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            "Epoch 43/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3834 - accuracy: 0.8418\n",
            "Epoch 44/500\n",
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            "Epoch 45/500\n",
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            "Epoch 46/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.4000 - accuracy: 0.8395\n",
            "Epoch 47/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3756 - accuracy: 0.8406\n",
            "Epoch 48/500\n",
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            "Epoch 49/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3862 - accuracy: 0.8395\n",
            "Epoch 50/500\n",
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            "Epoch 51/500\n",
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            "Epoch 52/500\n",
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            "Epoch 53/500\n",
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            "Epoch 55/500\n",
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            "Epoch 56/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3777 - accuracy: 0.8429\n",
            "Epoch 57/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3706 - accuracy: 0.8474\n",
            "Epoch 58/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3798 - accuracy: 0.8451\n",
            "Epoch 59/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3672 - accuracy: 0.8496\n",
            "Epoch 60/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3700 - accuracy: 0.8541\n",
            "Epoch 61/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3708 - accuracy: 0.8440\n",
            "Epoch 62/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3700 - accuracy: 0.8440\n",
            "Epoch 63/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3614 - accuracy: 0.8563\n",
            "Epoch 64/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3634 - accuracy: 0.8440\n",
            "Epoch 65/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3689 - accuracy: 0.8462\n",
            "Epoch 66/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3617 - accuracy: 0.8541\n",
            "Epoch 67/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3624 - accuracy: 0.8563\n",
            "Epoch 68/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3630 - accuracy: 0.8485\n",
            "Epoch 69/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3669 - accuracy: 0.8474\n",
            "Epoch 70/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3635 - accuracy: 0.8451\n",
            "Epoch 71/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3845 - accuracy: 0.8384\n",
            "Epoch 72/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3550 - accuracy: 0.8541\n",
            "Epoch 73/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3649 - accuracy: 0.8451\n",
            "Epoch 74/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3789 - accuracy: 0.8418\n",
            "Epoch 75/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3553 - accuracy: 0.8563\n",
            "Epoch 76/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3526 - accuracy: 0.8530\n",
            "Epoch 77/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3637 - accuracy: 0.8519\n",
            "Epoch 78/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3679 - accuracy: 0.8485\n",
            "Epoch 79/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3703 - accuracy: 0.8496\n",
            "Epoch 80/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3653 - accuracy: 0.8440\n",
            "Epoch 81/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3520 - accuracy: 0.8496\n",
            "Epoch 82/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3522 - accuracy: 0.8541\n",
            "Epoch 83/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3572 - accuracy: 0.8552\n",
            "Epoch 84/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3519 - accuracy: 0.8552\n",
            "Epoch 85/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3514 - accuracy: 0.8597\n",
            "Epoch 86/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3607 - accuracy: 0.8496\n",
            "Epoch 87/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3555 - accuracy: 0.8485\n",
            "Epoch 88/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3500 - accuracy: 0.8586\n",
            "Epoch 89/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3448 - accuracy: 0.8563\n",
            "Epoch 90/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3454 - accuracy: 0.8631\n",
            "Epoch 91/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3425 - accuracy: 0.8575\n",
            "Epoch 92/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3554 - accuracy: 0.8507\n",
            "Epoch 93/500\n",
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            "Epoch 94/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3760 - accuracy: 0.8395\n",
            "Epoch 95/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3505 - accuracy: 0.8586\n",
            "Epoch 96/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3373 - accuracy: 0.8597\n",
            "Epoch 97/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3403 - accuracy: 0.8586\n",
            "Epoch 98/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3519 - accuracy: 0.8586\n",
            "Epoch 99/500\n",
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            "Epoch 100/500\n",
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            "Epoch 101/500\n",
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            "Epoch 102/500\n",
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            "Epoch 103/500\n",
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            "Epoch 104/500\n",
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            "Epoch 105/500\n",
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            "Epoch 106/500\n",
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            "Epoch 107/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3444 - accuracy: 0.8597\n",
            "Epoch 108/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3398 - accuracy: 0.8507\n",
            "Epoch 109/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3480 - accuracy: 0.8608\n",
            "Epoch 110/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3413 - accuracy: 0.8552\n",
            "Epoch 111/500\n",
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            "Epoch 112/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3655 - accuracy: 0.8496\n",
            "Epoch 113/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3459 - accuracy: 0.8530\n",
            "Epoch 114/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3381 - accuracy: 0.8575\n",
            "Epoch 115/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3441 - accuracy: 0.8620\n",
            "Epoch 116/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3396 - accuracy: 0.8597\n",
            "Epoch 117/500\n",
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            "Epoch 118/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3365 - accuracy: 0.8608\n",
            "Epoch 119/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3405 - accuracy: 0.8507\n",
            "Epoch 120/500\n",
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            "Epoch 121/500\n",
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            "Epoch 122/500\n",
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            "Epoch 123/500\n",
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            "Epoch 124/500\n",
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            "Epoch 125/500\n",
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            "Epoch 126/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3346 - accuracy: 0.8620\n",
            "Epoch 127/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3328 - accuracy: 0.8597\n",
            "Epoch 128/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3490 - accuracy: 0.8507\n",
            "Epoch 129/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3374 - accuracy: 0.8586\n",
            "Epoch 130/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3381 - accuracy: 0.8676\n",
            "Epoch 131/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3253 - accuracy: 0.8608\n",
            "Epoch 132/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3283 - accuracy: 0.8664\n",
            "Epoch 133/500\n",
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            "Epoch 134/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3475 - accuracy: 0.8586\n",
            "Epoch 135/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3377 - accuracy: 0.8507\n",
            "Epoch 136/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3252 - accuracy: 0.8597\n",
            "Epoch 137/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3608 - accuracy: 0.8530\n",
            "Epoch 138/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3339 - accuracy: 0.8597\n",
            "Epoch 139/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3328 - accuracy: 0.8620\n",
            "Epoch 140/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3239 - accuracy: 0.8620\n",
            "Epoch 141/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3294 - accuracy: 0.8631\n",
            "Epoch 142/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3476 - accuracy: 0.8608\n",
            "Epoch 143/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3343 - accuracy: 0.8642\n",
            "Epoch 144/500\n",
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            "Epoch 145/500\n",
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            "Epoch 146/500\n",
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            "Epoch 147/500\n",
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            "Epoch 148/500\n",
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            "Epoch 149/500\n",
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            "Epoch 150/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3221 - accuracy: 0.8698\n",
            "Epoch 151/500\n",
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            "Epoch 153/500\n",
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            "Epoch 154/500\n",
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            "Epoch 155/500\n",
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            "Epoch 156/500\n",
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            "Epoch 157/500\n",
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            "Epoch 158/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3220 - accuracy: 0.8642\n",
            "Epoch 159/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3297 - accuracy: 0.8620\n",
            "Epoch 160/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3185 - accuracy: 0.8709\n",
            "Epoch 161/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3145 - accuracy: 0.8687\n",
            "Epoch 162/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3431 - accuracy: 0.8485\n",
            "Epoch 163/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3273 - accuracy: 0.8586\n",
            "Epoch 164/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3301 - accuracy: 0.8608\n",
            "Epoch 165/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3140 - accuracy: 0.8664\n",
            "Epoch 166/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3212 - accuracy: 0.8676\n",
            "Epoch 167/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3217 - accuracy: 0.8698\n",
            "Epoch 168/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3184 - accuracy: 0.8653\n",
            "Epoch 169/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3226 - accuracy: 0.8552\n",
            "Epoch 170/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3248 - accuracy: 0.8631\n",
            "Epoch 171/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3125 - accuracy: 0.8676\n",
            "Epoch 172/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3139 - accuracy: 0.8687\n",
            "Epoch 173/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3214 - accuracy: 0.8597\n",
            "Epoch 174/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3081 - accuracy: 0.8698\n",
            "Epoch 175/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3256 - accuracy: 0.8575\n",
            "Epoch 176/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3217 - accuracy: 0.8608\n",
            "Epoch 177/500\n",
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            "297/297 [==============================] - 1s 3ms/step - loss: 0.2903 - accuracy: 0.8822\n",
            "Epoch 355/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2881 - accuracy: 0.8810\n",
            "Epoch 356/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2705 - accuracy: 0.8945\n",
            "Epoch 357/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2664 - accuracy: 0.8889\n",
            "Epoch 358/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2824 - accuracy: 0.8765\n",
            "Epoch 359/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2781 - accuracy: 0.8855\n",
            "Epoch 360/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2602 - accuracy: 0.8866\n",
            "Epoch 361/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2829 - accuracy: 0.8844\n",
            "Epoch 362/500\n",
            "297/297 [==============================] - 1s 2ms/step - loss: 0.3288 - accuracy: 0.8642\n",
            "Epoch 363/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2748 - accuracy: 0.8878\n",
            "Epoch 364/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2662 - accuracy: 0.8844\n",
            "Epoch 365/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2761 - accuracy: 0.8833\n",
            "Epoch 366/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2743 - accuracy: 0.8855\n",
            "Epoch 367/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2700 - accuracy: 0.8911\n",
            "Epoch 368/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2647 - accuracy: 0.8900\n",
            "Epoch 369/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2751 - accuracy: 0.8934\n",
            "Epoch 370/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2587 - accuracy: 0.8967\n",
            "Epoch 371/500\n",
            "297/297 [==============================] - 2s 5ms/step - loss: 0.2646 - accuracy: 0.8844\n",
            "Epoch 372/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3460 - accuracy: 0.8519\n",
            "Epoch 373/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2851 - accuracy: 0.8799\n",
            "Epoch 374/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2688 - accuracy: 0.8833\n",
            "Epoch 375/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2652 - accuracy: 0.8956\n",
            "Epoch 376/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2747 - accuracy: 0.8732\n",
            "Epoch 377/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2633 - accuracy: 0.8889\n",
            "Epoch 378/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2644 - accuracy: 0.8866\n",
            "Epoch 379/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2846 - accuracy: 0.8732\n",
            "Epoch 380/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2809 - accuracy: 0.8822\n",
            "Epoch 381/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2594 - accuracy: 0.8900\n",
            "Epoch 382/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2604 - accuracy: 0.8878\n",
            "Epoch 383/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2647 - accuracy: 0.8911\n",
            "Epoch 384/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2921 - accuracy: 0.8810\n",
            "Epoch 385/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2971 - accuracy: 0.8709\n",
            "Epoch 386/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2746 - accuracy: 0.8799\n",
            "Epoch 387/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2710 - accuracy: 0.8911\n",
            "Epoch 388/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2739 - accuracy: 0.8833\n",
            "Epoch 389/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2686 - accuracy: 0.8777\n",
            "Epoch 390/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2747 - accuracy: 0.8777\n",
            "Epoch 391/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2716 - accuracy: 0.8788\n",
            "Epoch 392/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2779 - accuracy: 0.8878\n",
            "Epoch 393/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2861 - accuracy: 0.8810\n",
            "Epoch 394/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2546 - accuracy: 0.8923\n",
            "Epoch 395/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2899 - accuracy: 0.8732\n",
            "Epoch 396/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2691 - accuracy: 0.8833\n",
            "Epoch 397/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2862 - accuracy: 0.8754\n",
            "Epoch 398/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2874 - accuracy: 0.8866\n",
            "Epoch 399/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2634 - accuracy: 0.8900\n",
            "Epoch 400/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2612 - accuracy: 0.8844\n",
            "Epoch 401/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2580 - accuracy: 0.8889\n",
            "Epoch 402/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2674 - accuracy: 0.8855\n",
            "Epoch 403/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2768 - accuracy: 0.8822\n",
            "Epoch 404/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2650 - accuracy: 0.8900\n",
            "Epoch 405/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2668 - accuracy: 0.8866\n",
            "Epoch 406/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2845 - accuracy: 0.8799\n",
            "Epoch 407/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2600 - accuracy: 0.8956\n",
            "Epoch 408/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2610 - accuracy: 0.8945\n",
            "Epoch 409/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3118 - accuracy: 0.8653\n",
            "Epoch 410/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2679 - accuracy: 0.8866\n",
            "Epoch 411/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2533 - accuracy: 0.9001\n",
            "Epoch 412/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2576 - accuracy: 0.8934\n",
            "Epoch 413/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2736 - accuracy: 0.8799\n",
            "Epoch 414/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2658 - accuracy: 0.8777\n",
            "Epoch 415/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2546 - accuracy: 0.8967\n",
            "Epoch 416/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2906 - accuracy: 0.8709\n",
            "Epoch 417/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2575 - accuracy: 0.8900\n",
            "Epoch 418/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2730 - accuracy: 0.8799\n",
            "Epoch 419/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2779 - accuracy: 0.8810\n",
            "Epoch 420/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2710 - accuracy: 0.8878\n",
            "Epoch 421/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2577 - accuracy: 0.8956\n",
            "Epoch 422/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2629 - accuracy: 0.8889\n",
            "Epoch 423/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2579 - accuracy: 0.8855\n",
            "Epoch 424/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2657 - accuracy: 0.8833\n",
            "Epoch 425/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2722 - accuracy: 0.8923\n",
            "Epoch 426/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2908 - accuracy: 0.8732\n",
            "Epoch 427/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2816 - accuracy: 0.8833\n",
            "Epoch 428/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2775 - accuracy: 0.8810\n",
            "Epoch 429/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2770 - accuracy: 0.8732\n",
            "Epoch 430/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2638 - accuracy: 0.8878\n",
            "Epoch 431/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2602 - accuracy: 0.8833\n",
            "Epoch 432/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2589 - accuracy: 0.8979\n",
            "Epoch 433/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2673 - accuracy: 0.8777\n",
            "Epoch 434/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2507 - accuracy: 0.8956\n",
            "Epoch 435/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2727 - accuracy: 0.8810\n",
            "Epoch 436/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2538 - accuracy: 0.8979\n",
            "Epoch 437/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2992 - accuracy: 0.8833\n",
            "Epoch 438/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2970 - accuracy: 0.8721\n",
            "Epoch 439/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2943 - accuracy: 0.8810\n",
            "Epoch 440/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2675 - accuracy: 0.8844\n",
            "Epoch 441/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2672 - accuracy: 0.8866\n",
            "Epoch 442/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2535 - accuracy: 0.8934\n",
            "Epoch 443/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2686 - accuracy: 0.8833\n",
            "Epoch 444/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2805 - accuracy: 0.8866\n",
            "Epoch 445/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2720 - accuracy: 0.8900\n",
            "Epoch 446/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2620 - accuracy: 0.8855\n",
            "Epoch 447/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2721 - accuracy: 0.8900\n",
            "Epoch 448/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2696 - accuracy: 0.8833\n",
            "Epoch 449/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2579 - accuracy: 0.8911\n",
            "Epoch 450/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2862 - accuracy: 0.8844\n",
            "Epoch 451/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2625 - accuracy: 0.8900\n",
            "Epoch 452/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2585 - accuracy: 0.8866\n",
            "Epoch 453/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2517 - accuracy: 0.8956\n",
            "Epoch 454/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2650 - accuracy: 0.8911\n",
            "Epoch 455/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2655 - accuracy: 0.8878\n",
            "Epoch 456/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2725 - accuracy: 0.8822\n",
            "Epoch 457/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2703 - accuracy: 0.8844\n",
            "Epoch 458/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2620 - accuracy: 0.8822\n",
            "Epoch 459/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2681 - accuracy: 0.8833\n",
            "Epoch 460/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2863 - accuracy: 0.8889\n",
            "Epoch 461/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2856 - accuracy: 0.8732\n",
            "Epoch 462/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2629 - accuracy: 0.8889\n",
            "Epoch 463/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2604 - accuracy: 0.8945\n",
            "Epoch 464/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2579 - accuracy: 0.8810\n",
            "Epoch 465/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2652 - accuracy: 0.8878\n",
            "Epoch 466/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2527 - accuracy: 0.8900\n",
            "Epoch 467/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2787 - accuracy: 0.8833\n",
            "Epoch 468/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2931 - accuracy: 0.8754\n",
            "Epoch 469/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2889 - accuracy: 0.8777\n",
            "Epoch 470/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2793 - accuracy: 0.8777\n",
            "Epoch 471/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2677 - accuracy: 0.8889\n",
            "Epoch 472/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2534 - accuracy: 0.8866\n",
            "Epoch 473/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2593 - accuracy: 0.8866\n",
            "Epoch 474/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.3110 - accuracy: 0.8765\n",
            "Epoch 475/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2686 - accuracy: 0.8799\n",
            "Epoch 476/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2510 - accuracy: 0.8979\n",
            "Epoch 477/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2478 - accuracy: 0.8956\n",
            "Epoch 478/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2584 - accuracy: 0.8923\n",
            "Epoch 479/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2516 - accuracy: 0.8934\n",
            "Epoch 480/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2472 - accuracy: 0.8990\n",
            "Epoch 481/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2401 - accuracy: 0.9012\n",
            "Epoch 482/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2912 - accuracy: 0.8799\n",
            "Epoch 483/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2649 - accuracy: 0.8945\n",
            "Epoch 484/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2519 - accuracy: 0.8956\n",
            "Epoch 485/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2591 - accuracy: 0.8889\n",
            "Epoch 486/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2673 - accuracy: 0.8833\n",
            "Epoch 487/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2601 - accuracy: 0.8911\n",
            "Epoch 488/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2826 - accuracy: 0.8788\n",
            "Epoch 489/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2547 - accuracy: 0.8911\n",
            "Epoch 490/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2789 - accuracy: 0.8855\n",
            "Epoch 491/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2852 - accuracy: 0.8687\n",
            "Epoch 492/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2778 - accuracy: 0.8878\n",
            "Epoch 493/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2491 - accuracy: 0.8990\n",
            "Epoch 494/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2523 - accuracy: 0.8866\n",
            "Epoch 495/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2629 - accuracy: 0.8799\n",
            "Epoch 496/500\n",
            "297/297 [==============================] - 2s 5ms/step - loss: 0.2517 - accuracy: 0.8956\n",
            "Epoch 497/500\n",
            "297/297 [==============================] - 2s 5ms/step - loss: 0.2687 - accuracy: 0.8844\n",
            "Epoch 498/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2845 - accuracy: 0.8889\n",
            "Epoch 499/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2567 - accuracy: 0.8945\n",
            "Epoch 500/500\n",
            "297/297 [==============================] - 1s 3ms/step - loss: 0.2810 - accuracy: 0.8855\n",
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_56 (Dense)            (3, 20)                   480       \n",
            "                                                                 \n",
            " dense_57 (Dense)            (3, 15)                   315       \n",
            "                                                                 \n",
            " dense_58 (Dense)            (3, 13)                   208       \n",
            "                                                                 \n",
            " dense_59 (Dense)            (3, 10)                   140       \n",
            "                                                                 \n",
            " dense_60 (Dense)            (3, 8)                    88        \n",
            "                                                                 \n",
            " dense_61 (Dense)            (3, 4)                    36        \n",
            "                                                                 \n",
            " dense_62 (Dense)            (3, 1)                    5         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,272\n",
            "Trainable params: 1,272\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "_, accuracy=model.evaluate(train,y)\n",
        "print('Accuracy: %.2f' % (accuracy * 100))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d3P6IIJq98je",
        "outputId": "fc80e367-2b7b-49fe-caa1-9e5836cbe8e4"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "28/28 [==============================] - 0s 2ms/step - loss: 0.2375 - accuracy: 0.8956\n",
            "Accuracy: 89.56\n"
          ]
        }
      ]
    }
  ]
}