Titanic_survival.ipynb

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      "name": "Titanic-survival.ipynb",
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      "source": [
        "import numpy as np\n",
        "import pandas as pd \n",
        "import plotly as plt\n",
        "from sklearn import preprocessing\n",
        "import seaborn as sns\n",
        "from sklearn.cluster import KMeans, AgglomerativeClustering\n",
        "from sklearn.metrics import mean_squared_error"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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        "id": "QoAQM0dEsOX-"
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      "source": [
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Kaggle/Titanic/train.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "df.head()"
      ],
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              "      <th>PassengerId</th>\n",
              "      <th>Survived</th>\n",
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            "text/plain": [
              "   PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked\n",
              "0            1         0       3  ...   7.2500   NaN         S\n",
              "1            2         1       1  ...  71.2833   C85         C\n",
              "2            3         1       3  ...   7.9250   NaN         S\n",
              "3            4         1       1  ...  53.1000  C123         S\n",
              "4            5         0       3  ...   8.0500   NaN         S\n",
              "\n",
              "[5 rows x 12 columns]"
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        "df.isna().sum()"
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          "data": {
            "text/plain": [
              "PassengerId      0\n",
              "Survived         0\n",
              "Pclass           0\n",
              "Name             0\n",
              "Sex              0\n",
              "Age            177\n",
              "SibSp            0\n",
              "Parch            0\n",
              "Ticket           0\n",
              "Fare             0\n",
              "Cabin          687\n",
              "Embarked         2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 261
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Embarked'] = np.where(df['Embarked'].isna(),df['Embarked'].mode(),df['Embarked'])"
      ],
      "metadata": {
        "id": "TpyE9iSzbFpW"
      },
      "execution_count": null,
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    },
    {
      "cell_type": "code",
      "source": [
        "df.groupby(['Pclass']).count()"
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            "text/plain": [
              "        PassengerId  Survived  Name  Sex  ...  Ticket  Fare  Cabin  Embarked\n",
              "Pclass                                    ...                               \n",
              "1               216       216   216  216  ...     216   216    176       216\n",
              "2               184       184   184  184  ...     184   184     16       184\n",
              "3               491       491   491  491  ...     491   491     12       491\n",
              "\n",
              "[3 rows x 11 columns]"
            ]
          },
          "metadata": {},
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        }
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        "df.drop(columns=['Age']).dropna().groupby(['Pclass']).count()"
      ],
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      "outputs": [
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              "      <th>Survived</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
              "      <th>Embarked</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "      <td>176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-94162edb-3743-4a9f-8caa-9f119d295a55')\"\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-94162edb-3743-4a9f-8caa-9f119d295a55 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-94162edb-3743-4a9f-8caa-9f119d295a55');\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",
              "  "
            ],
            "text/plain": [
              "        PassengerId  Survived  Name  Sex  ...  Ticket  Fare  Cabin  Embarked\n",
              "Pclass                                    ...                               \n",
              "1               176       176   176  176  ...     176   176    176       176\n",
              "2                16        16    16   16  ...      16    16     16        16\n",
              "3                12        12    12   12  ...      12    12     12        12\n",
              "\n",
              "[3 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 274
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df['Cabin'] = np.where(df['Cabin'].isna(),'X',df['Cabin'])"
      ],
      "metadata": {
        "id": "vTHQ_0Fcnsrn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df['Cabin'] = df['Cabin'].apply(lambda x: x[0:1])"
      ],
      "metadata": {
        "id": "iytwjLYQ2VTQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Alone'] = (df['SibSp'] + df['Parch']) >0"
      ],
      "metadata": {
        "id": "FzBZBm4T2VV-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['Parch', 'SibSp', 'Name','Cabin'],axis = 1)"
      ],
      "metadata": {
        "id": "3_K5XoGv2VYf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ticket_prefix'] = df['Ticket'].str.split()"
      ],
      "metadata": {
        "id": "t_2nTHhpM7uW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_prefix(arr):\n",
        "  if len(arr) > 1 :\n",
        "    p = arr[0].replace('.','')\n",
        "    p = p.replace('/','')\n",
        "  else:\n",
        "    p = 'X'\n",
        "  return p\n",
        "def get_number(arr):\n",
        "  return arr[-1]"
      ],
      "metadata": {
        "id": "jxq600H2hD6T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df['Ticket_number'] = df.Ticket_number.apply(get_number)"
      ],
      "metadata": {
        "id": "t7OL7YsKfM9s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ticket_prefix'] = df.Ticket_prefix.apply(get_prefix)"
      ],
      "metadata": {
        "id": "z7KjXFs0hsuc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#\n",
        "df['Ticket_prefix'].unique"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OPaaabvHNC3s",
        "outputId": "a802e100-5489-4f6a-9960-c679fb1839d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Series.unique of 0          A5\n",
              "1          PC\n",
              "2      STONO2\n",
              "3           X\n",
              "4           X\n",
              "        ...  \n",
              "886         X\n",
              "887         X\n",
              "888        WC\n",
              "889         X\n",
              "890         X\n",
              "Name: Ticket_prefix, Length: 891, dtype: object>"
            ]
          },
          "metadata": {},
          "execution_count": 295
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop(['Ticket'], axis = 1)"
      ],
      "metadata": {
        "id": "41X-ybrrjHv0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Ticket_prefix'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "axz7JgpG8USZ",
        "outputId": "9b2c0507-544e-4625-96d5-ad14e555865c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['A5', 'PC', 'STONO2', 'X', 'PP', 'CA', 'SCParis', 'SCA4', 'A4',\n",
              "       'SP', 'SOC', 'WC', 'SOTONOQ', 'WEP', 'STONO', 'C', 'SCPARIS',\n",
              "       'SOP', 'Fa', 'FCC', 'SWPP', 'SCOW', 'PPP', 'SC', 'SCAH', 'AS',\n",
              "       'SOPP', 'FC', 'SOTONO2', 'CASOTON'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 299
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df['Ticket_number'] = df['Ticket_number'].replace('LINE',0)\n",
        "#df['Ticket_number'] = df['Ticket_number'].astype(float)\n",
        "#df =df.drop(columns=['Ticket_number'])"
      ],
      "metadata": {
        "id": "_W95hGEk2VdW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#df['Ticket_number'] = df['Ticket_number'].replace(0,df['Ticket_number'].min()-1)"
      ],
      "metadata": {
        "id": "P5n4ehA3lYyK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = 'Pclass', y = 'Age', data = df, palette = 'winter')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298
        },
        "id": "zO3J7_hzPK2L",
        "outputId": "b3ccae93-c4bc-487c-89ff-357029c34f6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a86e25e50>"
            ]
          },
          "metadata": {},
          "execution_count": 300
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 385,
              "height": 264
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna()[['Age','Pclass','Fare']].corr()['Age']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jhVEoF_rcKgX",
        "outputId": "d7fc9efd-a70b-4503-b709-b60f169d4817"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age       1.000000\n",
              "Pclass   -0.369226\n",
              "Fare      0.096067\n",
              "Name: Age, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 301
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def transform_columns(column):\n",
        "    Age = column[0]\n",
        "    Pclass = column[1]\n",
        "    \n",
        "    if(pd.isna(Age)):\n",
        "       if(Pclass == 1):\n",
        "            return np.random.randint(27,47)\n",
        "       elif(Pclass == 2):\n",
        "            return np.random.randint(25,35)   \n",
        "       else:\n",
        "            return np.random.randint(18,31)  \n",
        "    else:\n",
        "       return Age\n",
        "\n",
        "df['Age'] = df[['Age', 'Pclass']].apply(transform_columns, axis = 1)"
      ],
      "metadata": {
        "id": "6uDZTpbrdyMU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JDwbIamVd9jG",
        "outputId": "1dd5d3d8-00c1-4c22-ad32-eb5db76b7f03"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 891 entries, 0 to 890\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   PassengerId    891 non-null    int64  \n",
            " 1   Survived       891 non-null    int64  \n",
            " 2   Pclass         891 non-null    int64  \n",
            " 3   Sex            891 non-null    object \n",
            " 4   Age            891 non-null    float64\n",
            " 5   Fare           891 non-null    float64\n",
            " 6   Embarked       891 non-null    object \n",
            " 7   Alone          891 non-null    bool   \n",
            " 8   Ticket_prefix  891 non-null    object \n",
            "dtypes: bool(1), float64(2), int64(3), object(3)\n",
            "memory usage: 56.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.drop('PassengerId',axis = 1)"
      ],
      "metadata": {
        "id": "Pf0i3VW3kKV8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "categorical_cols = df.columns[df.dtypes==object].tolist()\n",
        "categorical_cols"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRNn8saakKYS",
        "outputId": "22e8e404-670e-4f9b-e6af-352dde2d3810"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sex', 'Embarked', 'Ticket_prefix']"
            ]
          },
          "metadata": {},
          "execution_count": 305
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for col in categorical_cols:\n",
        "    df[col] = df[col].astype('category')\n",
        "    df[col] = df[col].cat.codes"
      ],
      "metadata": {
        "id": "Qoh7iFW1mfig"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "corr = df.corr()\n",
        "sns.heatmap(corr, annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "b_J-y_ovmflH",
        "outputId": "5b5fca15-a0d0-4b57-b3ea-cd60e43e15cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5a86caf410>"
            ]
          },
          "metadata": {},
          "execution_count": 307
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 411,
              "height": 306
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#df = df.drop(columns = ['Ticket_prefix'])"
      ],
      "metadata": {
        "id": "ervM_KQSPTum"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7yilZXdZz4O5",
        "outputId": "cdd2ef51-d985-44a9-f943-b07fa9a2c3ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-7fc01374-ba35-4534-9548-5f22664ad934\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Survived</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Alone</th>\n",
              "      <th>Ticket_prefix</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>38.0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>0</td>\n",
              "      <td>True</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>35.0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fc01374-ba35-4534-9548-5f22664ad934')\"\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-7fc01374-ba35-4534-9548-5f22664ad934 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-7fc01374-ba35-4534-9548-5f22664ad934');\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",
              "  "
            ],
            "text/plain": [
              "   Survived  Pclass  Sex   Age     Fare  Embarked  Alone  Ticket_prefix\n",
              "0         0       3    1  22.0   7.2500         2   True              1\n",
              "1         1       1    0  38.0  71.2833         0   True              9\n",
              "2         1       3    0  26.0   7.9250         2  False             25\n",
              "3         1       1    0  35.0  53.1000         2   True             29\n",
              "4         0       3    1  35.0   8.0500         2  False             29"
            ]
          },
          "metadata": {},
          "execution_count": 308
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "num_features = 9\n",
        "model_rfi = RandomForestClassifier(n_estimators=100)\n",
        "model_rfi.fit(df.drop(columns='Survived'), df['Survived'])\n",
        "fs_indices_rfi = np.argsort(model_rfi.feature_importances_)[::-1][0:num_features]\n",
        "\n",
        "best_features_rfi = df.columns[fs_indices_rfi].values\n",
        "best_features_rfi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqQYnBq0zee1",
        "outputId": "2a108a22-e788-4e04-f966-938b0646133b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Sex', 'Age', 'Pclass', 'Survived', 'Alone', 'Fare', 'Embarked'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 309
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_rfi.score(df.drop(columns='Survived'), df['Survived'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6zhxoQQ6kpO",
        "outputId": "352d31a5-e374-4a98-c972-6ae768f115b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9910213243546577"
            ]
          },
          "metadata": {},
          "execution_count": 310
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "feature_importances_rfi = model_rfi.feature_importances_[fs_indices_rfi]\n",
        "feature_importances_rfi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jroLCS-I6ksd",
        "outputId": "7762cc9e-a688-4b65-9bb8-e58c09b5e8b4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.28639809, 0.26851737, 0.25714968, 0.08391339, 0.04666564,\n",
              "       0.0338202 , 0.02353563])"
            ]
          },
          "metadata": {},
          "execution_count": 311
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "%matplotlib inline \n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "def plot_imp(best_features, scores, method_name): \n",
        "    plt.figure(figsize=(10,10))\n",
        "    plt.barh(best_features, scores)\n",
        "    plt.title(method_name + ' Feature Importances')\n",
        "    plt.xlabel(\"Importance\")\n",
        "    plt.ylabel(\"Features\")\n",
        "    plt.show()\n",
        "plot_imp(best_features_rfi, feature_importances_rfi, 'Random Forest')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 624
        },
        "id": "8_4LzLEQ6kzW",
        "outputId": "d242ccf0-e6a5-421e-8ea8-5f7f5b202d25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "image/png": {
              "width": 656,
              "height": 607
            }
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop(columns= 'Survived')\n",
        "y = df['Survived']"
      ],
      "metadata": {
        "id": "mJDKLjgTmfnx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inp_df = X.copy()\n",
        "Data_scaler = preprocessing.MinMaxScaler()\n",
        "Data_scaler.fit(X)\n",
        "X = Data_scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "cdmiNJ00mfqO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(X, columns=inp_df.columns).sample(15, random_state=999)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "93qbBeznmfs4",
        "outputId": "60c06eca-836d-4eca-9088-89089f34c1b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "     Pclass  Sex       Age      Fare  Embarked  Alone  Ticket_prefix\n",
              "857     0.0  1.0  0.635587  0.051822       1.0    0.0       1.000000\n",
              "666     0.5  1.0  0.308872  0.025374       1.0    0.0       1.000000\n",
              "350     1.0  1.0  0.283740  0.018006       1.0    0.0       1.000000\n",
              "90      1.0  1.0  0.359135  0.015713       1.0    0.0       1.000000\n",
              "583     0.0  1.0  0.447097  0.078319       0.0    0.0       1.000000\n",
              "545     0.0  1.0  0.798944  0.050749       1.0    0.0       1.000000\n",
              "691     1.0  0.0  0.044986  0.026188       0.0    1.0       1.000000\n",
              "170     0.0  1.0  0.761247  0.065388       1.0    0.0       1.000000\n",
              "40      1.0  0.0  0.497361  0.018494       1.0    1.0       1.000000\n",
              "180     1.0  0.0  0.308872  0.135753       1.0    1.0       0.137931\n",
              "94      1.0  1.0  0.736115  0.014151       1.0    0.0       1.000000\n",
              "615     0.5  0.0  0.296306  0.126872       1.0    1.0       1.000000\n",
              "744     1.0  1.0  0.384267  0.015469       1.0    0.0       0.827586\n",
              "843     1.0  1.0  0.428248  0.012565       0.0    0.0       1.000000\n",
              "275     0.0  0.0  0.786378  0.152164       1.0    1.0       1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 315
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 25)"
      ],
      "metadata": {
        "id": "G5r_q4dT9Jqo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rf_reg = RandomForestClassifier()\n",
        "rf_reg.fit(X_train, y_train)\n",
        "y_pred= rf_reg.predict(X_test)\n",
        "print(\"R2 score on Traing set: %.2f\"% rf_reg.score(X_train,y_train))\n",
        "print(\"R2 score on Testing set: %.2f\"% rf_reg.score(X_test,y_test))\n",
        "print('Mean squared error: %.2f'% mean_squared_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iOd01XeXCK-H",
        "outputId": "70e0da17-dee8-46d4-8f2b-c5af4a26686b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score on Traing set: 0.99\n",
            "R2 score on Testing set: 0.78\n",
            "Mean squared error: 0.22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "logis_model = LogisticRegression()\n",
        "logis_model.fit(X_train, y_train)\n",
        "y_pred= logis_model.predict(X_test)\n",
        "print(\"R2 score on Traing set: %.2f\"% logis_model.score(X_train,y_train))\n",
        "print(\"R2 score on Testing set: %.2f\"% logis_model.score(X_test,y_test))\n",
        "print('Mean squared error: %.2f'% mean_squared_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blqOS1YQ7Cb1",
        "outputId": "9fffd510-5217-4c2f-b86e-6b30d06b642f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score on Traing set: 0.79\n",
            "R2 score on Testing set: 0.80\n",
            "Mean squared error: 0.20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "ada_model = AdaBoostClassifier()\n",
        "ada_model.fit(X_train, y_train)\n",
        "y_pred= ada_model.predict(X_test)\n",
        "print(\"R2 score on Traing set: %.2f\"% ada_model.score(X_train,y_train))\n",
        "print(\"R2 score on Testing set: %.2f\"% ada_model.score(X_test,y_test))\n",
        "print('Mean squared error: %.2f'% mean_squared_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQATWpcdC-n_",
        "outputId": "29cb2906-1c8d-4039-b0a9-ce6dece52436"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score on Traing set: 0.83\n",
            "R2 score on Testing set: 0.80\n",
            "Mean squared error: 0.20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import StackingClassifier\n",
        "models = [\n",
        "    ('lr', logis_model),   \n",
        "    ('ran', rf_reg), \n",
        "    ('ada', ada_model)\n",
        "    ]\n",
        "stack_model = StackingClassifier(estimators = models, passthrough=True)\n",
        "stack_model.fit(X_train, y_train)\n",
        "y_pred= stack_model.predict(X_test)\n",
        "print(\"R2 score on Traing set: %.2f\"% stack_model.score(X_train,y_train))\n",
        "print(\"R2 score on Testing set: %.2f\"% stack_model.score(X_test,y_test))\n",
        "print('Mean squared error: %.2f'% mean_squared_error(y_test,y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7WetemrQC-q3",
        "outputId": "bb2ac35b-e7e6-4abe-9433-bed7c50a1d8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score on Traing set: 0.94\n",
            "R2 score on Testing set: 0.82\n",
            "Mean squared error: 0.18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Kaggle/Titanic/test.csv')"
      ],
      "metadata": {
        "id": "YWpiEh2-C-1n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RWLFG5ayE_hQ",
        "outputId": "baef70e5-6c87-4acb-b77a-31fa9e4635ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PassengerId</th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
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              "      <th>Ticket</th>\n",
              "      <th>Fare</th>\n",
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              "      <th>0</th>\n",
              "      <td>892</td>\n",
              "      <td>3</td>\n",
              "      <td>Kelly, Mr. James</td>\n",
              "      <td>male</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>330911</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>893</td>\n",
              "      <td>3</td>\n",
              "      <td>Wilkes, Mrs. James (Ellen Needs)</td>\n",
              "      <td>female</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>363272</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>894</td>\n",
              "      <td>2</td>\n",
              "      <td>Myles, Mr. Thomas Francis</td>\n",
              "      <td>male</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>240276</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Q</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>895</td>\n",
              "      <td>3</td>\n",
              "      <td>Wirz, Mr. Albert</td>\n",
              "      <td>male</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>315154</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>896</td>\n",
              "      <td>3</td>\n",
              "      <td>Hirvonen, Mrs. Alexander (Helga E Lindqvist)</td>\n",
              "      <td>female</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>3101298</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>NaN</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6dbeff26-fbad-48c3-a60b-f2f0c63c47ef')\"\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",
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              "\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\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-6dbeff26-fbad-48c3-a60b-f2f0c63c47ef 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-6dbeff26-fbad-48c3-a60b-f2f0c63c47ef');\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",
              "  "
            ],
            "text/plain": [
              "   PassengerId  Pclass  ... Cabin Embarked\n",
              "0          892       3  ...   NaN        Q\n",
              "1          893       3  ...   NaN        S\n",
              "2          894       2  ...   NaN        Q\n",
              "3          895       3  ...   NaN        S\n",
              "4          896       3  ...   NaN        S\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 322
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test['Cabin'] = np.where(test['Cabin'].isna(),'XX',test['Cabin'])\n",
        "test['Embarked'] = np.where(test['Embarked'].isna(),test['Embarked'].mode(),test['Embarked'])\n",
        "test['Fare'] = np.where(test['Fare'].isna(),test['Fare'].mode(),test['Fare'])\n",
        "test['Age'] = test[['Age', 'Pclass']].apply(transform_columns, axis = 1)\n",
        "test['Alone'] = (test['SibSp'] + test['Parch']) >0\n",
        "test = test.drop(['Parch', 'SibSp', 'Name', 'Cabin'],axis = 1)\n",
        "test['Ticket_prefix'] = test['Ticket'].str.split()\n",
        "#test['Ticket_number'] = test['Ticket_prefix']\n",
        "#test['Ticket_number'] = test.Ticket_number.apply(get_number)\n",
        "test['Ticket_prefix'] = test.Ticket_prefix.apply(get_prefix)\n",
        "test = test.drop(['Ticket'], axis = 1)"
      ],
      "metadata": {
        "id": "4uB9F7cHE_j0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTXs-4WYez5K",
        "outputId": "4b8a6e0c-e952-44b7-fa9a-e5a3357771da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 418 entries, 0 to 417\n",
            "Data columns (total 8 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   PassengerId    418 non-null    int64  \n",
            " 1   Pclass         418 non-null    int64  \n",
            " 2   Sex            418 non-null    object \n",
            " 3   Age            418 non-null    float64\n",
            " 4   Fare           418 non-null    float64\n",
            " 5   Embarked       418 non-null    object \n",
            " 6   Alone          418 non-null    bool   \n",
            " 7   Ticket_prefix  418 non-null    object \n",
            "dtypes: bool(1), float64(2), int64(2), object(3)\n",
            "memory usage: 23.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#test['Ticket_number'] = test['Ticket_number'].astype(float)\n",
        "index = test['PassengerId']\n",
        "test = test.drop('PassengerId',axis = 1)\n",
        "categorical_cols = test.columns[test.dtypes==object].tolist()\n",
        "categorical_cols\n",
        "for col in categorical_cols:\n",
        "    test[col] = test[col].astype('category')\n",
        "    test[col] = test[col].cat.codes"
      ],
      "metadata": {
        "id": "L75L5nkkE_mQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inp_df = test.copy()\n",
        "Data_scaler = preprocessing.MinMaxScaler()\n",
        "Data_scaler.fit(test)\n",
        "test = Data_scaler.fit_transform(test)\n"
      ],
      "metadata": {
        "id": "Jono9CBnS7q6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(test, columns=inp_df.columns).sample(5, random_state=999)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "5c_DdcatTOM3",
        "outputId": "5dff7aa3-5cb8-466f-9a87-c41f99b90e5c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-ae9729f1-f3eb-41de-bfb2-543bb56ebb61\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ae9729f1-f3eb-41de-bfb2-543bb56ebb61 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-ae9729f1-f3eb-41de-bfb2-543bb56ebb61');\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",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Pclass  Sex       Age      Fare  Embarked  Alone  Ticket_prefix\n",
              "317     0.5  1.0  0.248319  0.020495       1.0    0.0       1.000000\n",
              "344     1.0  1.0  0.301068  0.028302       1.0    1.0       0.074074\n",
              "221     1.0  1.0  0.274693  0.015713       1.0    0.0       1.000000\n",
              "287     0.0  1.0  0.314256  0.160574       1.0    1.0       1.000000\n",
              "219     1.0  1.0  0.235131  0.015713       1.0    0.0       1.000000"
            ]
          },
          "metadata": {},
          "execution_count": 327
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predic = ada_model.predict(test)"
      ],
      "metadata": {
        "id": "paRoIfLbTcp8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "submission = pd.DataFrame(index)\n",
        "submission[\"Survived\"] = predic\n",
        "\n",
        "submission.to_csv(\"/content/drive/MyDrive/Colab Notebooks/Kaggle/Titanic/submission.csv\", index=False)"
      ],
      "metadata": {
        "id": "Nv3shA2MTkxr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}