Titanic_logistic_regression_Tensorflow_AT.ipynb

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    "colab": {
      "name": "Titanic logistic regression Tensorflow.ipynb",
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    {
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        "id": "5Ntq1DBKKCdu"
      },
      "source": [
        "# **Introduction**"
      ]
    },
    {
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        "tags": [],
        "id": "VclXaC2CKCdw"
      },
      "source": [
        "***Howdy, Welcome to the Titanic***"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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        "id": "WuIyb0x1KCdy"
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      "source": [
        "**To whom does this notebook appeal to?**<br><br>\n",
        "If you are just starting with WNNC's HELLO FOSS and want to do beginner task in ML, you can try this project!\n",
        "\n",
        "Data reading, visualization and pre-processing has been done for you. We need you to use logistic regression using sklearn library to predict whether or not they survived the sinking of the Titanic. We have also included `gender_submission.csv`, a set of predictions that assume all and only female passengers survive, as an example of what a submission file should look like.\n",
        "\n",
        "Head over to [this cell](#main) to implement logistic regression and then add code for writing the output to file `predictions-ann.csv` [here](#main2). "
      ]
    },
    {
      "cell_type": "markdown",
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        "tags": [],
        "id": "N_27sUKIKCdz"
      },
      "source": [
        "![](https://faithmag.com/sites/default/files/styles/article_full/public/2018-09/titanic2.jpg?h=6521bd5e&itok=H8td6QVv)"
      ]
    },
    {
      "cell_type": "markdown",
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        "tags": [],
        "id": "elYqLSI1KCd0"
      },
      "source": [
        "**Basic Imports**"
      ]
    },
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "sns.set()\n",
        "\n",
        "import os\n",
        "files = [f for f in os.listdir('./titanic/')]\n",
        "for f in files:\n",
        "        print(f)\n",
        "\n"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test.csv\n",
            "output.csv\n",
            "grader.py\n",
            "gender_submission.csv\n",
            "train.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
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        "tags": [],
        "id": "fkA_kiLAKCd-"
      },
      "source": [
        "# Reading In the Data"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "We will be first importing the data, and creating copies. I recommend this because it give you access to a clean untouched base file.\n",
        "Next, we will be dropping some things that we don't need such as passenger Id and Ticket price"
      ]
    },
    {
      "cell_type": "code",
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        "execution": {
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      "source": [
        "train_data = pd.read_csv('./titanic/train.csv')\n",
        "test_data = pd.read_csv('./titanic/test.csv')\n",
        "#make a copy so your original data is not touched\n",
        "train = train_data.copy()\n",
        "test = test_data.copy()\n",
        "train.shape\n",
        "y_train = train['Survived']\n",
        "\n",
        "#We won't need passenger ID or ticket price for the model! They do not provide much insight on the training.\n",
        "Id = pd.DataFrame(test['PassengerId'])\n",
        "train.drop(['PassengerId'], axis = 1, inplace=True)\n",
        "test.drop(['PassengerId'], axis = 1, inplace=True)\n",
        "train.drop(['Survived'], axis = 1, inplace=True)\n",
        "train.drop(['Ticket'], axis = 1, inplace=True)\n",
        "test.drop(['Ticket'], axis = 1, inplace=True)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
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        "tags": [],
        "id": "OELeUe8-KCeG"
      },
      "source": [
        "**Let's take a look at our data!**"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Name</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Cabin</th>\n",
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              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>Braund, Mr. Owen Harris</td>\n",
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              "      <td>1</td>\n",
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              "      <td>7.2500</td>\n",
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              "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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              "      <td>38.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
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              "      <td>3</td>\n",
              "      <td>Heikkinen, Miss. Laina</td>\n",
              "      <td>female</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
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              "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
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              "      <td>35.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>53.1000</td>\n",
              "      <td>C123</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>male</td>\n",
              "      <td>35.0</td>\n",
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              "   Pclass                                               Name  ... Cabin  Embarked\n",
              "0       3                            Braund, Mr. Owen Harris  ...   NaN         S\n",
              "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  ...   C85         C\n",
              "2       3                             Heikkinen, Miss. Laina  ...   NaN         S\n",
              "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  ...  C123         S\n",
              "4       3                           Allen, Mr. William Henry  ...   NaN         S\n",
              "\n",
              "[5 rows x 9 columns]"
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            "tags": []
          },
          "execution_count": 75
        }
      ]
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        "tags": [],
        "id": "-gRn4V-6KCeO"
      },
      "source": [
        "**Lets see how many null values there are! We need to fill out these values later.**"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "**We see that we have some midding values from age, and a ton missing from cabin**"
      ]
    },
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      },
      "source": [
        "sns.heatmap(train.isnull(),yticklabels=False,cbar='BuPu')\n"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f75e0a06518>"
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          "execution_count": 76
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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:04.797107Z",
          "iopub.status.busy": "2020-08-16T00:03:04.795587Z",
          "iopub.status.idle": "2020-08-16T00:03:04.799528Z",
          "shell.execute_reply": "2020-08-16T00:03:04.799041Z"
        },
        "papermill": {
          "duration": 0.027205,
          "end_time": "2020-08-16T00:03:04.799633",
          "exception": false,
          "start_time": "2020-08-16T00:03:04.772428",
          "status": "completed"
        },
        "tags": [],
        "id": "usNUjwx1KCeX",
        "outputId": "6132bf87-a2aa-48f2-cfbf-ff6648700b46",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "train.isnull().sum().sort_values(ascending=False)[0:20]\n",
        "# we can see that cabin is midding a lot of values, and age is tooi!"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Cabin       687\n",
              "Age         177\n",
              "Embarked      2\n",
              "Fare          0\n",
              "Parch         0\n",
              "SibSp         0\n",
              "Sex           0\n",
              "Name          0\n",
              "Pclass        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.011933,
          "end_time": "2020-08-16T00:03:04.823720",
          "exception": false,
          "start_time": "2020-08-16T00:03:04.811787",
          "status": "completed"
        },
        "tags": [],
        "id": "Leed790wKCed"
      },
      "source": [
        "# **Data Cleaning**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.011829,
          "end_time": "2020-08-16T00:03:04.847591",
          "exception": false,
          "start_time": "2020-08-16T00:03:04.835762",
          "status": "completed"
        },
        "tags": [],
        "id": "If8p2gpVKCee"
      },
      "source": [
        "**This is an awesome function I created that preprocesses the data. It does thes following**\n",
        "\n",
        "* Fills in null values based on mean or mode\n",
        "* Drops columns that are missing 50 percent of the data\n",
        "\n",
        "*You guys are free to copy this for loop for your own projects!*[](http://)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:04.892575Z",
          "iopub.status.busy": "2020-08-16T00:03:04.891608Z",
          "iopub.status.idle": "2020-08-16T00:03:04.907126Z",
          "shell.execute_reply": "2020-08-16T00:03:04.906618Z"
        },
        "papermill": {
          "duration": 0.047502,
          "end_time": "2020-08-16T00:03:04.907233",
          "exception": false,
          "start_time": "2020-08-16T00:03:04.859731",
          "status": "completed"
        },
        "tags": [],
        "id": "RHMizfJxKCef"
      },
      "source": [
        "\n",
        "\n",
        "#clean the train data\n",
        "for i in list(train.columns):\n",
        "    dtype = train[i].dtype\n",
        "    values = 0\n",
        "    if(dtype == float or dtype == int):\n",
        "        method = 'mean'\n",
        "    else:\n",
        "        method = 'mode'\n",
        "    if(train[i].notnull().sum() / 891 <= .5):\n",
        "        train.drop(i, axis = 1, inplace=True)\n",
        "    elif method == 'mean':\n",
        "        train[i]=train[i].fillna(train[i].mean())\n",
        "\n",
        "    else:\n",
        "        train[i]=train[i].fillna(train[i].mode()[0])\n",
        "\n",
        "# WE CAN DO THIS FOR THE TEST SET TOO!\n",
        "\n",
        "#clean the test data\n",
        "for i in list(test.columns):\n",
        "    dtype = test[i].dtype\n",
        "    values = 0\n",
        "    if(dtype == float or dtype == int):\n",
        "        method = 'mean'\n",
        "    else:\n",
        "        method = 'mode'\n",
        "    if(test[i].notnull().sum() / 418 <= .5):\n",
        "        test.drop(i, axis = 1, inplace=True)\n",
        "    elif method == 'mean':\n",
        "        test[i]=test[i].fillna(test[i].mean())\n",
        "\n",
        "    else:\n",
        "        test[i]=test[i].fillna(test[i].mode()[0])\n",
        "\n",
        "\n"
      ],
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.011993,
          "end_time": "2020-08-16T00:03:04.931751",
          "exception": false,
          "start_time": "2020-08-16T00:03:04.919758",
          "status": "completed"
        },
        "tags": [],
        "id": "hxkl4k_GKCek"
      },
      "source": [
        "**As we can see, all of the missing values are gone!**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "_kg_hide-input": true,
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:04.963771Z",
          "iopub.status.busy": "2020-08-16T00:03:04.962852Z",
          "iopub.status.idle": "2020-08-16T00:03:05.218436Z",
          "shell.execute_reply": "2020-08-16T00:03:05.218946Z"
        },
        "papermill": {
          "duration": 0.275108,
          "end_time": "2020-08-16T00:03:05.219105",
          "exception": false,
          "start_time": "2020-08-16T00:03:04.943997",
          "status": "completed"
        },
        "tags": [],
        "id": "WOwMEmnoKCel",
        "outputId": "99421684-e904-4c2f-fb1e-13048fae58d3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        }
      },
      "source": [
        "sns.heatmap(train.isnull(),yticklabels=False,cbar='BuPu')\n"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f75e07c1668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.013345,
          "end_time": "2020-08-16T00:03:05.246558",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.233213",
          "status": "completed"
        },
        "tags": [],
        "id": "MU1nFSUhKCeq"
      },
      "source": [
        "# **Feature Engineering**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.014138,
          "end_time": "2020-08-16T00:03:05.274477",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.260339",
          "status": "completed"
        },
        "tags": [],
        "id": "BtXZoKvBKCer"
      },
      "source": [
        "**Title**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.012818,
          "end_time": "2020-08-16T00:03:05.300776",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.287958",
          "status": "completed"
        },
        "tags": [],
        "id": "8WJnpk5JKCes"
      },
      "source": [
        "Lets take out the Mr, Miss, etc from the name section, and create a new column names title!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:05.346398Z",
          "iopub.status.busy": "2020-08-16T00:03:05.345561Z",
          "iopub.status.idle": "2020-08-16T00:03:05.349914Z",
          "shell.execute_reply": "2020-08-16T00:03:05.349327Z"
        },
        "papermill": {
          "duration": 0.035659,
          "end_time": "2020-08-16T00:03:05.350024",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.314365",
          "status": "completed"
        },
        "tags": [],
        "id": "QIBvxjgqKCes"
      },
      "source": [
        "#TITLE\n",
        "\n",
        "train_test_data = [train, test] # combining train and test dataset\n",
        "\n",
        "for dataset in train_test_data:\n",
        "    dataset['Title'] = dataset['Name'].str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
        "\n",
        "\n",
        "title_mapping = {\"Mr\": 0, \"Miss\": 1, \"Mrs\": 1, \n",
        "                 \"Master\": 0, \"Dr\": 1, \"Rev\": 0, \"Col\": 0, \"Major\": 0, \"Mlle\": 1,\"Countess\": 1,\n",
        "                 \"Ms\": 1, \"Lady\": 1, \"Jonkheer\": 1, \"Don\": 0, \"Dona\" : 1, \"Mme\": 0,\"Capt\": 0,\"Sir\": 0 }\n",
        "for dataset in train_test_data:\n",
        "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
        "    \n"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.013586,
          "end_time": "2020-08-16T00:03:05.377981",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.364395",
          "status": "completed"
        },
        "tags": [],
        "id": "WefldkttKCe0"
      },
      "source": [
        "**Sex**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.013191,
          "end_time": "2020-08-16T00:03:05.405476",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.392285",
          "status": "completed"
        },
        "tags": [],
        "id": "tlyULF5TKCe1"
      },
      "source": [
        "We can make male and female into catagorical variables such as 0 and 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:05.442498Z",
          "iopub.status.busy": "2020-08-16T00:03:05.441620Z",
          "iopub.status.idle": "2020-08-16T00:03:05.444837Z",
          "shell.execute_reply": "2020-08-16T00:03:05.444291Z"
        },
        "papermill": {
          "duration": 0.025484,
          "end_time": "2020-08-16T00:03:05.444947",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.419463",
          "status": "completed"
        },
        "tags": [],
        "id": "3QuHNF9QKCe3"
      },
      "source": [
        "    \n",
        "sex_mapping = {\"male\": 0, \"female\":1}\n",
        "for dataset in train_test_data:\n",
        "     dataset['Sex'] = dataset['Sex'].map(sex_mapping)\n"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.013681,
          "end_time": "2020-08-16T00:03:05.472676",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.458995",
          "status": "completed"
        },
        "tags": [],
        "id": "I_YvwnWmKCe8"
      },
      "source": [
        "**Embarked**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.014216,
          "end_time": "2020-08-16T00:03:05.500391",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.486175",
          "status": "completed"
        },
        "tags": [],
        "id": "ELLdTSf0KCe9"
      },
      "source": [
        "Lets test to see if there is any correlation with Pclass and Embarked "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:05.537924Z",
          "iopub.status.busy": "2020-08-16T00:03:05.537005Z",
          "iopub.status.idle": "2020-08-16T00:03:05.764053Z",
          "shell.execute_reply": "2020-08-16T00:03:05.763057Z"
        },
        "papermill": {
          "duration": 0.249537,
          "end_time": "2020-08-16T00:03:05.764169",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.514632",
          "status": "completed"
        },
        "tags": [],
        "id": "u-x_kRv-KCe-",
        "outputId": "6bc17a5b-dead-4029-a7bf-68e22c186a9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "source": [
        "Pclass1 = train_data[train_data['Pclass'] == 1]['Embarked'].value_counts()\n",
        "Pclass2 = train_data[train_data['Pclass'] == 2]['Embarked'].value_counts()\n",
        "Pclass3 = train_data[train_data['Pclass'] == 3]['Embarked'].value_counts()\n",
        "\n",
        "df = pd.DataFrame([Pclass1, Pclass2, Pclass3])\n",
        "df.index = ['1st class','2nd class', '3rd class']\n",
        "df.plot(kind='bar',stacked=True, figsize=(10,5))"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f75e06fbba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.014366,
          "end_time": "2020-08-16T00:03:05.793175",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.778809",
          "status": "completed"
        },
        "tags": [],
        "id": "-tRPubfiKCfE"
      },
      "source": [
        "We can assign each embarked value to a numberical value for training later!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:05.830003Z",
          "iopub.status.busy": "2020-08-16T00:03:05.829030Z",
          "iopub.status.idle": "2020-08-16T00:03:05.835622Z",
          "shell.execute_reply": "2020-08-16T00:03:05.835037Z"
        },
        "papermill": {
          "duration": 0.027899,
          "end_time": "2020-08-16T00:03:05.835744",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.807845",
          "status": "completed"
        },
        "tags": [],
        "id": "3deYpK2JKCfF"
      },
      "source": [
        "for data in train_test_data:\n",
        "    data['Embarked'] = data['Embarked'].fillna(\"S\")\n",
        "    \n",
        "embarked_mapping = {\"S\": 0, \"C\": 1, \"Q\": 2}\n",
        "for dataset in train_test_data:\n",
        "    dataset['Embarked'] = dataset['Embarked'].map(embarked_mapping)"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.013917,
          "end_time": "2020-08-16T00:03:05.863782",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.849865",
          "status": "completed"
        },
        "tags": [],
        "id": "LWb3GbzWKCfL"
      },
      "source": [
        "**Family Size**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.014221,
          "end_time": "2020-08-16T00:03:05.892815",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.878594",
          "status": "completed"
        },
        "tags": [],
        "id": "6aQauwFJKCfM"
      },
      "source": [
        "Parch ( Parent & child ) and Sibsp( Sibling & Spouse ) are both contributing factors to family size, so lets make a new column called family size, and drop the other ones."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:05.931503Z",
          "iopub.status.busy": "2020-08-16T00:03:05.930571Z",
          "iopub.status.idle": "2020-08-16T00:03:05.933769Z",
          "shell.execute_reply": "2020-08-16T00:03:05.933193Z"
        },
        "papermill": {
          "duration": 0.025993,
          "end_time": "2020-08-16T00:03:05.933872",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.907879",
          "status": "completed"
        },
        "tags": [],
        "id": "14VHn112KCfN"
      },
      "source": [
        "train[\"FamilySize\"] = train['SibSp'] + train['Parch'] + 1\n",
        "test[\"FamilySize\"] = test['SibSp'] + test['Parch'] + 1"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.014325,
          "end_time": "2020-08-16T00:03:05.962742",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.948417",
          "status": "completed"
        },
        "tags": [],
        "id": "h5iRRX4jKCfS"
      },
      "source": [
        "# **Correlations**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:05.997135Z",
          "iopub.status.busy": "2020-08-16T00:03:05.996451Z",
          "iopub.status.idle": "2020-08-16T00:03:06.312682Z",
          "shell.execute_reply": "2020-08-16T00:03:06.312109Z"
        },
        "papermill": {
          "duration": 0.334816,
          "end_time": "2020-08-16T00:03:06.312796",
          "exception": false,
          "start_time": "2020-08-16T00:03:05.977980",
          "status": "completed"
        },
        "tags": [],
        "id": "TDMb1dVKKCfU",
        "outputId": "e6ef1e43-09a4-46d0-899a-aaecbecac3f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        }
      },
      "source": [
        "sns.heatmap(train.corr(),cbar='plasma')\n"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f75e0660940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:06.354102Z",
          "iopub.status.busy": "2020-08-16T00:03:06.352041Z",
          "iopub.status.idle": "2020-08-16T00:03:06.354808Z",
          "shell.execute_reply": "2020-08-16T00:03:06.355312Z"
        },
        "papermill": {
          "duration": 0.027178,
          "end_time": "2020-08-16T00:03:06.355462",
          "exception": false,
          "start_time": "2020-08-16T00:03:06.328284",
          "status": "completed"
        },
        "tags": [],
        "id": "CE2T1lH7KCfZ"
      },
      "source": [
        "train.drop(['Name'], axis = 1, inplace=True)\n",
        "test.drop(['Name'], axis = 1, inplace=True)"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:06.398320Z",
          "iopub.status.busy": "2020-08-16T00:03:06.397342Z",
          "iopub.status.idle": "2020-08-16T00:03:06.401654Z",
          "shell.execute_reply": "2020-08-16T00:03:06.401144Z"
        },
        "papermill": {
          "duration": 0.031322,
          "end_time": "2020-08-16T00:03:06.401771",
          "exception": false,
          "start_time": "2020-08-16T00:03:06.370449",
          "status": "completed"
        },
        "tags": [],
        "id": "A6KpT2BqKCfe",
        "outputId": "18577a0b-15bc-4612-97b3-11d53cfe8457",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\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>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
              "      <th>Parch</th>\n",
              "      <th>Fare</th>\n",
              "      <th>Embarked</th>\n",
              "      <th>Title</th>\n",
              "      <th>FamilySize</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.2500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>38.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>71.2833</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.9250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</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>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.0500</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Title  FamilySize\n",
              "0       3    0  22.0      1      0   7.2500         0      0           2\n",
              "1       1    1  38.0      1      0  71.2833         1      1           2\n",
              "2       3    1  26.0      0      0   7.9250         0      1           1\n",
              "3       1    1  35.0      1      0  53.1000         0      1           2\n",
              "4       3    0  35.0      0      0   8.0500         0      0           1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:06.444039Z",
          "iopub.status.busy": "2020-08-16T00:03:06.443083Z",
          "iopub.status.idle": "2020-08-16T00:03:06.447367Z",
          "shell.execute_reply": "2020-08-16T00:03:06.446894Z"
        },
        "papermill": {
          "duration": 0.030995,
          "end_time": "2020-08-16T00:03:06.447465",
          "exception": false,
          "start_time": "2020-08-16T00:03:06.416470",
          "status": "completed"
        },
        "tags": [],
        "id": "AiiJC9jJKCfk",
        "outputId": "95c6a9fa-4ecd-4b54-ce60-c88b4b63e574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "test.head()"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pclass</th>\n",
              "      <th>Sex</th>\n",
              "      <th>Age</th>\n",
              "      <th>SibSp</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>34.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>7.8292</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>47.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>62.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>9.6875</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>8.6625</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>22.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>12.2875</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Pclass  Sex   Age  SibSp  Parch     Fare  Embarked  Title  FamilySize\n",
              "0       3    0  34.5      0      0   7.8292         2      0           1\n",
              "1       3    1  47.0      1      0   7.0000         0      1           2\n",
              "2       2    0  62.0      0      0   9.6875         2      0           1\n",
              "3       3    0  27.0      0      0   8.6625         0      0           1\n",
              "4       3    1  22.0      1      1  12.2875         0      1           3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.01354,
          "end_time": "2020-08-16T00:03:06.476026",
          "exception": false,
          "start_time": "2020-08-16T00:03:06.462486",
          "status": "completed"
        },
        "tags": [],
        "id": "ugM9BwXgKCfp"
      },
      "source": [
        "# **PreProcessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.014099,
          "end_time": "2020-08-16T00:03:06.504426",
          "exception": false,
          "start_time": "2020-08-16T00:03:06.490327",
          "status": "completed"
        },
        "tags": [],
        "id": "gq5o54HcKCfq"
      },
      "source": [
        "**Now that our data looks good, lets get ready to build our models!**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:06.538376Z",
          "iopub.status.busy": "2020-08-16T00:03:06.537612Z",
          "iopub.status.idle": "2020-08-16T00:03:12.103848Z",
          "shell.execute_reply": "2020-08-16T00:03:12.102607Z"
        },
        "papermill": {
          "duration": 5.585812,
          "end_time": "2020-08-16T00:03:12.103987",
          "exception": false,
          "start_time": "2020-08-16T00:03:06.518175",
          "status": "completed"
        },
        "tags": [],
        "id": "YloUu1gWKCfr"
      },
      "source": [
        "#imports\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import GridSearchCV"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.01503,
          "end_time": "2020-08-16T00:03:12.134200",
          "exception": false,
          "start_time": "2020-08-16T00:03:12.119170",
          "status": "completed"
        },
        "tags": [],
        "id": "gw5Xgvo2KCfv"
      },
      "source": [
        "We should scale the values in the data, so that the neural network can train better!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:12.174580Z",
          "iopub.status.busy": "2020-08-16T00:03:12.173590Z",
          "iopub.status.idle": "2020-08-16T00:03:12.188156Z",
          "shell.execute_reply": "2020-08-16T00:03:12.187674Z"
        },
        "papermill": {
          "duration": 0.038245,
          "end_time": "2020-08-16T00:03:12.188266",
          "exception": false,
          "start_time": "2020-08-16T00:03:12.150021",
          "status": "completed"
        },
        "tags": [],
        "id": "BczepkMHKCfw"
      },
      "source": [
        "continuous = ['Age', 'Fare', 'Parch', 'Pclass', 'SibSp', 'FamilySize']\n",
        "\n",
        "scaler = StandardScaler()\n",
        "\n",
        "for var in continuous:\n",
        "    train[var] = train[var].astype('float64')\n",
        "    train[var] = scaler.fit_transform(train[var].values.reshape(-1, 1))\n",
        "for var in continuous:\n",
        "    test[var] = test[var].astype('float64')\n",
        "    test[var] = scaler.fit_transform(test[var].values.reshape(-1, 1))"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:12.223938Z",
          "iopub.status.busy": "2020-08-16T00:03:12.223053Z",
          "iopub.status.idle": "2020-08-16T00:03:12.259925Z",
          "shell.execute_reply": "2020-08-16T00:03:12.260500Z"
        },
        "papermill": {
          "duration": 0.057722,
          "end_time": "2020-08-16T00:03:12.260637",
          "exception": false,
          "start_time": "2020-08-16T00:03:12.202915",
          "status": "completed"
        },
        "tags": [],
        "id": "s5YkDFz_KCf0",
        "outputId": "5a72a263-9a1c-4724-b68b-b64c5750f8ee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        }
      },
      "source": [
        "train.describe(include='all').T\n"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Pclass</th>\n",
              "      <td>891.0</td>\n",
              "      <td>-2.031048e-16</td>\n",
              "      <td>1.000562</td>\n",
              "      <td>-1.566107</td>\n",
              "      <td>-0.369365</td>\n",
              "      <td>0.827377</td>\n",
              "      <td>0.827377</td>\n",
              "      <td>0.827377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sex</th>\n",
              "      <td>891.0</td>\n",
              "      <td>3.524130e-01</td>\n",
              "      <td>0.477990</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>891.0</td>\n",
              "      <td>2.562796e-16</td>\n",
              "      <td>1.000562</td>\n",
              "      <td>-2.253155</td>\n",
              "      <td>-0.592481</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.407926</td>\n",
              "      <td>3.870872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SibSp</th>\n",
              "      <td>891.0</td>\n",
              "      <td>3.456519e-16</td>\n",
              "      <td>1.000562</td>\n",
              "      <td>-0.474545</td>\n",
              "      <td>-0.474545</td>\n",
              "      <td>-0.474545</td>\n",
              "      <td>0.432793</td>\n",
              "      <td>6.784163</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Parch</th>\n",
              "      <td>891.0</td>\n",
              "      <td>6.716164e-17</td>\n",
              "      <td>1.000562</td>\n",
              "      <td>-0.473674</td>\n",
              "      <td>-0.473674</td>\n",
              "      <td>-0.473674</td>\n",
              "      <td>-0.473674</td>\n",
              "      <td>6.974147</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fare</th>\n",
              "      <td>891.0</td>\n",
              "      <td>-4.373606e-17</td>\n",
              "      <td>1.000562</td>\n",
              "      <td>-0.648422</td>\n",
              "      <td>-0.489148</td>\n",
              "      <td>-0.357391</td>\n",
              "      <td>-0.024246</td>\n",
              "      <td>9.667167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Embarked</th>\n",
              "      <td>891.0</td>\n",
              "      <td>3.613917e-01</td>\n",
              "      <td>0.635673</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title</th>\n",
              "      <td>891.0</td>\n",
              "      <td>3.591470e-01</td>\n",
              "      <td>0.480020</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FamilySize</th>\n",
              "      <td>891.0</td>\n",
              "      <td>2.502051e-16</td>\n",
              "      <td>1.000562</td>\n",
              "      <td>-0.560975</td>\n",
              "      <td>-0.560975</td>\n",
              "      <td>-0.560975</td>\n",
              "      <td>0.059160</td>\n",
              "      <td>5.640372</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            count          mean       std  ...       50%       75%       max\n",
              "Pclass      891.0 -2.031048e-16  1.000562  ...  0.827377  0.827377  0.827377\n",
              "Sex         891.0  3.524130e-01  0.477990  ...  0.000000  1.000000  1.000000\n",
              "Age         891.0  2.562796e-16  1.000562  ...  0.000000  0.407926  3.870872\n",
              "SibSp       891.0  3.456519e-16  1.000562  ... -0.474545  0.432793  6.784163\n",
              "Parch       891.0  6.716164e-17  1.000562  ... -0.473674 -0.473674  6.974147\n",
              "Fare        891.0 -4.373606e-17  1.000562  ... -0.357391 -0.024246  9.667167\n",
              "Embarked    891.0  3.613917e-01  0.635673  ...  0.000000  1.000000  2.000000\n",
              "Title       891.0  3.591470e-01  0.480020  ...  0.000000  1.000000  1.000000\n",
              "FamilySize  891.0  2.502051e-16  1.000562  ... -0.560975  0.059160  5.640372\n",
              "\n",
              "[9 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.014497,
          "end_time": "2020-08-16T00:03:12.290830",
          "exception": false,
          "start_time": "2020-08-16T00:03:12.276333",
          "status": "completed"
        },
        "tags": [],
        "id": "4v2sVynVKCf5"
      },
      "source": [
        "# **Logistic Regression**\n",
        "\n",
        "* `train` - stores training set data\n",
        "* `y_train` - stores training set results\n",
        "* `test` - stores test set data on which predictions have to be made\n",
        "\n",
        "First, fit the model using `train` and `y_train` and then make prediction on `test`\n",
        "<a id='main'></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:12.359244Z",
          "iopub.status.busy": "2020-08-16T00:03:12.358425Z",
          "iopub.status.idle": "2020-08-16T00:03:15.147061Z",
          "shell.execute_reply": "2020-08-16T00:03:15.146465Z"
        },
        "papermill": {
          "duration": 2.812936,
          "end_time": "2020-08-16T00:03:15.147176",
          "exception": false,
          "start_time": "2020-08-16T00:03:12.334240",
          "status": "completed"
        },
        "tags": [],
        "id": "jSggkz6AKCf6"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "preds = LogisticRegression().fit(train,y_train)\n",
        "test_preds = preds.predict(test)\n",
        "\n",
        "#Write code for logistic regression here"
      ],
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "papermill": {
          "duration": 0.026701,
          "end_time": "2020-08-16T00:03:18.692199",
          "exception": false,
          "start_time": "2020-08-16T00:03:18.665498",
          "status": "completed"
        },
        "tags": [],
        "id": "wVJkQArOKCf_"
      },
      "source": [
        "**Create Predictions**\n",
        "<a id='main2'></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-08-16T00:03:18.751021Z",
          "iopub.status.busy": "2020-08-16T00:03:18.750047Z",
          "iopub.status.idle": "2020-08-16T00:03:19.062102Z",
          "shell.execute_reply": "2020-08-16T00:03:19.061231Z"
        },
        "papermill": {
          "duration": 0.350469,
          "end_time": "2020-08-16T00:03:19.062253",
          "exception": false,
          "start_time": "2020-08-16T00:03:18.711784",
          "status": "completed"
        },
        "tags": [],
        "id": "MhF2n5hlKCgA",
        "outputId": "ef39be92-0f14-4f5f-f935-08a1e7ed43de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "#Write code to generate predictions file\n",
        "passenger_ids = test_data['PassengerId'].to_numpy()\n",
        "# combine passenger ids with the predictions\n",
        "combined = np.concatenate((passenger_ids.reshape(-1,1) , test_preds.reshape(-1,1)), axis = -1)\n",
        "# convert array to dataframe\n",
        "result_df = pd.DataFrame(combined, columns=[\"Pass_id\",\"Survived\"])\n",
        "# save the result to predictions-ann.csv\n",
        "result_df.to_csv(\"predictions-ann.csv\",index = False)\n",
        "print(result_df)"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     Pass_id  Survived\n",
            "0        892         0\n",
            "1        893         0\n",
            "2        894         0\n",
            "3        895         0\n",
            "4        896         1\n",
            "..       ...       ...\n",
            "413     1305         0\n",
            "414     1306         1\n",
            "415     1307         0\n",
            "416     1308         0\n",
            "417     1309         0\n",
            "\n",
            "[418 rows x 2 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eh0AIh6bRCxO"
      },
      "source": [
        "def grade():\n",
        "    data_to_be_graded = pd.read_csv('/content/predictions-ann.csv')\n",
        "    expected = pd.read_csv('/content/titanic/output.csv')\n",
        "    accuracy = sum(expected['Survived'].values == data_to_be_graded['Survived'].values) / len(expected)\n",
        "    print(\"Your accuracy is {}%\".format(round(accuracy * 100, 2)))\n"
      ],
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fr5SQ6qDRMbo",
        "outputId": "f31b0c3b-e7cb-40b9-df2d-32d0aeb20398",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "grade()"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your accuracy is 77.03%\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}