Titanic neural network Tensorflow.ipynb

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   },
   "source": [
    "# **Introduction**"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "***Howdy, Welcome to the Titanic***"
   ]
  },
  {
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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, I suggest you start here!\n",
    "\n",
    "Run this notebook once and you can see the accuracy of this model is mere 61% :(\n",
    "\n",
    "Data reading, visualization and pre-processing has been done for you. We need you to tune the hypreparameters so as to improve its accuracy and bring it above **80%**. Head over to [this cell](#main) to make the nececsaary changes. Afterwards graphically visualise your training and validation accuracy with each epoch [here](#main2)."
   ]
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   "source": [
    "![](https://faithmag.com/sites/default/files/styles/article_full/public/2018-09/titanic2.jpg?h=6521bd5e&itok=H8td6QVv)"
   ]
  },
  {
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    "tags": []
   },
   "source": [
    "**Basic Imports**"
   ]
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  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gender_submission.csv\n",
      "test.csv\n",
      "train.csv\n"
     ]
    }
   ],
   "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"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "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",
   "execution_count": 2,
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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)"
   ]
  },
  {
   "cell_type": "markdown",
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    "tags": []
   },
   "source": [
    "**Let's take a look at our data!**"
   ]
  },
  {
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       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
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       "2       3                             Heikkinen, Miss. Laina  female  26.0   \n",
       "3       1       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "4       3                           Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "   SibSp  Parch     Fare Cabin Embarked  \n",
       "0      1      0   7.2500   NaN        S  \n",
       "1      1      0  71.2833   C85        C  \n",
       "2      0      0   7.9250   NaN        S  \n",
       "3      1      0  53.1000  C123        S  \n",
       "4      0      0   8.0500   NaN        S  "
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     "metadata": {},
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   "source": [
    "train.head()"
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    "tags": []
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   "source": [
    "**Lets see how many null values there are! We need to fill out these values later.**"
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    "**We see that we have some midding values from age, and a ton missing from cabin**"
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       "<AxesSubplot:>"
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar='BuPu')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": []
   },
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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!"
   ]
  },
  {
   "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": []
   },
   "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": []
   },
   "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",
   "execution_count": 6,
   "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": []
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "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": []
   },
   "source": [
    "**As we can see, all of the missing values are gone!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.isnull(),yticklabels=False,cbar='BuPu')\n"
   ]
  },
  {
   "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": []
   },
   "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": []
   },
   "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": []
   },
   "source": [
    "Lets take out the Mr, Miss, etc from the name section, and create a new column names title!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": []
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "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": []
   },
   "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": []
   },
   "source": [
    "We can make male and female into catagorical variables such as 0 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": []
   },
   "outputs": [],
   "source": [
    "    \n",
    "sex_mapping = {\"male\": 0, \"female\":1}\n",
    "for dataset in train_test_data:\n",
    "    dataset['Sex'] = dataset['Sex'].map(sex_mapping)\n"
   ]
  },
  {
   "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": []
   },
   "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": []
   },
   "source": [
    "Lets test to see if there is any correlation with Pclass and Embarked "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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))"
   ]
  },
  {
   "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": []
   },
   "source": [
    "We can assign each embarked value to a numberical value for training later!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": []
   },
   "outputs": [],
   "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)"
   ]
  },
  {
   "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": []
   },
   "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": []
   },
   "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",
   "execution_count": 12,
   "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": []
   },
   "outputs": [],
   "source": [
    "train[\"FamilySize\"] = train['SibSp'] + train['Parch'] + 1\n",
    "test[\"FamilySize\"] = test['SibSp'] + test['Parch'] + 1"
   ]
  },
  {
   "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": []
   },
   "source": [
    "# **Correlations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(train.corr(),cbar='plasma')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": []
   },
   "outputs": [],
   "source": [
    "train.drop(['Name'], axis = 1, inplace=True)\n",
    "test.drop(['Name'], axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": []
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "</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",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <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",
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       "  </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"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": []
   },
   "outputs": [
    {
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       "      <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"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "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": []
   },
   "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": []
   },
   "source": [
    "**Now that our data looks good, lets get ready to build our models!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": []
   },
   "outputs": [],
   "source": [
    "#imports\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "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": []
   },
   "source": [
    "We should scale the values in the data, so that the neural network can train better!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": []
   },
   "outputs": [],
   "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))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": []
   },
   "outputs": [
    {
     "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>-8.772133e-17</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>2.232906e-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>4.386066e-17</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>5.382900e-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>3.987333e-18</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>-2.392400e-17</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       min       25%       50%  \\\n",
       "Pclass      891.0 -8.772133e-17  1.000562 -1.566107 -0.369365  0.827377   \n",
       "Sex         891.0  3.524130e-01  0.477990  0.000000  0.000000  0.000000   \n",
       "Age         891.0  2.232906e-16  1.000562 -2.253155 -0.592481  0.000000   \n",
       "SibSp       891.0  4.386066e-17  1.000562 -0.474545 -0.474545 -0.474545   \n",
       "Parch       891.0  5.382900e-17  1.000562 -0.473674 -0.473674 -0.473674   \n",
       "Fare        891.0  3.987333e-18  1.000562 -0.648422 -0.489148 -0.357391   \n",
       "Embarked    891.0  3.613917e-01  0.635673  0.000000  0.000000  0.000000   \n",
       "Title       891.0  3.591470e-01  0.480020  0.000000  0.000000  0.000000   \n",
       "FamilySize  891.0 -2.392400e-17  1.000562 -0.560975 -0.560975 -0.560975   \n",
       "\n",
       "                 75%       max  \n",
       "Pclass      0.827377  0.827377  \n",
       "Sex         1.000000  1.000000  \n",
       "Age         0.407926  3.870872  \n",
       "SibSp       0.432793  6.784163  \n",
       "Parch      -0.473674  6.974147  \n",
       "Fare       -0.024246  9.667167  \n",
       "Embarked    1.000000  2.000000  \n",
       "Title       1.000000  1.000000  \n",
       "FamilySize  0.059160  5.640372  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.describe(include='all').T\n"
   ]
  },
  {
   "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": []
   },
   "source": [
    "# **Neural Network**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.014215,
     "end_time": "2020-08-16T00:03:12.319324",
     "exception": false,
     "start_time": "2020-08-16T00:03:12.305109",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='main'></a>\n",
    "Here's the model! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": []
   },
   "outputs": [],
   "source": [
    "import keras\n",
    "import tensorflow as tf\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Activation, Dropout, Flatten, Conv2D,MaxPool2D\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "\n",
    "tf.keras.optimizers.SGD(\n",
    "    learning_rate=0.5, name='SGD'\n",
    ")\n",
    "#creating a model with sequential columns\n",
    "model = Sequential()\n",
    "\n",
    "#flattens the data into a 1d array\n",
    "model.add(Flatten())\n",
    "#creates the first latyer with the input dimanetion. \n",
    "\n",
    "model.add(Dense(4, input_dim=train.shape[1],kernel_initializer = 'uniform', activation='relu'))\n",
    "#next layer with 32 dense nodes\n",
    "model.add(Dense(4, kernel_initializer = 'uniform', activation = 'relu'))\n",
    "\n",
    "#last layer is initiated here\n",
    "model.add(Dense(4,kernel_initializer = 'uniform', activation = 'relu'))\n",
    "\n",
    "# create output layer\n",
    "    # Feel free to experiment with the activation functions and the optimizers\n",
    "model.add(Dense(1, activation='sigmoid'))  # output layer\n",
    "    \n",
    "model.compile(loss='binary_crossentropy', optimizer='SGD', metrics=['accuracy'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.01447,
     "end_time": "2020-08-16T00:03:15.177913",
     "exception": false,
     "start_time": "2020-08-16T00:03:15.163443",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Let's fit our model!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-16T00:03:15.255525Z",
     "iopub.status.busy": "2020-08-16T00:03:15.254916Z",
     "iopub.status.idle": "2020-08-16T00:03:18.280296Z",
     "shell.execute_reply": "2020-08-16T00:03:18.277527Z"
    },
    "papermill": {
     "duration": 3.047511,
     "end_time": "2020-08-16T00:03:18.280453",
     "exception": false,
     "start_time": "2020-08-16T00:03:15.232942",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "WARNING:tensorflow:Layer flatten is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because its dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "15/15 [==============================] - 0s 10ms/step - loss: 0.6920 - accuracy: 0.5899 - val_loss: 0.6896 - val_accuracy: 0.6425\n",
      "Epoch 2/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6894 - accuracy: 0.6096 - val_loss: 0.6868 - val_accuracy: 0.6425\n",
      "Epoch 3/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6874 - accuracy: 0.6096 - val_loss: 0.6838 - val_accuracy: 0.6425\n",
      "Epoch 4/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6853 - accuracy: 0.6096 - val_loss: 0.6807 - val_accuracy: 0.6425\n",
      "Epoch 5/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6831 - accuracy: 0.6096 - val_loss: 0.6782 - val_accuracy: 0.6425\n",
      "Epoch 6/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6814 - accuracy: 0.6096 - val_loss: 0.6762 - val_accuracy: 0.6425\n",
      "Epoch 7/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6801 - accuracy: 0.6096 - val_loss: 0.6741 - val_accuracy: 0.6425\n",
      "Epoch 8/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6787 - accuracy: 0.6096 - val_loss: 0.6724 - val_accuracy: 0.6425\n",
      "Epoch 9/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6776 - accuracy: 0.6096 - val_loss: 0.6712 - val_accuracy: 0.6425\n",
      "Epoch 10/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6768 - accuracy: 0.6096 - val_loss: 0.6698 - val_accuracy: 0.6425\n",
      "Epoch 11/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6760 - accuracy: 0.6096 - val_loss: 0.6685 - val_accuracy: 0.6425\n",
      "Epoch 12/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6752 - accuracy: 0.6096 - val_loss: 0.6670 - val_accuracy: 0.6425\n",
      "Epoch 13/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6743 - accuracy: 0.6096 - val_loss: 0.6658 - val_accuracy: 0.6425\n",
      "Epoch 14/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6736 - accuracy: 0.6096 - val_loss: 0.6648 - val_accuracy: 0.6425\n",
      "Epoch 15/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6730 - accuracy: 0.6096 - val_loss: 0.6641 - val_accuracy: 0.6425\n",
      "Epoch 16/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6727 - accuracy: 0.6096 - val_loss: 0.6633 - val_accuracy: 0.6425\n",
      "Epoch 17/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6723 - accuracy: 0.6096 - val_loss: 0.6625 - val_accuracy: 0.6425\n",
      "Epoch 18/20\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.6718 - accuracy: 0.6096 - val_loss: 0.6617 - val_accuracy: 0.6425\n",
      "Epoch 19/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6714 - accuracy: 0.6096 - val_loss: 0.6611 - val_accuracy: 0.6425\n",
      "Epoch 20/20\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6712 - accuracy: 0.6096 - val_loss: 0.6607 - val_accuracy: 0.6425\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train, y_train, epochs=20, batch_size=50, validation_split = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-16T00:03:18.351368Z",
     "iopub.status.busy": "2020-08-16T00:03:18.350307Z",
     "iopub.status.idle": "2020-08-16T00:03:18.359687Z",
     "shell.execute_reply": "2020-08-16T00:03:18.360395Z"
    },
    "papermill": {
     "duration": 0.051961,
     "end_time": "2020-08-16T00:03:18.360578",
     "exception": false,
     "start_time": "2020-08-16T00:03:18.308617",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 9)                 0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4)                 40        \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 4)                 20        \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 1)                 5         \n",
      "=================================================================\n",
      "Total params: 85\n",
      "Trainable params: 85\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.023642,
     "end_time": "2020-08-16T00:03:18.414997",
     "exception": false,
     "start_time": "2020-08-16T00:03:18.391355",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Evaluate the model!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-16T00:03:18.471648Z",
     "iopub.status.busy": "2020-08-16T00:03:18.470735Z",
     "iopub.status.idle": "2020-08-16T00:03:18.633492Z",
     "shell.execute_reply": "2020-08-16T00:03:18.636724Z"
    },
    "papermill": {
     "duration": 0.197758,
     "end_time": "2020-08-16T00:03:18.636917",
     "exception": false,
     "start_time": "2020-08-16T00:03:18.439159",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28/28 [==============================] - 0s 499us/step - loss: 0.6690 - accuracy: 0.6162\n"
     ]
    }
   ],
   "source": [
    "scores = model.evaluate(train, y_train, batch_size=32)"
   ]
  },
  {
   "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": []
   },
   "source": [
    "**Create Predictions**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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": []
   },
   "outputs": [],
   "source": [
    "y_pred = model.predict(test)\n",
    "\n",
    "y_final = (y_pred > 0.5).astype(int).reshape(test.shape[0])\n",
    "\n",
    "output = pd.DataFrame({'PassengerId': test_data['PassengerId'], 'Survived': y_final})\n",
    "output.to_csv('prediction-ann.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.026373,
     "end_time": "2020-08-16T00:03:19.115974",
     "exception": false,
     "start_time": "2020-08-16T00:03:19.089601",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# **Neural Network Analyasis**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.026135,
     "end_time": "2020-08-16T00:03:19.168875",
     "exception": false,
     "start_time": "2020-08-16T00:03:19.142740",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id='main2'></a>\n",
    "We can plot the loss, and val_loss & acc and val_acc\n",
    "The main purpose of this is to make sure the model follows a good trend, and to make sure that you are not overfitteing your model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-08-16T00:03:19.244170Z",
     "iopub.status.busy": "2020-08-16T00:03:19.240633Z",
     "iopub.status.idle": "2020-08-16T00:03:19.614096Z",
     "shell.execute_reply": "2020-08-16T00:03:19.613359Z"
    },
    "papermill": {
     "duration": 0.419401,
     "end_time": "2020-08-16T00:03:19.614231",
     "exception": false,
     "start_time": "2020-08-16T00:03:19.194830",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss_train = history.history['loss']\n",
    "loss_val = history.history['val_loss']\n",
    "epochs = range(1,21)\n",
    "plt.plot(epochs, loss_train, 'g', label='Training loss')\n",
    "plt.plot(epochs, loss_val, 'b', label='validation loss')\n",
    "plt.title('Training and Validation loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  },
  "papermill": {
   "duration": 22.836091,
   "end_time": "2020-08-16T00:03:21.705944",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2020-08-16T00:02:58.869853",
   "version": "2.1.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}