TitanicRandomForest_SudhakarKuma.ipynb

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   },
   "source": [
    "# **Introduction**"
   ]
  },
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   },
   "source": [
    "***Howdy, Welcome to the Titanic***"
   ]
  },
  {
   "cell_type": "markdown",
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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 implement Random Forests 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 Random Forest and then add code for writing the output to file `predictions-ann.csv` [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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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "test.csv\n",
      "train.csv\n",
      "gender_submission.csv\n",
      "ouput.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"
   ]
  },
  {
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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"
   ]
  },
  {
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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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       "      <th>Pclass</th>\n",
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       "      <td>26.0</td>\n",
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       "      <td>35.0</td>\n",
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       "   Pclass                                               Name     Sex   Age  \\\n",
       "0       3                            Braund, Mr. Owen Harris    male  22.0   \n",
       "1       1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "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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     "execution_count": 3,
     "metadata": {},
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   "source": [
    "train.head()"
   ]
  },
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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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\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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56e086ab90>"
      ]
     },
     "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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56e36e6f10>"
      ]
     },
     "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": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f56e0781cd0>"
      ]
     },
     "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,
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     "exception": false,
     "start_time": "2020-08-16T00:03:06.370449",
     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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      "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",
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     "exception": false,
     "start_time": "2020-08-16T00:03:06.416470",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "    <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",
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       "      <th>FamilySize</th>\n",
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       "      <td>0</td>\n",
       "      <td>7.8292</td>\n",
       "      <td>2</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>3</td>\n",
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      "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",
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     "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",
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     "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",
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     "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": [
    {
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       "</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       min       25%       50%  \\\n",
       "Pclass      891.0 -2.031048e-16  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  2.562796e-16  1.000562 -2.253155 -0.592481  0.000000   \n",
       "SibSp       891.0  3.456519e-16  1.000562 -0.474545 -0.474545 -0.474545   \n",
       "Parch       891.0  6.716164e-17  1.000562 -0.473674 -0.473674 -0.473674   \n",
       "Fare        891.0 -4.373606e-17  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  2.502051e-16  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": [
    "# **Random Forests**\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",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(668, 9) (223, 9)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(train, y_train, test_size = 0.25, \n",
    "                                                    stratify = y_train, random_state = 1)\n",
    "print(X_train.shape, X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=10,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=500,\n",
       "                       n_jobs=None, oob_score=False, random_state=1, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators = 500, criterion = 'gini', \n",
    "                            min_samples_split = 10,\n",
    "                            min_samples_leaf = 1,\n",
    "                            max_features = 'auto',\n",
    "                            random_state = 1)\n",
    "rf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9116766467065869 0.8251121076233184\n"
     ]
    }
   ],
   "source": [
    "train_score = rf.score(X_train, y_train)\n",
    "test_score = rf.score(X_test, y_test)\n",
    "print(train_score, test_score)"
   ]
  },
  {
   "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**\n",
    "<a id='main2'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "preds = rf.predict(test)\n",
    "# Preparing data for submission \n",
    "predictions = pd.DataFrame({'PassengerId':test_data['PassengerId'], 'Survived':preds})\n",
    "predictions.to_csv('predictions-ann.csv', index = False)"
   ]
  }
 ],
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