├── README.md
└── Predict_survival.ipynb


/README.md:
--------------------------------------------------------------------------------
 1 | # predict_survival
 2 | 
 3 | The data has been split into two groups:
 4 | 
 5 | training set (train.csv)
 6 | test set (test.csv)
 7 | The training set should be used to build your machine learning models. For the training set, we provide the outcome (also known as the “ground truth”) for each passenger. Your model will be based on “features” like passengers’ gender and class. You can also use feature engineering to create new features.
 8 | 
 9 | The test set should be used to see how well your model performs on unseen data. For the test set, we do not provide the ground truth for each passenger. It is your job to predict these outcomes. For each passenger in the test set, use the model you trained to predict whether or not they survived the sinking of the Titanic.
10 | 
11 | We also include 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.
12 | 


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/Predict_survival.ipynb:
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   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# Import data wrangling and analytics library"
   8 |    ]
   9 |   },
  10 |   {
  11 |    "cell_type": "code",
  12 |    "execution_count": 7,
  13 |    "metadata": {},
  14 |    "outputs": [],
  15 |    "source": [
  16 |     "import numpy as np\n",
  17 |     "import pandas as pd\n",
  18 |     "import matplotlib.pyplot as plt\n",
  19 |     "import seaborn as sns\n",
  20 |     "import os\n",
  21 |     "\n",
  22 |     "\n",
  23 |     "from sklearn.model_selection import train_test_split\n",
  24 |     "from sklearn.ensemble import RandomForestClassifier\n",
  25 |     "from sklearn.linear_model import LogisticRegression\n",
  26 |     "from sklearn.naive_bayes import GaussianNB\n",
  27 |     "from sklearn.tree import DecisionTreeClassifier\n",
  28 |     "from sklearn.neighbors import KNeighborsClassifier\n",
  29 |     "from sklearn.preprocessing import MinMaxScaler\n",
  30 |     "from sklearn import metrics\n",
  31 |     "from sklearn.preprocessing import LabelEncoder\n",
  32 |     "from sklearn.feature_selection import RFE\n",
  33 |     "from sklearn import metrics\n",
  34 |     "\n",
  35 |     "\n",
  36 |     "import warnings\n",
  37 |     "from sklearn.exceptions import DataConversionWarning\n",
  38 |     "warnings.filterwarnings(action='ignore', category=UserWarning)\n",
  39 |     "\n",
  40 |     "\n",
  41 |     "import warnings\n",
  42 |     "from sklearn.exceptions import DataConversionWarning\n",
  43 |     "warnings.filterwarnings(action='ignore', category=DataConversionWarning)\n",
  44 |     "\n",
  45 |     "import warnings\n",
  46 |     "from sklearn.exceptions import DataConversionWarning\n",
  47 |     "warnings.filterwarnings(action='ignore', category=DataConversionWarning)\n",
  48 |     "\n",
  49 |     "import warnings\n",
  50 |     "warnings.simplefilter(action='ignore', category=FutureWarning)\n"
  51 |    ]
  52 |   },
  53 |   {
  54 |    "cell_type": "markdown",
  55 |    "metadata": {},
  56 |    "source": [
  57 |     "# Data read and Analysis"
  58 |    ]
  59 |   },
  60 |   {
  61 |    "cell_type": "code",
  62 |    "execution_count": 8,
  63 |    "metadata": {},
  64 |    "outputs": [],
  65 |    "source": [
  66 |     "trainDf = pd.read_csv('train.csv')\n",
  67 |     "testDf = pd.read_csv('test.csv')"
  68 |    ]
  69 |   },
  70 |   {
  71 |    "cell_type": "code",
  72 |    "execution_count": 9,
  73 |    "metadata": {},
  74 |    "outputs": [
  75 |     {
  76 |      "data": {
  77 |       "text/html": [
  78 |        "<div>\n",
  79 |        "<style scoped>\n",
  80 |        "    .dataframe tbody tr th:only-of-type {\n",
  81 |        "        vertical-align: middle;\n",
  82 |        "    }\n",
  83 |        "\n",
  84 |        "    .dataframe tbody tr th {\n",
  85 |        "        vertical-align: top;\n",
  86 |        "    }\n",
  87 |        "\n",
  88 |        "    .dataframe thead th {\n",
  89 |        "        text-align: right;\n",
  90 |        "    }\n",
  91 |        "</style>\n",
  92 |        "<table border=\"1\" class=\"dataframe\">\n",
  93 |        "  <thead>\n",
  94 |        "    <tr style=\"text-align: right;\">\n",
  95 |        "      <th></th>\n",
  96 |        "      <th>PassengerId</th>\n",
  97 |        "      <th>Survived</th>\n",
  98 |        "      <th>Pclass</th>\n",
  99 |        "      <th>Name</th>\n",
 100 |        "      <th>Sex</th>\n",
 101 |        "      <th>Age</th>\n",
 102 |        "      <th>SibSp</th>\n",
 103 |        "      <th>Parch</th>\n",
 104 |        "      <th>Ticket</th>\n",
 105 |        "      <th>Fare</th>\n",
 106 |        "      <th>Cabin</th>\n",
 107 |        "      <th>Embarked</th>\n",
 108 |        "    </tr>\n",
 109 |        "  </thead>\n",
 110 |        "  <tbody>\n",
 111 |        "    <tr>\n",
 112 |        "      <th>0</th>\n",
 113 |        "      <td>1</td>\n",
 114 |        "      <td>0</td>\n",
 115 |        "      <td>3</td>\n",
 116 |        "      <td>Braund, Mr. Owen Harris</td>\n",
 117 |        "      <td>male</td>\n",
 118 |        "      <td>22.0</td>\n",
 119 |        "      <td>1</td>\n",
 120 |        "      <td>0</td>\n",
 121 |        "      <td>A/5 21171</td>\n",
 122 |        "      <td>7.2500</td>\n",
 123 |        "      <td>NaN</td>\n",
 124 |        "      <td>S</td>\n",
 125 |        "    </tr>\n",
 126 |        "    <tr>\n",
 127 |        "      <th>1</th>\n",
 128 |        "      <td>2</td>\n",
 129 |        "      <td>1</td>\n",
 130 |        "      <td>1</td>\n",
 131 |        "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
 132 |        "      <td>female</td>\n",
 133 |        "      <td>38.0</td>\n",
 134 |        "      <td>1</td>\n",
 135 |        "      <td>0</td>\n",
 136 |        "      <td>PC 17599</td>\n",
 137 |        "      <td>71.2833</td>\n",
 138 |        "      <td>C85</td>\n",
 139 |        "      <td>C</td>\n",
 140 |        "    </tr>\n",
 141 |        "    <tr>\n",
 142 |        "      <th>2</th>\n",
 143 |        "      <td>3</td>\n",
 144 |        "      <td>1</td>\n",
 145 |        "      <td>3</td>\n",
 146 |        "      <td>Heikkinen, Miss. Laina</td>\n",
 147 |        "      <td>female</td>\n",
 148 |        "      <td>26.0</td>\n",
 149 |        "      <td>0</td>\n",
 150 |        "      <td>0</td>\n",
 151 |        "      <td>STON/O2. 3101282</td>\n",
 152 |        "      <td>7.9250</td>\n",
 153 |        "      <td>NaN</td>\n",
 154 |        "      <td>S</td>\n",
 155 |        "    </tr>\n",
 156 |        "    <tr>\n",
 157 |        "      <th>3</th>\n",
 158 |        "      <td>4</td>\n",
 159 |        "      <td>1</td>\n",
 160 |        "      <td>1</td>\n",
 161 |        "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
 162 |        "      <td>female</td>\n",
 163 |        "      <td>35.0</td>\n",
 164 |        "      <td>1</td>\n",
 165 |        "      <td>0</td>\n",
 166 |        "      <td>113803</td>\n",
 167 |        "      <td>53.1000</td>\n",
 168 |        "      <td>C123</td>\n",
 169 |        "      <td>S</td>\n",
 170 |        "    </tr>\n",
 171 |        "    <tr>\n",
 172 |        "      <th>4</th>\n",
 173 |        "      <td>5</td>\n",
 174 |        "      <td>0</td>\n",
 175 |        "      <td>3</td>\n",
 176 |        "      <td>Allen, Mr. William Henry</td>\n",
 177 |        "      <td>male</td>\n",
 178 |        "      <td>35.0</td>\n",
 179 |        "      <td>0</td>\n",
 180 |        "      <td>0</td>\n",
 181 |        "      <td>373450</td>\n",
 182 |        "      <td>8.0500</td>\n",
 183 |        "      <td>NaN</td>\n",
 184 |        "      <td>S</td>\n",
 185 |        "    </tr>\n",
 186 |        "  </tbody>\n",
 187 |        "</table>\n",
 188 |        "</div>"
 189 |       ],
 190 |       "text/plain": [
 191 |        "   PassengerId  Survived  Pclass  \\\n",
 192 |        "0            1         0       3   \n",
 193 |        "1            2         1       1   \n",
 194 |        "2            3         1       3   \n",
 195 |        "3            4         1       1   \n",
 196 |        "4            5         0       3   \n",
 197 |        "\n",
 198 |        "                                                Name     Sex   Age  SibSp  \\\n",
 199 |        "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
 200 |        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
 201 |        "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
 202 |        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
 203 |        "4                           Allen, Mr. William Henry    male  35.0      0   \n",
 204 |        "\n",
 205 |        "   Parch            Ticket     Fare Cabin Embarked  \n",
 206 |        "0      0         A/5 21171   7.2500   NaN        S  \n",
 207 |        "1      0          PC 17599  71.2833   C85        C  \n",
 208 |        "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
 209 |        "3      0            113803  53.1000  C123        S  \n",
 210 |        "4      0            373450   8.0500   NaN        S  "
 211 |       ]
 212 |      },
 213 |      "execution_count": 9,
 214 |      "metadata": {},
 215 |      "output_type": "execute_result"
 216 |     }
 217 |    ],
 218 |    "source": [
 219 |     "trainDf.head()"
 220 |    ]
 221 |   },
 222 |   {
 223 |    "cell_type": "code",
 224 |    "execution_count": 10,
 225 |    "metadata": {},
 226 |    "outputs": [
 227 |     {
 228 |      "name": "stdout",
 229 |      "output_type": "stream",
 230 |      "text": [
 231 |       "<class 'pandas.core.frame.DataFrame'>\n",
 232 |       "RangeIndex: 891 entries, 0 to 890\n",
 233 |       "Data columns (total 12 columns):\n",
 234 |       " #   Column       Non-Null Count  Dtype  \n",
 235 |       "---  ------       --------------  -----  \n",
 236 |       " 0   PassengerId  891 non-null    int64  \n",
 237 |       " 1   Survived     891 non-null    int64  \n",
 238 |       " 2   Pclass       891 non-null    int64  \n",
 239 |       " 3   Name         891 non-null    object \n",
 240 |       " 4   Sex          891 non-null    object \n",
 241 |       " 5   Age          714 non-null    float64\n",
 242 |       " 6   SibSp        891 non-null    int64  \n",
 243 |       " 7   Parch        891 non-null    int64  \n",
 244 |       " 8   Ticket       891 non-null    object \n",
 245 |       " 9   Fare         891 non-null    float64\n",
 246 |       " 10  Cabin        204 non-null    object \n",
 247 |       " 11  Embarked     889 non-null    object \n",
 248 |       "dtypes: float64(2), int64(5), object(5)\n",
 249 |       "memory usage: 83.7+ KB\n"
 250 |      ]
 251 |     }
 252 |    ],
 253 |    "source": [
 254 |     "trainDf.info()"
 255 |    ]
 256 |   },
 257 |   {
 258 |    "cell_type": "code",
 259 |    "execution_count": 11,
 260 |    "metadata": {},
 261 |    "outputs": [
 262 |     {
 263 |      "name": "stdout",
 264 |      "output_type": "stream",
 265 |      "text": [
 266 |       "<class 'pandas.core.frame.DataFrame'>\n",
 267 |       "RangeIndex: 418 entries, 0 to 417\n",
 268 |       "Data columns (total 11 columns):\n",
 269 |       " #   Column       Non-Null Count  Dtype  \n",
 270 |       "---  ------       --------------  -----  \n",
 271 |       " 0   PassengerId  418 non-null    int64  \n",
 272 |       " 1   Pclass       418 non-null    int64  \n",
 273 |       " 2   Name         418 non-null    object \n",
 274 |       " 3   Sex          418 non-null    object \n",
 275 |       " 4   Age          332 non-null    float64\n",
 276 |       " 5   SibSp        418 non-null    int64  \n",
 277 |       " 6   Parch        418 non-null    int64  \n",
 278 |       " 7   Ticket       418 non-null    object \n",
 279 |       " 8   Fare         417 non-null    float64\n",
 280 |       " 9   Cabin        91 non-null     object \n",
 281 |       " 10  Embarked     418 non-null    object \n",
 282 |       "dtypes: float64(2), int64(4), object(5)\n",
 283 |       "memory usage: 36.0+ KB\n"
 284 |      ]
 285 |     }
 286 |    ],
 287 |    "source": [
 288 |     "testDf.info()"
 289 |    ]
 290 |   },
 291 |   {
 292 |    "cell_type": "code",
 293 |    "execution_count": 6,
 294 |    "metadata": {
 295 |     "scrolled": true
 296 |    },
 297 |    "outputs": [
 298 |     {
 299 |      "ename": "KeyboardInterrupt",
 300 |      "evalue": "",
 301 |      "output_type": "error",
 302 |      "traceback": [
 303 |       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
 304 |       "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
 305 |       "\u001b[1;32m<ipython-input-6-4d193c13d465>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mProfileReport\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mProfileReport\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrainDf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
 306 |       "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas_profiling\\__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconfig\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mConfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontroller\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas_decorator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile_report\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mProfileReport\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m__version__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
 307 |       "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas_profiling\\controller\\pandas_decorator.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprofile_report\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mProfileReport\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
 308 |       "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python37\\site-packages\\pandas_profiling\\profile_report.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m )\n\u001b[0;32m     19\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mserialize_report\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSerializeReport\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdataframe\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mhash_dataframe\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrename_index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     21\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mpandas_profiling\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpaths\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_config\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
 309 |       "\u001b[1;32m~\\anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load\u001b[1;34m(name, import_)\u001b[0m\n",
 310 |       "\u001b[1;32m~\\anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n",
 311 |       "\u001b[1;32m~\\anaconda3\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n",
 312 |       "\u001b[1;32m~\\anaconda3\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n",
 313 |       "\u001b[1;32m~\\anaconda3\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mget_code\u001b[1;34m(self, fullname)\u001b[0m\n",
 314 |       "\u001b[1;32m~\\anaconda3\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mget_data\u001b[1;34m(self, path)\u001b[0m\n",
 315 |       "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
 316 |      ]
 317 |     }
 318 |    ],
 319 |    "source": [
 320 |     "from pandas_profiling import ProfileReport\n",
 321 |     "ProfileReport(trainDf)"
 322 |    ]
 323 |   },
 324 |   {
 325 |    "cell_type": "markdown",
 326 |    "metadata": {},
 327 |    "source": [
 328 |     "# Data Cleaning"
 329 |    ]
 330 |   },
 331 |   {
 332 |    "cell_type": "code",
 333 |    "execution_count": 12,
 334 |    "metadata": {
 335 |     "scrolled": true
 336 |    },
 337 |    "outputs": [
 338 |     {
 339 |      "data": {
 340 |       "text/plain": [
 341 |        "PassengerId      0\n",
 342 |        "Survived         0\n",
 343 |        "Pclass           0\n",
 344 |        "Name             0\n",
 345 |        "Sex              0\n",
 346 |        "Age            177\n",
 347 |        "SibSp            0\n",
 348 |        "Parch            0\n",
 349 |        "Ticket           0\n",
 350 |        "Fare             0\n",
 351 |        "Cabin          687\n",
 352 |        "Embarked         2\n",
 353 |        "dtype: int64"
 354 |       ]
 355 |      },
 356 |      "execution_count": 12,
 357 |      "metadata": {},
 358 |      "output_type": "execute_result"
 359 |     }
 360 |    ],
 361 |    "source": [
 362 |     "trainDf.isna().sum()"
 363 |    ]
 364 |   },
 365 |   {
 366 |    "cell_type": "code",
 367 |    "execution_count": 13,
 368 |    "metadata": {
 369 |     "scrolled": true
 370 |    },
 371 |    "outputs": [
 372 |     {
 373 |      "data": {
 374 |       "text/plain": [
 375 |        "PassengerId      0\n",
 376 |        "Pclass           0\n",
 377 |        "Name             0\n",
 378 |        "Sex              0\n",
 379 |        "Age             86\n",
 380 |        "SibSp            0\n",
 381 |        "Parch            0\n",
 382 |        "Ticket           0\n",
 383 |        "Fare             1\n",
 384 |        "Cabin          327\n",
 385 |        "Embarked         0\n",
 386 |        "dtype: int64"
 387 |       ]
 388 |      },
 389 |      "execution_count": 13,
 390 |      "metadata": {},
 391 |      "output_type": "execute_result"
 392 |     }
 393 |    ],
 394 |    "source": [
 395 |     "testDf.isnull().sum()"
 396 |    ]
 397 |   },
 398 |   {
 399 |    "cell_type": "code",
 400 |    "execution_count": 14,
 401 |    "metadata": {},
 402 |    "outputs": [
 403 |     {
 404 |      "data": {
 405 |       "text/html": [
 406 |        "<div>\n",
 407 |        "<style scoped>\n",
 408 |        "    .dataframe tbody tr th:only-of-type {\n",
 409 |        "        vertical-align: middle;\n",
 410 |        "    }\n",
 411 |        "\n",
 412 |        "    .dataframe tbody tr th {\n",
 413 |        "        vertical-align: top;\n",
 414 |        "    }\n",
 415 |        "\n",
 416 |        "    .dataframe thead th {\n",
 417 |        "        text-align: right;\n",
 418 |        "    }\n",
 419 |        "</style>\n",
 420 |        "<table border=\"1\" class=\"dataframe\">\n",
 421 |        "  <thead>\n",
 422 |        "    <tr style=\"text-align: right;\">\n",
 423 |        "      <th></th>\n",
 424 |        "      <th>PassengerId</th>\n",
 425 |        "      <th>Survived</th>\n",
 426 |        "      <th>Pclass</th>\n",
 427 |        "      <th>Name</th>\n",
 428 |        "      <th>Sex</th>\n",
 429 |        "      <th>Age</th>\n",
 430 |        "      <th>SibSp</th>\n",
 431 |        "      <th>Parch</th>\n",
 432 |        "      <th>Ticket</th>\n",
 433 |        "      <th>Fare</th>\n",
 434 |        "      <th>Cabin</th>\n",
 435 |        "      <th>Embarked</th>\n",
 436 |        "    </tr>\n",
 437 |        "  </thead>\n",
 438 |        "  <tbody>\n",
 439 |        "    <tr>\n",
 440 |        "      <th>61</th>\n",
 441 |        "      <td>62</td>\n",
 442 |        "      <td>1</td>\n",
 443 |        "      <td>1</td>\n",
 444 |        "      <td>Icard, Miss. Amelie</td>\n",
 445 |        "      <td>female</td>\n",
 446 |        "      <td>38.0</td>\n",
 447 |        "      <td>0</td>\n",
 448 |        "      <td>0</td>\n",
 449 |        "      <td>113572</td>\n",
 450 |        "      <td>80.0</td>\n",
 451 |        "      <td>B28</td>\n",
 452 |        "      <td>NaN</td>\n",
 453 |        "    </tr>\n",
 454 |        "    <tr>\n",
 455 |        "      <th>829</th>\n",
 456 |        "      <td>830</td>\n",
 457 |        "      <td>1</td>\n",
 458 |        "      <td>1</td>\n",
 459 |        "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
 460 |        "      <td>female</td>\n",
 461 |        "      <td>62.0</td>\n",
 462 |        "      <td>0</td>\n",
 463 |        "      <td>0</td>\n",
 464 |        "      <td>113572</td>\n",
 465 |        "      <td>80.0</td>\n",
 466 |        "      <td>B28</td>\n",
 467 |        "      <td>NaN</td>\n",
 468 |        "    </tr>\n",
 469 |        "  </tbody>\n",
 470 |        "</table>\n",
 471 |        "</div>"
 472 |       ],
 473 |       "text/plain": [
 474 |        "     PassengerId  Survived  Pclass                                       Name  \\\n",
 475 |        "61            62         1       1                        Icard, Miss. Amelie   \n",
 476 |        "829          830         1       1  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
 477 |        "\n",
 478 |        "        Sex   Age  SibSp  Parch  Ticket  Fare Cabin Embarked  \n",
 479 |        "61   female  38.0      0      0  113572  80.0   B28      NaN  \n",
 480 |        "829  female  62.0      0      0  113572  80.0   B28      NaN  "
 481 |       ]
 482 |      },
 483 |      "execution_count": 14,
 484 |      "metadata": {},
 485 |      "output_type": "execute_result"
 486 |     }
 487 |    ],
 488 |    "source": [
 489 |     "trainDf[trainDf[\"Embarked\"].isnull()]"
 490 |    ]
 491 |   },
 492 |   {
 493 |    "cell_type": "code",
 494 |    "execution_count": 15,
 495 |    "metadata": {},
 496 |    "outputs": [
 497 |     {
 498 |      "data": {
 499 |       "text/html": [
 500 |        "<div>\n",
 501 |        "<style scoped>\n",
 502 |        "    .dataframe tbody tr th:only-of-type {\n",
 503 |        "        vertical-align: middle;\n",
 504 |        "    }\n",
 505 |        "\n",
 506 |        "    .dataframe tbody tr th {\n",
 507 |        "        vertical-align: top;\n",
 508 |        "    }\n",
 509 |        "\n",
 510 |        "    .dataframe thead th {\n",
 511 |        "        text-align: right;\n",
 512 |        "    }\n",
 513 |        "</style>\n",
 514 |        "<table border=\"1\" class=\"dataframe\">\n",
 515 |        "  <thead>\n",
 516 |        "    <tr style=\"text-align: right;\">\n",
 517 |        "      <th></th>\n",
 518 |        "      <th>PassengerId</th>\n",
 519 |        "      <th>Survived</th>\n",
 520 |        "      <th>Pclass</th>\n",
 521 |        "      <th>Name</th>\n",
 522 |        "      <th>Sex</th>\n",
 523 |        "      <th>Age</th>\n",
 524 |        "      <th>SibSp</th>\n",
 525 |        "      <th>Parch</th>\n",
 526 |        "      <th>Ticket</th>\n",
 527 |        "      <th>Fare</th>\n",
 528 |        "      <th>Cabin</th>\n",
 529 |        "      <th>Embarked</th>\n",
 530 |        "    </tr>\n",
 531 |        "  </thead>\n",
 532 |        "  <tbody>\n",
 533 |        "  </tbody>\n",
 534 |        "</table>\n",
 535 |        "</div>"
 536 |       ],
 537 |       "text/plain": [
 538 |        "Empty DataFrame\n",
 539 |        "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
 540 |        "Index: []"
 541 |       ]
 542 |      },
 543 |      "execution_count": 15,
 544 |      "metadata": {},
 545 |      "output_type": "execute_result"
 546 |     }
 547 |    ],
 548 |    "source": [
 549 |     "trainDf[\"Embarked\"] = trainDf[\"Embarked\"].fillna(\"C\")\n",
 550 |     "trainDf[trainDf[\"Embarked\"].isnull()]"
 551 |    ]
 552 |   },
 553 |   {
 554 |    "cell_type": "code",
 555 |    "execution_count": 16,
 556 |    "metadata": {},
 557 |    "outputs": [
 558 |     {
 559 |      "data": {
 560 |       "text/plain": [
 561 |        "PassengerId      0\n",
 562 |        "Survived         0\n",
 563 |        "Pclass           0\n",
 564 |        "Name             0\n",
 565 |        "Sex              0\n",
 566 |        "Age            177\n",
 567 |        "SibSp            0\n",
 568 |        "Parch            0\n",
 569 |        "Ticket           0\n",
 570 |        "Fare             0\n",
 571 |        "Cabin          687\n",
 572 |        "Embarked         0\n",
 573 |        "dtype: int64"
 574 |       ]
 575 |      },
 576 |      "execution_count": 16,
 577 |      "metadata": {},
 578 |      "output_type": "execute_result"
 579 |     }
 580 |    ],
 581 |    "source": [
 582 |     "trainDf.isnull().sum()"
 583 |    ]
 584 |   },
 585 |   {
 586 |    "cell_type": "code",
 587 |    "execution_count": 17,
 588 |    "metadata": {},
 589 |    "outputs": [
 590 |     {
 591 |      "data": {
 592 |       "text/html": [
 593 |        "<div>\n",
 594 |        "<style scoped>\n",
 595 |        "    .dataframe tbody tr th:only-of-type {\n",
 596 |        "        vertical-align: middle;\n",
 597 |        "    }\n",
 598 |        "\n",
 599 |        "    .dataframe tbody tr th {\n",
 600 |        "        vertical-align: top;\n",
 601 |        "    }\n",
 602 |        "\n",
 603 |        "    .dataframe thead th {\n",
 604 |        "        text-align: right;\n",
 605 |        "    }\n",
 606 |        "</style>\n",
 607 |        "<table border=\"1\" class=\"dataframe\">\n",
 608 |        "  <thead>\n",
 609 |        "    <tr style=\"text-align: right;\">\n",
 610 |        "      <th></th>\n",
 611 |        "      <th>PassengerId</th>\n",
 612 |        "      <th>Pclass</th>\n",
 613 |        "      <th>Name</th>\n",
 614 |        "      <th>Sex</th>\n",
 615 |        "      <th>Age</th>\n",
 616 |        "      <th>SibSp</th>\n",
 617 |        "      <th>Parch</th>\n",
 618 |        "      <th>Ticket</th>\n",
 619 |        "      <th>Fare</th>\n",
 620 |        "      <th>Cabin</th>\n",
 621 |        "      <th>Embarked</th>\n",
 622 |        "    </tr>\n",
 623 |        "  </thead>\n",
 624 |        "  <tbody>\n",
 625 |        "    <tr>\n",
 626 |        "      <th>152</th>\n",
 627 |        "      <td>1044</td>\n",
 628 |        "      <td>3</td>\n",
 629 |        "      <td>Storey, Mr. Thomas</td>\n",
 630 |        "      <td>male</td>\n",
 631 |        "      <td>60.5</td>\n",
 632 |        "      <td>0</td>\n",
 633 |        "      <td>0</td>\n",
 634 |        "      <td>3701</td>\n",
 635 |        "      <td>NaN</td>\n",
 636 |        "      <td>NaN</td>\n",
 637 |        "      <td>S</td>\n",
 638 |        "    </tr>\n",
 639 |        "  </tbody>\n",
 640 |        "</table>\n",
 641 |        "</div>"
 642 |       ],
 643 |       "text/plain": [
 644 |        "     PassengerId  Pclass                Name   Sex   Age  SibSp  Parch Ticket  \\\n",
 645 |        "152         1044       3  Storey, Mr. Thomas  male  60.5      0      0   3701   \n",
 646 |        "\n",
 647 |        "     Fare Cabin Embarked  \n",
 648 |        "152   NaN   NaN        S  "
 649 |       ]
 650 |      },
 651 |      "execution_count": 17,
 652 |      "metadata": {},
 653 |      "output_type": "execute_result"
 654 |     }
 655 |    ],
 656 |    "source": [
 657 |     "testDf[testDf[\"Fare\"].isnull()]"
 658 |    ]
 659 |   },
 660 |   {
 661 |    "cell_type": "code",
 662 |    "execution_count": 18,
 663 |    "metadata": {},
 664 |    "outputs": [
 665 |     {
 666 |      "data": {
 667 |       "text/html": [
 668 |        "<div>\n",
 669 |        "<style scoped>\n",
 670 |        "    .dataframe tbody tr th:only-of-type {\n",
 671 |        "        vertical-align: middle;\n",
 672 |        "    }\n",
 673 |        "\n",
 674 |        "    .dataframe tbody tr th {\n",
 675 |        "        vertical-align: top;\n",
 676 |        "    }\n",
 677 |        "\n",
 678 |        "    .dataframe thead th {\n",
 679 |        "        text-align: right;\n",
 680 |        "    }\n",
 681 |        "</style>\n",
 682 |        "<table border=\"1\" class=\"dataframe\">\n",
 683 |        "  <thead>\n",
 684 |        "    <tr style=\"text-align: right;\">\n",
 685 |        "      <th></th>\n",
 686 |        "      <th>PassengerId</th>\n",
 687 |        "      <th>Pclass</th>\n",
 688 |        "      <th>Name</th>\n",
 689 |        "      <th>Sex</th>\n",
 690 |        "      <th>Age</th>\n",
 691 |        "      <th>SibSp</th>\n",
 692 |        "      <th>Parch</th>\n",
 693 |        "      <th>Ticket</th>\n",
 694 |        "      <th>Fare</th>\n",
 695 |        "      <th>Cabin</th>\n",
 696 |        "      <th>Embarked</th>\n",
 697 |        "    </tr>\n",
 698 |        "  </thead>\n",
 699 |        "  <tbody>\n",
 700 |        "  </tbody>\n",
 701 |        "</table>\n",
 702 |        "</div>"
 703 |       ],
 704 |       "text/plain": [
 705 |        "Empty DataFrame\n",
 706 |        "Columns: [PassengerId, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
 707 |        "Index: []"
 708 |       ]
 709 |      },
 710 |      "execution_count": 18,
 711 |      "metadata": {},
 712 |      "output_type": "execute_result"
 713 |     }
 714 |    ],
 715 |    "source": [
 716 |     "testDf[\"Fare\"] = testDf[\"Fare\"].fillna(np.mean(testDf[testDf[\"Pclass\"] == 3][\"Fare\"]))\n",
 717 |     "testDf[testDf[\"Fare\"].isnull()]"
 718 |    ]
 719 |   },
 720 |   {
 721 |    "cell_type": "code",
 722 |    "execution_count": 19,
 723 |    "metadata": {},
 724 |    "outputs": [
 725 |     {
 726 |      "data": {
 727 |       "text/plain": [
 728 |        "PassengerId    0\n",
 729 |        "Survived       0\n",
 730 |        "Pclass         0\n",
 731 |        "Name           0\n",
 732 |        "Sex            0\n",
 733 |        "SibSp          0\n",
 734 |        "Parch          0\n",
 735 |        "Ticket         0\n",
 736 |        "Fare           0\n",
 737 |        "Embarked       0\n",
 738 |        "dtype: int64"
 739 |       ]
 740 |      },
 741 |      "execution_count": 19,
 742 |      "metadata": {},
 743 |      "output_type": "execute_result"
 744 |     }
 745 |    ],
 746 |    "source": [
 747 |     "trainDf.dropna(axis = 1, how = 'any', inplace = True)\n",
 748 |     "trainDf.isnull().sum()"
 749 |    ]
 750 |   },
 751 |   {
 752 |    "cell_type": "code",
 753 |    "execution_count": 20,
 754 |    "metadata": {},
 755 |    "outputs": [
 756 |     {
 757 |      "data": {
 758 |       "text/plain": [
 759 |        "PassengerId    0\n",
 760 |        "Pclass         0\n",
 761 |        "Name           0\n",
 762 |        "Sex            0\n",
 763 |        "SibSp          0\n",
 764 |        "Parch          0\n",
 765 |        "Ticket         0\n",
 766 |        "Fare           0\n",
 767 |        "Embarked       0\n",
 768 |        "dtype: int64"
 769 |       ]
 770 |      },
 771 |      "execution_count": 20,
 772 |      "metadata": {},
 773 |      "output_type": "execute_result"
 774 |     }
 775 |    ],
 776 |    "source": [
 777 |     "testDf.dropna(axis = 1, how = 'any', inplace = True)\n",
 778 |     "testDf.isnull().sum()"
 779 |    ]
 780 |   },
 781 |   {
 782 |    "cell_type": "code",
 783 |    "execution_count": 21,
 784 |    "metadata": {},
 785 |    "outputs": [
 786 |     {
 787 |      "data": {
 788 |       "text/html": [
 789 |        "<div>\n",
 790 |        "<style scoped>\n",
 791 |        "    .dataframe tbody tr th:only-of-type {\n",
 792 |        "        vertical-align: middle;\n",
 793 |        "    }\n",
 794 |        "\n",
 795 |        "    .dataframe tbody tr th {\n",
 796 |        "        vertical-align: top;\n",
 797 |        "    }\n",
 798 |        "\n",
 799 |        "    .dataframe thead th {\n",
 800 |        "        text-align: right;\n",
 801 |        "    }\n",
 802 |        "</style>\n",
 803 |        "<table border=\"1\" class=\"dataframe\">\n",
 804 |        "  <thead>\n",
 805 |        "    <tr style=\"text-align: right;\">\n",
 806 |        "      <th></th>\n",
 807 |        "      <th>PassengerId</th>\n",
 808 |        "      <th>Survived</th>\n",
 809 |        "      <th>Pclass</th>\n",
 810 |        "      <th>Name</th>\n",
 811 |        "      <th>Sex</th>\n",
 812 |        "      <th>SibSp</th>\n",
 813 |        "      <th>Parch</th>\n",
 814 |        "      <th>Ticket</th>\n",
 815 |        "      <th>Fare</th>\n",
 816 |        "      <th>Embarked</th>\n",
 817 |        "    </tr>\n",
 818 |        "  </thead>\n",
 819 |        "  <tbody>\n",
 820 |        "    <tr>\n",
 821 |        "      <th>0</th>\n",
 822 |        "      <td>1</td>\n",
 823 |        "      <td>0</td>\n",
 824 |        "      <td>3</td>\n",
 825 |        "      <td>Braund, Mr. Owen Harris</td>\n",
 826 |        "      <td>male</td>\n",
 827 |        "      <td>1</td>\n",
 828 |        "      <td>0</td>\n",
 829 |        "      <td>A/5 21171</td>\n",
 830 |        "      <td>7.2500</td>\n",
 831 |        "      <td>S</td>\n",
 832 |        "    </tr>\n",
 833 |        "    <tr>\n",
 834 |        "      <th>1</th>\n",
 835 |        "      <td>2</td>\n",
 836 |        "      <td>1</td>\n",
 837 |        "      <td>1</td>\n",
 838 |        "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
 839 |        "      <td>female</td>\n",
 840 |        "      <td>1</td>\n",
 841 |        "      <td>0</td>\n",
 842 |        "      <td>PC 17599</td>\n",
 843 |        "      <td>71.2833</td>\n",
 844 |        "      <td>C</td>\n",
 845 |        "    </tr>\n",
 846 |        "    <tr>\n",
 847 |        "      <th>2</th>\n",
 848 |        "      <td>3</td>\n",
 849 |        "      <td>1</td>\n",
 850 |        "      <td>3</td>\n",
 851 |        "      <td>Heikkinen, Miss. Laina</td>\n",
 852 |        "      <td>female</td>\n",
 853 |        "      <td>0</td>\n",
 854 |        "      <td>0</td>\n",
 855 |        "      <td>STON/O2. 3101282</td>\n",
 856 |        "      <td>7.9250</td>\n",
 857 |        "      <td>S</td>\n",
 858 |        "    </tr>\n",
 859 |        "    <tr>\n",
 860 |        "      <th>3</th>\n",
 861 |        "      <td>4</td>\n",
 862 |        "      <td>1</td>\n",
 863 |        "      <td>1</td>\n",
 864 |        "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
 865 |        "      <td>female</td>\n",
 866 |        "      <td>1</td>\n",
 867 |        "      <td>0</td>\n",
 868 |        "      <td>113803</td>\n",
 869 |        "      <td>53.1000</td>\n",
 870 |        "      <td>S</td>\n",
 871 |        "    </tr>\n",
 872 |        "    <tr>\n",
 873 |        "      <th>4</th>\n",
 874 |        "      <td>5</td>\n",
 875 |        "      <td>0</td>\n",
 876 |        "      <td>3</td>\n",
 877 |        "      <td>Allen, Mr. William Henry</td>\n",
 878 |        "      <td>male</td>\n",
 879 |        "      <td>0</td>\n",
 880 |        "      <td>0</td>\n",
 881 |        "      <td>373450</td>\n",
 882 |        "      <td>8.0500</td>\n",
 883 |        "      <td>S</td>\n",
 884 |        "    </tr>\n",
 885 |        "    <tr>\n",
 886 |        "      <th>5</th>\n",
 887 |        "      <td>6</td>\n",
 888 |        "      <td>0</td>\n",
 889 |        "      <td>3</td>\n",
 890 |        "      <td>Moran, Mr. James</td>\n",
 891 |        "      <td>male</td>\n",
 892 |        "      <td>0</td>\n",
 893 |        "      <td>0</td>\n",
 894 |        "      <td>330877</td>\n",
 895 |        "      <td>8.4583</td>\n",
 896 |        "      <td>Q</td>\n",
 897 |        "    </tr>\n",
 898 |        "    <tr>\n",
 899 |        "      <th>6</th>\n",
 900 |        "      <td>7</td>\n",
 901 |        "      <td>0</td>\n",
 902 |        "      <td>1</td>\n",
 903 |        "      <td>McCarthy, Mr. Timothy J</td>\n",
 904 |        "      <td>male</td>\n",
 905 |        "      <td>0</td>\n",
 906 |        "      <td>0</td>\n",
 907 |        "      <td>17463</td>\n",
 908 |        "      <td>51.8625</td>\n",
 909 |        "      <td>S</td>\n",
 910 |        "    </tr>\n",
 911 |        "    <tr>\n",
 912 |        "      <th>7</th>\n",
 913 |        "      <td>8</td>\n",
 914 |        "      <td>0</td>\n",
 915 |        "      <td>3</td>\n",
 916 |        "      <td>Palsson, Master. Gosta Leonard</td>\n",
 917 |        "      <td>male</td>\n",
 918 |        "      <td>3</td>\n",
 919 |        "      <td>1</td>\n",
 920 |        "      <td>349909</td>\n",
 921 |        "      <td>21.0750</td>\n",
 922 |        "      <td>S</td>\n",
 923 |        "    </tr>\n",
 924 |        "    <tr>\n",
 925 |        "      <th>8</th>\n",
 926 |        "      <td>9</td>\n",
 927 |        "      <td>1</td>\n",
 928 |        "      <td>3</td>\n",
 929 |        "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
 930 |        "      <td>female</td>\n",
 931 |        "      <td>0</td>\n",
 932 |        "      <td>2</td>\n",
 933 |        "      <td>347742</td>\n",
 934 |        "      <td>11.1333</td>\n",
 935 |        "      <td>S</td>\n",
 936 |        "    </tr>\n",
 937 |        "    <tr>\n",
 938 |        "      <th>9</th>\n",
 939 |        "      <td>10</td>\n",
 940 |        "      <td>1</td>\n",
 941 |        "      <td>2</td>\n",
 942 |        "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
 943 |        "      <td>female</td>\n",
 944 |        "      <td>1</td>\n",
 945 |        "      <td>0</td>\n",
 946 |        "      <td>237736</td>\n",
 947 |        "      <td>30.0708</td>\n",
 948 |        "      <td>C</td>\n",
 949 |        "    </tr>\n",
 950 |        "  </tbody>\n",
 951 |        "</table>\n",
 952 |        "</div>"
 953 |       ],
 954 |       "text/plain": [
 955 |        "   PassengerId  Survived  Pclass  \\\n",
 956 |        "0            1         0       3   \n",
 957 |        "1            2         1       1   \n",
 958 |        "2            3         1       3   \n",
 959 |        "3            4         1       1   \n",
 960 |        "4            5         0       3   \n",
 961 |        "5            6         0       3   \n",
 962 |        "6            7         0       1   \n",
 963 |        "7            8         0       3   \n",
 964 |        "8            9         1       3   \n",
 965 |        "9           10         1       2   \n",
 966 |        "\n",
 967 |        "                                                Name     Sex  SibSp  Parch  \\\n",
 968 |        "0                            Braund, Mr. Owen Harris    male      1      0   \n",
 969 |        "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female      1      0   \n",
 970 |        "2                             Heikkinen, Miss. Laina  female      0      0   \n",
 971 |        "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female      1      0   \n",
 972 |        "4                           Allen, Mr. William Henry    male      0      0   \n",
 973 |        "5                                   Moran, Mr. James    male      0      0   \n",
 974 |        "6                            McCarthy, Mr. Timothy J    male      0      0   \n",
 975 |        "7                     Palsson, Master. Gosta Leonard    male      3      1   \n",
 976 |        "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female      0      2   \n",
 977 |        "9                Nasser, Mrs. Nicholas (Adele Achem)  female      1      0   \n",
 978 |        "\n",
 979 |        "             Ticket     Fare Embarked  \n",
 980 |        "0         A/5 21171   7.2500        S  \n",
 981 |        "1          PC 17599  71.2833        C  \n",
 982 |        "2  STON/O2. 3101282   7.9250        S  \n",
 983 |        "3            113803  53.1000        S  \n",
 984 |        "4            373450   8.0500        S  \n",
 985 |        "5            330877   8.4583        Q  \n",
 986 |        "6             17463  51.8625        S  \n",
 987 |        "7            349909  21.0750        S  \n",
 988 |        "8            347742  11.1333        S  \n",
 989 |        "9            237736  30.0708        C  "
 990 |       ]
 991 |      },
 992 |      "execution_count": 21,
 993 |      "metadata": {},
 994 |      "output_type": "execute_result"
 995 |     }
 996 |    ],
 997 |    "source": [
 998 |     "trainDf.head(10)"
 999 |    ]
1000 |   },
1001 |   {
1002 |    "cell_type": "code",
1003 |    "execution_count": 22,
1004 |    "metadata": {},
1005 |    "outputs": [],
1006 |    "source": [
1007 |     "# set the PassengerId as index and drop the variable Name due to unique value \n",
1008 |     "trainDf.set_index('PassengerId', inplace=True)\n",
1009 |     "trainDf.drop(['Name', 'Ticket'], axis=1, inplace=True)"
1010 |    ]
1011 |   },
1012 |   {
1013 |    "cell_type": "code",
1014 |    "execution_count": 23,
1015 |    "metadata": {},
1016 |    "outputs": [],
1017 |    "source": [
1018 |     "# set the PassengerId as index and drop the variable Name due to unique value \n",
1019 |     "testDf.set_index('PassengerId', inplace=True)\n",
1020 |     "testDf.drop(['Name', 'Ticket'], axis=1, inplace=True)"
1021 |    ]
1022 |   },
1023 |   {
1024 |    "cell_type": "code",
1025 |    "execution_count": 24,
1026 |    "metadata": {},
1027 |    "outputs": [
1028 |     {
1029 |      "data": {
1030 |       "text/html": [
1031 |        "<div>\n",
1032 |        "<style scoped>\n",
1033 |        "    .dataframe tbody tr th:only-of-type {\n",
1034 |        "        vertical-align: middle;\n",
1035 |        "    }\n",
1036 |        "\n",
1037 |        "    .dataframe tbody tr th {\n",
1038 |        "        vertical-align: top;\n",
1039 |        "    }\n",
1040 |        "\n",
1041 |        "    .dataframe thead th {\n",
1042 |        "        text-align: right;\n",
1043 |        "    }\n",
1044 |        "</style>\n",
1045 |        "<table border=\"1\" class=\"dataframe\">\n",
1046 |        "  <thead>\n",
1047 |        "    <tr style=\"text-align: right;\">\n",
1048 |        "      <th></th>\n",
1049 |        "      <th>Survived</th>\n",
1050 |        "      <th>Pclass</th>\n",
1051 |        "      <th>Sex</th>\n",
1052 |        "      <th>SibSp</th>\n",
1053 |        "      <th>Parch</th>\n",
1054 |        "      <th>Fare</th>\n",
1055 |        "      <th>Embarked</th>\n",
1056 |        "    </tr>\n",
1057 |        "    <tr>\n",
1058 |        "      <th>PassengerId</th>\n",
1059 |        "      <th></th>\n",
1060 |        "      <th></th>\n",
1061 |        "      <th></th>\n",
1062 |        "      <th></th>\n",
1063 |        "      <th></th>\n",
1064 |        "      <th></th>\n",
1065 |        "      <th></th>\n",
1066 |        "    </tr>\n",
1067 |        "  </thead>\n",
1068 |        "  <tbody>\n",
1069 |        "    <tr>\n",
1070 |        "      <th>1</th>\n",
1071 |        "      <td>0</td>\n",
1072 |        "      <td>3</td>\n",
1073 |        "      <td>male</td>\n",
1074 |        "      <td>1</td>\n",
1075 |        "      <td>0</td>\n",
1076 |        "      <td>7.2500</td>\n",
1077 |        "      <td>S</td>\n",
1078 |        "    </tr>\n",
1079 |        "    <tr>\n",
1080 |        "      <th>2</th>\n",
1081 |        "      <td>1</td>\n",
1082 |        "      <td>1</td>\n",
1083 |        "      <td>female</td>\n",
1084 |        "      <td>1</td>\n",
1085 |        "      <td>0</td>\n",
1086 |        "      <td>71.2833</td>\n",
1087 |        "      <td>C</td>\n",
1088 |        "    </tr>\n",
1089 |        "    <tr>\n",
1090 |        "      <th>3</th>\n",
1091 |        "      <td>1</td>\n",
1092 |        "      <td>3</td>\n",
1093 |        "      <td>female</td>\n",
1094 |        "      <td>0</td>\n",
1095 |        "      <td>0</td>\n",
1096 |        "      <td>7.9250</td>\n",
1097 |        "      <td>S</td>\n",
1098 |        "    </tr>\n",
1099 |        "    <tr>\n",
1100 |        "      <th>4</th>\n",
1101 |        "      <td>1</td>\n",
1102 |        "      <td>1</td>\n",
1103 |        "      <td>female</td>\n",
1104 |        "      <td>1</td>\n",
1105 |        "      <td>0</td>\n",
1106 |        "      <td>53.1000</td>\n",
1107 |        "      <td>S</td>\n",
1108 |        "    </tr>\n",
1109 |        "    <tr>\n",
1110 |        "      <th>5</th>\n",
1111 |        "      <td>0</td>\n",
1112 |        "      <td>3</td>\n",
1113 |        "      <td>male</td>\n",
1114 |        "      <td>0</td>\n",
1115 |        "      <td>0</td>\n",
1116 |        "      <td>8.0500</td>\n",
1117 |        "      <td>S</td>\n",
1118 |        "    </tr>\n",
1119 |        "    <tr>\n",
1120 |        "      <th>...</th>\n",
1121 |        "      <td>...</td>\n",
1122 |        "      <td>...</td>\n",
1123 |        "      <td>...</td>\n",
1124 |        "      <td>...</td>\n",
1125 |        "      <td>...</td>\n",
1126 |        "      <td>...</td>\n",
1127 |        "      <td>...</td>\n",
1128 |        "    </tr>\n",
1129 |        "    <tr>\n",
1130 |        "      <th>887</th>\n",
1131 |        "      <td>0</td>\n",
1132 |        "      <td>2</td>\n",
1133 |        "      <td>male</td>\n",
1134 |        "      <td>0</td>\n",
1135 |        "      <td>0</td>\n",
1136 |        "      <td>13.0000</td>\n",
1137 |        "      <td>S</td>\n",
1138 |        "    </tr>\n",
1139 |        "    <tr>\n",
1140 |        "      <th>888</th>\n",
1141 |        "      <td>1</td>\n",
1142 |        "      <td>1</td>\n",
1143 |        "      <td>female</td>\n",
1144 |        "      <td>0</td>\n",
1145 |        "      <td>0</td>\n",
1146 |        "      <td>30.0000</td>\n",
1147 |        "      <td>S</td>\n",
1148 |        "    </tr>\n",
1149 |        "    <tr>\n",
1150 |        "      <th>889</th>\n",
1151 |        "      <td>0</td>\n",
1152 |        "      <td>3</td>\n",
1153 |        "      <td>female</td>\n",
1154 |        "      <td>1</td>\n",
1155 |        "      <td>2</td>\n",
1156 |        "      <td>23.4500</td>\n",
1157 |        "      <td>S</td>\n",
1158 |        "    </tr>\n",
1159 |        "    <tr>\n",
1160 |        "      <th>890</th>\n",
1161 |        "      <td>1</td>\n",
1162 |        "      <td>1</td>\n",
1163 |        "      <td>male</td>\n",
1164 |        "      <td>0</td>\n",
1165 |        "      <td>0</td>\n",
1166 |        "      <td>30.0000</td>\n",
1167 |        "      <td>C</td>\n",
1168 |        "    </tr>\n",
1169 |        "    <tr>\n",
1170 |        "      <th>891</th>\n",
1171 |        "      <td>0</td>\n",
1172 |        "      <td>3</td>\n",
1173 |        "      <td>male</td>\n",
1174 |        "      <td>0</td>\n",
1175 |        "      <td>0</td>\n",
1176 |        "      <td>7.7500</td>\n",
1177 |        "      <td>Q</td>\n",
1178 |        "    </tr>\n",
1179 |        "  </tbody>\n",
1180 |        "</table>\n",
1181 |        "<p>891 rows × 7 columns</p>\n",
1182 |        "</div>"
1183 |       ],
1184 |       "text/plain": [
1185 |        "             Survived  Pclass     Sex  SibSp  Parch     Fare Embarked\n",
1186 |        "PassengerId                                                          \n",
1187 |        "1                   0       3    male      1      0   7.2500        S\n",
1188 |        "2                   1       1  female      1      0  71.2833        C\n",
1189 |        "3                   1       3  female      0      0   7.9250        S\n",
1190 |        "4                   1       1  female      1      0  53.1000        S\n",
1191 |        "5                   0       3    male      0      0   8.0500        S\n",
1192 |        "...               ...     ...     ...    ...    ...      ...      ...\n",
1193 |        "887                 0       2    male      0      0  13.0000        S\n",
1194 |        "888                 1       1  female      0      0  30.0000        S\n",
1195 |        "889                 0       3  female      1      2  23.4500        S\n",
1196 |        "890                 1       1    male      0      0  30.0000        C\n",
1197 |        "891                 0       3    male      0      0   7.7500        Q\n",
1198 |        "\n",
1199 |        "[891 rows x 7 columns]"
1200 |       ]
1201 |      },
1202 |      "execution_count": 24,
1203 |      "metadata": {},
1204 |      "output_type": "execute_result"
1205 |     }
1206 |    ],
1207 |    "source": [
1208 |     "trainDf"
1209 |    ]
1210 |   },
1211 |   {
1212 |    "cell_type": "markdown",
1213 |    "metadata": {},
1214 |    "source": [
1215 |     "# Exploratory data analysis (EDA)"
1216 |    ]
1217 |   },
1218 |   {
1219 |    "cell_type": "code",
1220 |    "execution_count": 25,
1221 |    "metadata": {
1222 |     "scrolled": true
1223 |    },
1224 |    "outputs": [
1225 |     {
1226 |      "data": {
1227 |       "text/plain": [
1228 |        "<AxesSubplot:xlabel='Sex', ylabel='count'>"
1229 |       ]
1230 |      },
1231 |      "execution_count": 25,
1232 |      "metadata": {},
1233 |      "output_type": "execute_result"
1234 |     },
1235 |     {
1236 |      "data": {
1237 |       "image/png": 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\n",
1238 |       "text/plain": [
1239 |        "<Figure size 432x288 with 1 Axes>"
1240 |       ]
1241 |      },
1242 |      "metadata": {
1243 |       "needs_background": "light"
1244 |      },
1245 |      "output_type": "display_data"
1246 |     }
1247 |    ],
1248 |    "source": [
1249 |     "sns.countplot(trainDf['Sex'],  data=trainDf, )"
1250 |    ]
1251 |   },
1252 |   {
1253 |    "cell_type": "code",
1254 |    "execution_count": 26,
1255 |    "metadata": {},
1256 |    "outputs": [
1257 |     {
1258 |      "data": {
1259 |       "image/png": 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\n",
1260 |       "text/plain": [
1261 |        "<Figure size 432x288 with 1 Axes>"
1262 |       ]
1263 |      },
1264 |      "metadata": {
1265 |       "needs_background": "light"
1266 |      },
1267 |      "output_type": "display_data"
1268 |     }
1269 |    ],
1270 |    "source": [
1271 |     "sns.countplot('Sex',hue='Survived',data=trainDf)\n",
1272 |     "plt.show()"
1273 |    ]
1274 |   },
1275 |   {
1276 |    "cell_type": "code",
1277 |    "execution_count": 27,
1278 |    "metadata": {},
1279 |    "outputs": [
1280 |     {
1281 |      "data": {
1282 |       "text/plain": [
1283 |        "<AxesSubplot:xlabel='Survived', ylabel='count'>"
1284 |       ]
1285 |      },
1286 |      "execution_count": 27,
1287 |      "metadata": {},
1288 |      "output_type": "execute_result"
1289 |     },
1290 |     {
1291 |      "data": {
1292 |       "image/png": 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vKI3KUNCiV1UPAM8D1gNTwEVJXv8om22qqv9p7YuBV7X2acAlc4y/CHh1a5/ePuNQ4IXAJ5JcC/wN07MWgBcBH2/tC/bk+0iPxZJxFyAtBFX1IHAVcFWS64G1wA4e+h+nQ3ba5Huztt2W5O4kz2L6D/9vz/ERm4A/S3Ik0wF0BfBE4L6qOm5XZe3dt5H2njMFLXpJnp5k9ayu44DbgFuZ/gMO8BuPspuLgLcCh1XVdTuvbLORrzJ9WOjTVfVgVX0H+K8kp7Y6kuTZbZMvMT2jAHjtHn8paS8ZChIcCmxMclOS65g+X/B24B3AOUkmgQcfZR+XMP1H/OLdjLkIeF17n/FaYF2SrwM38tBPob4ROKvNWvwlPM0bL0mVJHXOFCRJnaEgSeoMBUlSZyhIkjpDQZLUGQqSpM5QkCR1/w/4gcjVwj04NgAAAABJRU5ErkJggg==\n",
1293 |       "text/plain": [
1294 |        "<Figure size 432x288 with 1 Axes>"
1295 |       ]
1296 |      },
1297 |      "metadata": {
1298 |       "needs_background": "light"
1299 |      },
1300 |      "output_type": "display_data"
1301 |     }
1302 |    ],
1303 |    "source": [
1304 |     "sns.countplot('Survived', data=trainDf)"
1305 |    ]
1306 |   },
1307 |   {
1308 |    "cell_type": "code",
1309 |    "execution_count": 28,
1310 |    "metadata": {},
1311 |    "outputs": [
1312 |     {
1313 |      "data": {
1314 |       "text/plain": [
1315 |        "<AxesSubplot:xlabel='Pclass', ylabel='count'>"
1316 |       ]
1317 |      },
1318 |      "execution_count": 28,
1319 |      "metadata": {},
1320 |      "output_type": "execute_result"
1321 |     },
1322 |     {
1323 |      "data": {
1324 |       "image/png": "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\n",
1325 |       "text/plain": [
1326 |        "<Figure size 432x288 with 1 Axes>"
1327 |       ]
1328 |      },
1329 |      "metadata": {
1330 |       "needs_background": "light"
1331 |      },
1332 |      "output_type": "display_data"
1333 |     }
1334 |    ],
1335 |    "source": [
1336 |     "sns.countplot('Pclass', data=trainDf)"
1337 |    ]
1338 |   },
1339 |   {
1340 |    "cell_type": "code",
1341 |    "execution_count": 29,
1342 |    "metadata": {},
1343 |    "outputs": [
1344 |     {
1345 |      "data": {
1346 |       "image/png": "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\n",
1347 |       "text/plain": [
1348 |        "<Figure size 432x288 with 1 Axes>"
1349 |       ]
1350 |      },
1351 |      "metadata": {
1352 |       "needs_background": "light"
1353 |      },
1354 |      "output_type": "display_data"
1355 |     }
1356 |    ],
1357 |    "source": [
1358 |     "sns.countplot('Embarked',hue='Survived',data=trainDf)\n",
1359 |     "plt.show()"
1360 |    ]
1361 |   },
1362 |   {
1363 |    "cell_type": "code",
1364 |    "execution_count": 30,
1365 |    "metadata": {},
1366 |    "outputs": [
1367 |     {
1368 |      "data": {
1369 |       "image/png": 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\n",
1370 |       "text/plain": [
1371 |        "<Figure size 1152x648 with 2 Axes>"
1372 |       ]
1373 |      },
1374 |      "metadata": {
1375 |       "needs_background": "light"
1376 |      },
1377 |      "output_type": "display_data"
1378 |     }
1379 |    ],
1380 |    "source": [
1381 |     "plt.figure(figsize=(16,9))\n",
1382 |     "sns.heatmap(trainDf.corr(), annot=True, cmap=\"cubehelix\")\n",
1383 |     "plt.show()"
1384 |    ]
1385 |   },
1386 |   {
1387 |    "cell_type": "code",
1388 |    "execution_count": 31,
1389 |    "metadata": {},
1390 |    "outputs": [
1391 |     {
1392 |      "data": {
1393 |       "text/plain": [
1394 |        "array([[<AxesSubplot:title={'center':'Survived'}>,\n",
1395 |        "        <AxesSubplot:title={'center':'Pclass'}>],\n",
1396 |        "       [<AxesSubplot:title={'center':'SibSp'}>,\n",
1397 |        "        <AxesSubplot:title={'center':'Parch'}>],\n",
1398 |        "       [<AxesSubplot:title={'center':'Fare'}>, <AxesSubplot:>]],\n",
1399 |        "      dtype=object)"
1400 |       ]
1401 |      },
1402 |      "execution_count": 31,
1403 |      "metadata": {},
1404 |      "output_type": "execute_result"
1405 |     },
1406 |     {
1407 |      "data": {
1408 |       "image/png": 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\n",
1409 |       "text/plain": [
1410 |        "<Figure size 1152x648 with 6 Axes>"
1411 |       ]
1412 |      },
1413 |      "metadata": {
1414 |       "needs_background": "light"
1415 |      },
1416 |      "output_type": "display_data"
1417 |     }
1418 |    ],
1419 |    "source": [
1420 |     "trainDf.hist(figsize=(16,9))"
1421 |    ]
1422 |   },
1423 |   {
1424 |    "cell_type": "markdown",
1425 |    "metadata": {},
1426 |    "source": [
1427 |     "# Data type transformation"
1428 |    ]
1429 |   },
1430 |   {
1431 |    "cell_type": "code",
1432 |    "execution_count": 32,
1433 |    "metadata": {},
1434 |    "outputs": [
1435 |     {
1436 |      "data": {
1437 |       "text/html": [
1438 |        "<div>\n",
1439 |        "<style scoped>\n",
1440 |        "    .dataframe tbody tr th:only-of-type {\n",
1441 |        "        vertical-align: middle;\n",
1442 |        "    }\n",
1443 |        "\n",
1444 |        "    .dataframe tbody tr th {\n",
1445 |        "        vertical-align: top;\n",
1446 |        "    }\n",
1447 |        "\n",
1448 |        "    .dataframe thead th {\n",
1449 |        "        text-align: right;\n",
1450 |        "    }\n",
1451 |        "</style>\n",
1452 |        "<table border=\"1\" class=\"dataframe\">\n",
1453 |        "  <thead>\n",
1454 |        "    <tr style=\"text-align: right;\">\n",
1455 |        "      <th></th>\n",
1456 |        "      <th>Survived</th>\n",
1457 |        "      <th>Pclass</th>\n",
1458 |        "      <th>Sex</th>\n",
1459 |        "      <th>SibSp</th>\n",
1460 |        "      <th>Parch</th>\n",
1461 |        "      <th>Fare</th>\n",
1462 |        "      <th>Embarked</th>\n",
1463 |        "    </tr>\n",
1464 |        "    <tr>\n",
1465 |        "      <th>PassengerId</th>\n",
1466 |        "      <th></th>\n",
1467 |        "      <th></th>\n",
1468 |        "      <th></th>\n",
1469 |        "      <th></th>\n",
1470 |        "      <th></th>\n",
1471 |        "      <th></th>\n",
1472 |        "      <th></th>\n",
1473 |        "    </tr>\n",
1474 |        "  </thead>\n",
1475 |        "  <tbody>\n",
1476 |        "    <tr>\n",
1477 |        "      <th>1</th>\n",
1478 |        "      <td>0</td>\n",
1479 |        "      <td>3</td>\n",
1480 |        "      <td>male</td>\n",
1481 |        "      <td>1</td>\n",
1482 |        "      <td>0</td>\n",
1483 |        "      <td>7.2500</td>\n",
1484 |        "      <td>S</td>\n",
1485 |        "    </tr>\n",
1486 |        "    <tr>\n",
1487 |        "      <th>2</th>\n",
1488 |        "      <td>1</td>\n",
1489 |        "      <td>1</td>\n",
1490 |        "      <td>female</td>\n",
1491 |        "      <td>1</td>\n",
1492 |        "      <td>0</td>\n",
1493 |        "      <td>71.2833</td>\n",
1494 |        "      <td>C</td>\n",
1495 |        "    </tr>\n",
1496 |        "    <tr>\n",
1497 |        "      <th>3</th>\n",
1498 |        "      <td>1</td>\n",
1499 |        "      <td>3</td>\n",
1500 |        "      <td>female</td>\n",
1501 |        "      <td>0</td>\n",
1502 |        "      <td>0</td>\n",
1503 |        "      <td>7.9250</td>\n",
1504 |        "      <td>S</td>\n",
1505 |        "    </tr>\n",
1506 |        "    <tr>\n",
1507 |        "      <th>4</th>\n",
1508 |        "      <td>1</td>\n",
1509 |        "      <td>1</td>\n",
1510 |        "      <td>female</td>\n",
1511 |        "      <td>1</td>\n",
1512 |        "      <td>0</td>\n",
1513 |        "      <td>53.1000</td>\n",
1514 |        "      <td>S</td>\n",
1515 |        "    </tr>\n",
1516 |        "    <tr>\n",
1517 |        "      <th>5</th>\n",
1518 |        "      <td>0</td>\n",
1519 |        "      <td>3</td>\n",
1520 |        "      <td>male</td>\n",
1521 |        "      <td>0</td>\n",
1522 |        "      <td>0</td>\n",
1523 |        "      <td>8.0500</td>\n",
1524 |        "      <td>S</td>\n",
1525 |        "    </tr>\n",
1526 |        "    <tr>\n",
1527 |        "      <th>6</th>\n",
1528 |        "      <td>0</td>\n",
1529 |        "      <td>3</td>\n",
1530 |        "      <td>male</td>\n",
1531 |        "      <td>0</td>\n",
1532 |        "      <td>0</td>\n",
1533 |        "      <td>8.4583</td>\n",
1534 |        "      <td>Q</td>\n",
1535 |        "    </tr>\n",
1536 |        "    <tr>\n",
1537 |        "      <th>7</th>\n",
1538 |        "      <td>0</td>\n",
1539 |        "      <td>1</td>\n",
1540 |        "      <td>male</td>\n",
1541 |        "      <td>0</td>\n",
1542 |        "      <td>0</td>\n",
1543 |        "      <td>51.8625</td>\n",
1544 |        "      <td>S</td>\n",
1545 |        "    </tr>\n",
1546 |        "    <tr>\n",
1547 |        "      <th>8</th>\n",
1548 |        "      <td>0</td>\n",
1549 |        "      <td>3</td>\n",
1550 |        "      <td>male</td>\n",
1551 |        "      <td>3</td>\n",
1552 |        "      <td>1</td>\n",
1553 |        "      <td>21.0750</td>\n",
1554 |        "      <td>S</td>\n",
1555 |        "    </tr>\n",
1556 |        "    <tr>\n",
1557 |        "      <th>9</th>\n",
1558 |        "      <td>1</td>\n",
1559 |        "      <td>3</td>\n",
1560 |        "      <td>female</td>\n",
1561 |        "      <td>0</td>\n",
1562 |        "      <td>2</td>\n",
1563 |        "      <td>11.1333</td>\n",
1564 |        "      <td>S</td>\n",
1565 |        "    </tr>\n",
1566 |        "    <tr>\n",
1567 |        "      <th>10</th>\n",
1568 |        "      <td>1</td>\n",
1569 |        "      <td>2</td>\n",
1570 |        "      <td>female</td>\n",
1571 |        "      <td>1</td>\n",
1572 |        "      <td>0</td>\n",
1573 |        "      <td>30.0708</td>\n",
1574 |        "      <td>C</td>\n",
1575 |        "    </tr>\n",
1576 |        "  </tbody>\n",
1577 |        "</table>\n",
1578 |        "</div>"
1579 |       ],
1580 |       "text/plain": [
1581 |        "             Survived  Pclass     Sex  SibSp  Parch     Fare Embarked\n",
1582 |        "PassengerId                                                          \n",
1583 |        "1                   0       3    male      1      0   7.2500        S\n",
1584 |        "2                   1       1  female      1      0  71.2833        C\n",
1585 |        "3                   1       3  female      0      0   7.9250        S\n",
1586 |        "4                   1       1  female      1      0  53.1000        S\n",
1587 |        "5                   0       3    male      0      0   8.0500        S\n",
1588 |        "6                   0       3    male      0      0   8.4583        Q\n",
1589 |        "7                   0       1    male      0      0  51.8625        S\n",
1590 |        "8                   0       3    male      3      1  21.0750        S\n",
1591 |        "9                   1       3  female      0      2  11.1333        S\n",
1592 |        "10                  1       2  female      1      0  30.0708        C"
1593 |       ]
1594 |      },
1595 |      "execution_count": 32,
1596 |      "metadata": {},
1597 |      "output_type": "execute_result"
1598 |     }
1599 |    ],
1600 |    "source": [
1601 |     "trainDf.head(10)"
1602 |    ]
1603 |   },
1604 |   {
1605 |    "cell_type": "code",
1606 |    "execution_count": 33,
1607 |    "metadata": {},
1608 |    "outputs": [
1609 |     {
1610 |      "data": {
1611 |       "text/html": [
1612 |        "<div>\n",
1613 |        "<style scoped>\n",
1614 |        "    .dataframe tbody tr th:only-of-type {\n",
1615 |        "        vertical-align: middle;\n",
1616 |        "    }\n",
1617 |        "\n",
1618 |        "    .dataframe tbody tr th {\n",
1619 |        "        vertical-align: top;\n",
1620 |        "    }\n",
1621 |        "\n",
1622 |        "    .dataframe thead th {\n",
1623 |        "        text-align: right;\n",
1624 |        "    }\n",
1625 |        "</style>\n",
1626 |        "<table border=\"1\" class=\"dataframe\">\n",
1627 |        "  <thead>\n",
1628 |        "    <tr style=\"text-align: right;\">\n",
1629 |        "      <th></th>\n",
1630 |        "      <th>Pclass</th>\n",
1631 |        "      <th>Sex</th>\n",
1632 |        "      <th>SibSp</th>\n",
1633 |        "      <th>Parch</th>\n",
1634 |        "      <th>Fare</th>\n",
1635 |        "      <th>Embarked</th>\n",
1636 |        "    </tr>\n",
1637 |        "    <tr>\n",
1638 |        "      <th>PassengerId</th>\n",
1639 |        "      <th></th>\n",
1640 |        "      <th></th>\n",
1641 |        "      <th></th>\n",
1642 |        "      <th></th>\n",
1643 |        "      <th></th>\n",
1644 |        "      <th></th>\n",
1645 |        "    </tr>\n",
1646 |        "  </thead>\n",
1647 |        "  <tbody>\n",
1648 |        "    <tr>\n",
1649 |        "      <th>892</th>\n",
1650 |        "      <td>3</td>\n",
1651 |        "      <td>male</td>\n",
1652 |        "      <td>0</td>\n",
1653 |        "      <td>0</td>\n",
1654 |        "      <td>7.8292</td>\n",
1655 |        "      <td>Q</td>\n",
1656 |        "    </tr>\n",
1657 |        "    <tr>\n",
1658 |        "      <th>893</th>\n",
1659 |        "      <td>3</td>\n",
1660 |        "      <td>female</td>\n",
1661 |        "      <td>1</td>\n",
1662 |        "      <td>0</td>\n",
1663 |        "      <td>7.0000</td>\n",
1664 |        "      <td>S</td>\n",
1665 |        "    </tr>\n",
1666 |        "    <tr>\n",
1667 |        "      <th>894</th>\n",
1668 |        "      <td>2</td>\n",
1669 |        "      <td>male</td>\n",
1670 |        "      <td>0</td>\n",
1671 |        "      <td>0</td>\n",
1672 |        "      <td>9.6875</td>\n",
1673 |        "      <td>Q</td>\n",
1674 |        "    </tr>\n",
1675 |        "    <tr>\n",
1676 |        "      <th>895</th>\n",
1677 |        "      <td>3</td>\n",
1678 |        "      <td>male</td>\n",
1679 |        "      <td>0</td>\n",
1680 |        "      <td>0</td>\n",
1681 |        "      <td>8.6625</td>\n",
1682 |        "      <td>S</td>\n",
1683 |        "    </tr>\n",
1684 |        "    <tr>\n",
1685 |        "      <th>896</th>\n",
1686 |        "      <td>3</td>\n",
1687 |        "      <td>female</td>\n",
1688 |        "      <td>1</td>\n",
1689 |        "      <td>1</td>\n",
1690 |        "      <td>12.2875</td>\n",
1691 |        "      <td>S</td>\n",
1692 |        "    </tr>\n",
1693 |        "  </tbody>\n",
1694 |        "</table>\n",
1695 |        "</div>"
1696 |       ],
1697 |       "text/plain": [
1698 |        "             Pclass     Sex  SibSp  Parch     Fare Embarked\n",
1699 |        "PassengerId                                                \n",
1700 |        "892               3    male      0      0   7.8292        Q\n",
1701 |        "893               3  female      1      0   7.0000        S\n",
1702 |        "894               2    male      0      0   9.6875        Q\n",
1703 |        "895               3    male      0      0   8.6625        S\n",
1704 |        "896               3  female      1      1  12.2875        S"
1705 |       ]
1706 |      },
1707 |      "execution_count": 33,
1708 |      "metadata": {},
1709 |      "output_type": "execute_result"
1710 |     }
1711 |    ],
1712 |    "source": [
1713 |     "testDf.head()"
1714 |    ]
1715 |   },
1716 |   {
1717 |    "cell_type": "code",
1718 |    "execution_count": 34,
1719 |    "metadata": {},
1720 |    "outputs": [],
1721 |    "source": [
1722 |     "le = LabelEncoder()\n",
1723 |     "# dtype transform of train dataset\n",
1724 |     "trainDf.Sex = le.fit_transform(trainDf.Sex)\n",
1725 |     "trainDf.Embarked = le.fit_transform(trainDf.Embarked)\n",
1726 |     "\n",
1727 |     "# dtype transform of test dataset\n",
1728 |     "testDf.Sex = le.fit_transform(testDf.Sex)\n",
1729 |     "testDf.Embarked = le.fit_transform(testDf.Embarked)"
1730 |    ]
1731 |   },
1732 |   {
1733 |    "cell_type": "markdown",
1734 |    "metadata": {},
1735 |    "source": [
1736 |     "# Feature Ranking"
1737 |    ]
1738 |   },
1739 |   {
1740 |    "cell_type": "code",
1741 |    "execution_count": 35,
1742 |    "metadata": {},
1743 |    "outputs": [],
1744 |    "source": [
1745 |     "# Extract the input variable and target variable\n",
1746 |     "X = trainDf.drop('Survived', axis=1)\n",
1747 |     "\n",
1748 |     "Y = trainDf[['Survived']]\n",
1749 |     "\n",
1750 |     "# Store the column/feature names into a list \"colnames\"\n",
1751 |     "colnames = list(trainDf.drop('Survived', axis=1))"
1752 |    ]
1753 |   },
1754 |   {
1755 |    "cell_type": "code",
1756 |    "execution_count": 36,
1757 |    "metadata": {},
1758 |    "outputs": [],
1759 |    "source": [
1760 |     "# Define dictionary to store our rankings\n",
1761 |     "ranks = {}\n",
1762 |     "# Create our function which stores the feature rankings to the ranks dictionary\n",
1763 |     "def ranking(ranks, names, order=1):\n",
1764 |     "    minmax = MinMaxScaler()\n",
1765 |     "    ranks = minmax.fit_transform(order*np.array([ranks]).T).T[0]\n",
1766 |     "    ranks = map(lambda x: round(x,2), ranks)\n",
1767 |     "    return dict(zip(names, ranks))"
1768 |    ]
1769 |   },
1770 |   {
1771 |    "cell_type": "code",
1772 |    "execution_count": 37,
1773 |    "metadata": {},
1774 |    "outputs": [],
1775 |    "source": [
1776 |     "# Construct Recursive Feature Elimination ( RFE ) of the Logistic Regression model\n",
1777 |     "lr = LogisticRegression(random_state= 42)  #lr = LinearRegression(normalize=True)\n",
1778 |     "lr.fit(X,Y)\n",
1779 |     "\n",
1780 |     "#stop the search when only the last feature is left\n",
1781 |     "rfe = RFE(lr, n_features_to_select=1, ) #verbose =3\n",
1782 |     "rfe.fit(X,Y)\n",
1783 |     "ranks[\"RFE\"] = ranking(list(map(float, rfe.ranking_)), colnames, order=-1)"
1784 |    ]
1785 |   },
1786 |   {
1787 |    "cell_type": "code",
1788 |    "execution_count": 38,
1789 |    "metadata": {},
1790 |    "outputs": [
1791 |     {
1792 |      "data": {
1793 |       "text/plain": [
1794 |        "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
1795 |        "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
1796 |        "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
1797 |        "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
1798 |        "                   warm_start=False)"
1799 |       ]
1800 |      },
1801 |      "execution_count": 38,
1802 |      "metadata": {},
1803 |      "output_type": "execute_result"
1804 |     }
1805 |    ],
1806 |    "source": [
1807 |     "# Using Logistic Regression\n",
1808 |     "lr = LogisticRegression()\n",
1809 |     "lr.fit(X,Y)\n",
1810 |     "\n",
1811 |     "#ranks[\"LogReg\"] = ranking(np.abs(lr.coef_), colnames)"
1812 |    ]
1813 |   },
1814 |   {
1815 |    "cell_type": "code",
1816 |    "execution_count": 39,
1817 |    "metadata": {},
1818 |    "outputs": [],
1819 |    "source": [
1820 |     "# Decision Tree Classifier\n",
1821 |     "\n",
1822 |     "dt = DecisionTreeClassifier()\n",
1823 |     "dt.fit(X,Y)\n",
1824 |     "ranks[\"DT\"] = ranking(dt.feature_importances_, colnames)"
1825 |    ]
1826 |   },
1827 |   {
1828 |    "cell_type": "code",
1829 |    "execution_count": 40,
1830 |    "metadata": {},
1831 |    "outputs": [],
1832 |    "source": [
1833 |     "# Random Forest Classifier\n",
1834 |     "\n",
1835 |     "rf = RandomForestClassifier(n_jobs=-1, n_estimators=9, ) #verbose=3\n",
1836 |     "rf.fit(X,Y)\n",
1837 |     "ranks[\"RF\"] = ranking(rf.feature_importances_, colnames)"
1838 |    ]
1839 |   },
1840 |   {
1841 |    "cell_type": "markdown",
1842 |    "metadata": {},
1843 |    "source": [
1844 |     "# Creating the Feature Ranking Matrix\n",
1845 |     "We combine the scores from the various methods above and output it in a matrix form for convenient viewing as such:"
1846 |    ]
1847 |   },
1848 |   {
1849 |    "cell_type": "code",
1850 |    "execution_count": 41,
1851 |    "metadata": {},
1852 |    "outputs": [
1853 |     {
1854 |      "name": "stdout",
1855 |      "output_type": "stream",
1856 |      "text": [
1857 |       "\tDT\tRF\tRFE\tMean\n",
1858 |       "Pclass\t0.22\t0.25\t0.8\t0.42\n",
1859 |       "Sex\t1.0\t0.69\t1.0\t0.9\n",
1860 |       "SibSp\t0.1\t0.03\t0.4\t0.18\n",
1861 |       "Parch\t0.06\t0.07\t0.2\t0.11\n",
1862 |       "Fare\t0.92\t1.0\t0.0\t0.64\n",
1863 |       "Embarked\t0.0\t0.0\t0.6\t0.2\n"
1864 |      ]
1865 |     }
1866 |    ],
1867 |    "source": [
1868 |     "# Create empty dictionary to store the mean value calculated from all the scores\n",
1869 |     "r = {}\n",
1870 |     "for name in colnames:\n",
1871 |     "    \n",
1872 |     "    r[name] = round(np.mean([ranks[method][name] for method in ranks.keys()]), 2)\n",
1873 |     " \n",
1874 |     "methods = sorted(ranks.keys())\n",
1875 |     "ranks[\"Mean\"] = r\n",
1876 |     "methods.append(\"Mean\")\n",
1877 |     " \n",
1878 |     "print(\"\\t%s\" % \"\\t\".join(methods))\n",
1879 |     "for name in colnames:\n",
1880 |     "    print(\"%s\\t%s\" % (name, \"\\t\".join(map(str, [ranks[method][name] for method in methods]))))"
1881 |    ]
1882 |   },
1883 |   {
1884 |    "cell_type": "code",
1885 |    "execution_count": 42,
1886 |    "metadata": {},
1887 |    "outputs": [],
1888 |    "source": [
1889 |     "# Put the mean scores into a Pandas dataframe\n",
1890 |     "meanplot = pd.DataFrame(list(r.items()), columns= ['Feature','Mean Ranking'])\n",
1891 |     "\n",
1892 |     "# Sort the dataframe\n",
1893 |     "meanplot = meanplot.sort_values('Mean Ranking', ascending=False)"
1894 |    ]
1895 |   },
1896 |   {
1897 |    "cell_type": "code",
1898 |    "execution_count": 43,
1899 |    "metadata": {
1900 |     "scrolled": true
1901 |    },
1902 |    "outputs": [
1903 |     {
1904 |      "data": {
1905 |       "text/plain": [
1906 |        "<seaborn.axisgrid.FacetGrid at 0x26de58f0d08>"
1907 |       ]
1908 |      },
1909 |      "execution_count": 43,
1910 |      "metadata": {},
1911 |      "output_type": "execute_result"
1912 |     },
1913 |     {
1914 |      "data": {
1915 |       "image/png": 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\n",
1916 |       "text/plain": [
1917 |        "<Figure size 684x360 with 1 Axes>"
1918 |       ]
1919 |      },
1920 |      "metadata": {
1921 |       "needs_background": "light"
1922 |      },
1923 |      "output_type": "display_data"
1924 |     }
1925 |    ],
1926 |    "source": [
1927 |     "# Let's plot the ranking of the features\n",
1928 |     "sns.factorplot(x=\"Mean Ranking\", y=\"Feature\", data = meanplot, kind=\"bar\", \n",
1929 |     "               size=5, aspect=1.9, palette='coolwarm')"
1930 |    ]
1931 |   },
1932 |   {
1933 |    "cell_type": "code",
1934 |    "execution_count": 44,
1935 |    "metadata": {},
1936 |    "outputs": [
1937 |     {
1938 |      "data": {
1939 |       "text/html": [
1940 |        "<div>\n",
1941 |        "<style scoped>\n",
1942 |        "    .dataframe tbody tr th:only-of-type {\n",
1943 |        "        vertical-align: middle;\n",
1944 |        "    }\n",
1945 |        "\n",
1946 |        "    .dataframe tbody tr th {\n",
1947 |        "        vertical-align: top;\n",
1948 |        "    }\n",
1949 |        "\n",
1950 |        "    .dataframe thead th {\n",
1951 |        "        text-align: right;\n",
1952 |        "    }\n",
1953 |        "</style>\n",
1954 |        "<table border=\"1\" class=\"dataframe\">\n",
1955 |        "  <thead>\n",
1956 |        "    <tr style=\"text-align: right;\">\n",
1957 |        "      <th></th>\n",
1958 |        "      <th>Feature</th>\n",
1959 |        "      <th>Mean Ranking</th>\n",
1960 |        "    </tr>\n",
1961 |        "  </thead>\n",
1962 |        "  <tbody>\n",
1963 |        "    <tr>\n",
1964 |        "      <th>1</th>\n",
1965 |        "      <td>Sex</td>\n",
1966 |        "      <td>0.90</td>\n",
1967 |        "    </tr>\n",
1968 |        "    <tr>\n",
1969 |        "      <th>4</th>\n",
1970 |        "      <td>Fare</td>\n",
1971 |        "      <td>0.64</td>\n",
1972 |        "    </tr>\n",
1973 |        "    <tr>\n",
1974 |        "      <th>0</th>\n",
1975 |        "      <td>Pclass</td>\n",
1976 |        "      <td>0.42</td>\n",
1977 |        "    </tr>\n",
1978 |        "    <tr>\n",
1979 |        "      <th>5</th>\n",
1980 |        "      <td>Embarked</td>\n",
1981 |        "      <td>0.20</td>\n",
1982 |        "    </tr>\n",
1983 |        "    <tr>\n",
1984 |        "      <th>2</th>\n",
1985 |        "      <td>SibSp</td>\n",
1986 |        "      <td>0.18</td>\n",
1987 |        "    </tr>\n",
1988 |        "    <tr>\n",
1989 |        "      <th>3</th>\n",
1990 |        "      <td>Parch</td>\n",
1991 |        "      <td>0.11</td>\n",
1992 |        "    </tr>\n",
1993 |        "  </tbody>\n",
1994 |        "</table>\n",
1995 |        "</div>"
1996 |       ],
1997 |       "text/plain": [
1998 |        "    Feature  Mean Ranking\n",
1999 |        "1       Sex          0.90\n",
2000 |        "4      Fare          0.64\n",
2001 |        "0    Pclass          0.42\n",
2002 |        "5  Embarked          0.20\n",
2003 |        "2     SibSp          0.18\n",
2004 |        "3     Parch          0.11"
2005 |       ]
2006 |      },
2007 |      "execution_count": 44,
2008 |      "metadata": {},
2009 |      "output_type": "execute_result"
2010 |     }
2011 |    ],
2012 |    "source": [
2013 |     "meanplot = meanplot.sort_values('Mean Ranking', ascending=False)\n",
2014 |     "meanplot"
2015 |    ]
2016 |   },
2017 |   {
2018 |    "cell_type": "code",
2019 |    "execution_count": 45,
2020 |    "metadata": {},
2021 |    "outputs": [
2022 |     {
2023 |      "data": {
2024 |       "text/plain": [
2025 |        "['Sex', 'Fare', 'Pclass', 'Embarked']"
2026 |       ]
2027 |      },
2028 |      "execution_count": 45,
2029 |      "metadata": {},
2030 |      "output_type": "execute_result"
2031 |     }
2032 |    ],
2033 |    "source": [
2034 |     "columnName = meanplot.loc[meanplot['Mean Ranking']  >= 0.20]\n",
2035 |     "columnName = list(columnName.Feature)\n",
2036 |     "columnName"
2037 |    ]
2038 |   },
2039 |   {
2040 |    "cell_type": "markdown",
2041 |    "metadata": {},
2042 |    "source": [
2043 |     "# Predictive Modeling"
2044 |    ]
2045 |   },
2046 |   {
2047 |    "cell_type": "code",
2048 |    "execution_count": 46,
2049 |    "metadata": {},
2050 |    "outputs": [],
2051 |    "source": [
2052 |     "# Extract the input variable and target variable\n",
2053 |     "X = trainDf[columnName]\n",
2054 |     "y = trainDf[['Survived']]\n",
2055 |     "\n",
2056 |     "testDf = testDf[columnName]\n"
2057 |    ]
2058 |   },
2059 |   {
2060 |    "cell_type": "code",
2061 |    "execution_count": 47,
2062 |    "metadata": {},
2063 |    "outputs": [],
2064 |    "source": [
2065 |     "# split original data [i.e X and y] into 70:30 \n",
2066 |     "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
2067 |    ]
2068 |   },
2069 |   {
2070 |    "cell_type": "markdown",
2071 |    "metadata": {},
2072 |    "source": [
2073 |     "### Utility functions"
2074 |    ]
2075 |   },
2076 |   {
2077 |    "cell_type": "code",
2078 |    "execution_count": 48,
2079 |    "metadata": {},
2080 |    "outputs": [],
2081 |    "source": [
2082 |     "def classifier_report(ModelName, model_object):\n",
2083 |     "    model_object.fit(X_train, y_train)\n",
2084 |     "    y_test_pred = model_object.predict(X_test)\n",
2085 |     "    print(ModelName, \"Classifier Report:\")\n",
2086 |     "    print(\"\\n\", metrics.classification_report(y_test, y_test_pred))\n",
2087 |     "    # Compute confusion matrix\n",
2088 |     "    print(\"\\n\\nConfusion_matrix: \\n\")\n",
2089 |     "    cnf_matrix = metrics.confusion_matrix(y_test, y_test_pred)\n",
2090 |     "    ax= plt.subplot()\n",
2091 |     "    sns.heatmap(cnf_matrix, annot=True, ax = None, fmt= '.1f' , cmap= 'Blues', linewidths=0.5); #annot=True to annotate cells    "
2092 |    ]
2093 |   },
2094 |   {
2095 |    "cell_type": "code",
2096 |    "execution_count": 49,
2097 |    "metadata": {},
2098 |    "outputs": [
2099 |     {
2100 |      "name": "stdout",
2101 |      "output_type": "stream",
2102 |      "text": [
2103 |       "Logistic Regression Classifier Report:\n",
2104 |       "\n",
2105 |       "               precision    recall  f1-score   support\n",
2106 |       "\n",
2107 |       "           0       0.81      0.78      0.79       157\n",
2108 |       "           1       0.70      0.75      0.72       111\n",
2109 |       "\n",
2110 |       "    accuracy                           0.76       268\n",
2111 |       "   macro avg       0.76      0.76      0.76       268\n",
2112 |       "weighted avg       0.77      0.76      0.77       268\n",
2113 |       "\n",
2114 |       "\n",
2115 |       "\n",
2116 |       "Confusion_matrix: \n",
2117 |       "\n"
2118 |      ]
2119 |     },
2120 |     {
2121 |      "data": {
2122 |       "image/png": "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\n",
2123 |       "text/plain": [
2124 |        "<Figure size 432x288 with 2 Axes>"
2125 |       ]
2126 |      },
2127 |      "metadata": {
2128 |       "needs_background": "light"
2129 |      },
2130 |      "output_type": "display_data"
2131 |     }
2132 |    ],
2133 |    "source": [
2134 |     "#Logistic Regression\n",
2135 |     "\n",
2136 |     "lr = LogisticRegression()\n",
2137 |     "classifier_report(\"Logistic Regression\", lr)"
2138 |    ]
2139 |   },
2140 |   {
2141 |    "cell_type": "code",
2142 |    "execution_count": 50,
2143 |    "metadata": {},
2144 |    "outputs": [
2145 |     {
2146 |      "name": "stdout",
2147 |      "output_type": "stream",
2148 |      "text": [
2149 |       "Decision Tree Classifier Report:\n",
2150 |       "\n",
2151 |       "               precision    recall  f1-score   support\n",
2152 |       "\n",
2153 |       "           0       0.81      0.89      0.85       157\n",
2154 |       "           1       0.81      0.71      0.76       111\n",
2155 |       "\n",
2156 |       "    accuracy                           0.81       268\n",
2157 |       "   macro avg       0.81      0.80      0.80       268\n",
2158 |       "weighted avg       0.81      0.81      0.81       268\n",
2159 |       "\n",
2160 |       "\n",
2161 |       "\n",
2162 |       "Confusion_matrix: \n",
2163 |       "\n"
2164 |      ]
2165 |     },
2166 |     {
2167 |      "data": {
2168 |       "image/png": "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\n",
2169 |       "text/plain": [
2170 |        "<Figure size 432x288 with 2 Axes>"
2171 |       ]
2172 |      },
2173 |      "metadata": {
2174 |       "needs_background": "light"
2175 |      },
2176 |      "output_type": "display_data"
2177 |     }
2178 |    ],
2179 |    "source": [
2180 |     "dt = DecisionTreeClassifier()\n",
2181 |     "classifier_report(\"Decision Tree\", dt)"
2182 |    ]
2183 |   },
2184 |   {
2185 |    "cell_type": "code",
2186 |    "execution_count": 51,
2187 |    "metadata": {},
2188 |    "outputs": [
2189 |     {
2190 |      "name": "stdout",
2191 |      "output_type": "stream",
2192 |      "text": [
2193 |       "Random Forest Classifier Report:\n",
2194 |       "\n",
2195 |       "               precision    recall  f1-score   support\n",
2196 |       "\n",
2197 |       "           0       0.82      0.85      0.83       157\n",
2198 |       "           1       0.78      0.73      0.75       111\n",
2199 |       "\n",
2200 |       "    accuracy                           0.80       268\n",
2201 |       "   macro avg       0.80      0.79      0.79       268\n",
2202 |       "weighted avg       0.80      0.80      0.80       268\n",
2203 |       "\n",
2204 |       "\n",
2205 |       "\n",
2206 |       "Confusion_matrix: \n",
2207 |       "\n"
2208 |      ]
2209 |     },
2210 |     {
2211 |      "data": {
2212 |       "image/png": "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\n",
2213 |       "text/plain": [
2214 |        "<Figure size 432x288 with 2 Axes>"
2215 |       ]
2216 |      },
2217 |      "metadata": {
2218 |       "needs_background": "light"
2219 |      },
2220 |      "output_type": "display_data"
2221 |     }
2222 |    ],
2223 |    "source": [
2224 |     "# Random Forest Classifier\n",
2225 |     "rf = RandomForestClassifier(n_estimators = 51)\n",
2226 |     "classifier_report(\"Random Forest\", rf)"
2227 |    ]
2228 |   },
2229 |   {
2230 |    "cell_type": "code",
2231 |    "execution_count": 52,
2232 |    "metadata": {},
2233 |    "outputs": [
2234 |     {
2235 |      "name": "stdout",
2236 |      "output_type": "stream",
2237 |      "text": [
2238 |       "K-Neighbors Classifier Report:\n",
2239 |       "\n",
2240 |       "               precision    recall  f1-score   support\n",
2241 |       "\n",
2242 |       "           0       0.77      0.85      0.81       157\n",
2243 |       "           1       0.76      0.64      0.69       111\n",
2244 |       "\n",
2245 |       "    accuracy                           0.76       268\n",
2246 |       "   macro avg       0.76      0.75      0.75       268\n",
2247 |       "weighted avg       0.76      0.76      0.76       268\n",
2248 |       "\n",
2249 |       "\n",
2250 |       "\n",
2251 |       "Confusion_matrix: \n",
2252 |       "\n"
2253 |      ]
2254 |     },
2255 |     {
2256 |      "data": {
2257 |       "image/png": "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\n",
2258 |       "text/plain": [
2259 |        "<Figure size 432x288 with 2 Axes>"
2260 |       ]
2261 |      },
2262 |      "metadata": {
2263 |       "needs_background": "light"
2264 |      },
2265 |      "output_type": "display_data"
2266 |     }
2267 |    ],
2268 |    "source": [
2269 |     "# K-Neighbors Classifier\n",
2270 |     "knn = KNeighborsClassifier(n_neighbors=3)\n",
2271 |     "classifier_report(\"K-Neighbors\", knn)"
2272 |    ]
2273 |   },
2274 |   {
2275 |    "cell_type": "markdown",
2276 |    "metadata": {},
2277 |    "source": [
2278 |     "# Model selection\n",
2279 |     "\n",
2280 |     "Decision tree model performs well compared to other models such as Logistic regression, Random forest and K-Neighbors Classifier. Random forest model has selected for further prediction and analytics.\n"
2281 |    ]
2282 |   },
2283 |   {
2284 |    "cell_type": "code",
2285 |    "execution_count": 90,
2286 |    "metadata": {},
2287 |    "outputs": [
2288 |     {
2289 |      "data": {
2290 |       "text/html": [
2291 |        "<div>\n",
2292 |        "<style scoped>\n",
2293 |        "    .dataframe tbody tr th:only-of-type {\n",
2294 |        "        vertical-align: middle;\n",
2295 |        "    }\n",
2296 |        "\n",
2297 |        "    .dataframe tbody tr th {\n",
2298 |        "        vertical-align: top;\n",
2299 |        "    }\n",
2300 |        "\n",
2301 |        "    .dataframe thead th {\n",
2302 |        "        text-align: right;\n",
2303 |        "    }\n",
2304 |        "</style>\n",
2305 |        "<table border=\"1\" class=\"dataframe\">\n",
2306 |        "  <thead>\n",
2307 |        "    <tr style=\"text-align: right;\">\n",
2308 |        "      <th></th>\n",
2309 |        "      <th>Survived</th>\n",
2310 |        "    </tr>\n",
2311 |        "    <tr>\n",
2312 |        "      <th>PassengerId</th>\n",
2313 |        "      <th></th>\n",
2314 |        "    </tr>\n",
2315 |        "  </thead>\n",
2316 |        "  <tbody>\n",
2317 |        "    <tr>\n",
2318 |        "      <th>892</th>\n",
2319 |        "      <td>0</td>\n",
2320 |        "    </tr>\n",
2321 |        "    <tr>\n",
2322 |        "      <th>893</th>\n",
2323 |        "      <td>1</td>\n",
2324 |        "    </tr>\n",
2325 |        "    <tr>\n",
2326 |        "      <th>894</th>\n",
2327 |        "      <td>0</td>\n",
2328 |        "    </tr>\n",
2329 |        "    <tr>\n",
2330 |        "      <th>895</th>\n",
2331 |        "      <td>0</td>\n",
2332 |        "    </tr>\n",
2333 |        "    <tr>\n",
2334 |        "      <th>896</th>\n",
2335 |        "      <td>1</td>\n",
2336 |        "    </tr>\n",
2337 |        "    <tr>\n",
2338 |        "      <th>...</th>\n",
2339 |        "      <td>...</td>\n",
2340 |        "    </tr>\n",
2341 |        "    <tr>\n",
2342 |        "      <th>1305</th>\n",
2343 |        "      <td>0</td>\n",
2344 |        "    </tr>\n",
2345 |        "    <tr>\n",
2346 |        "      <th>1306</th>\n",
2347 |        "      <td>1</td>\n",
2348 |        "    </tr>\n",
2349 |        "    <tr>\n",
2350 |        "      <th>1307</th>\n",
2351 |        "      <td>0</td>\n",
2352 |        "    </tr>\n",
2353 |        "    <tr>\n",
2354 |        "      <th>1308</th>\n",
2355 |        "      <td>0</td>\n",
2356 |        "    </tr>\n",
2357 |        "    <tr>\n",
2358 |        "      <th>1309</th>\n",
2359 |        "      <td>0</td>\n",
2360 |        "    </tr>\n",
2361 |        "  </tbody>\n",
2362 |        "</table>\n",
2363 |        "<p>418 rows × 1 columns</p>\n",
2364 |        "</div>"
2365 |       ],
2366 |       "text/plain": [
2367 |        "             Survived\n",
2368 |        "PassengerId          \n",
2369 |        "892                 0\n",
2370 |        "893                 1\n",
2371 |        "894                 0\n",
2372 |        "895                 0\n",
2373 |        "896                 1\n",
2374 |        "...               ...\n",
2375 |        "1305                0\n",
2376 |        "1306                1\n",
2377 |        "1307                0\n",
2378 |        "1308                0\n",
2379 |        "1309                0\n",
2380 |        "\n",
2381 |        "[418 rows x 1 columns]"
2382 |       ]
2383 |      },
2384 |      "execution_count": 90,
2385 |      "metadata": {},
2386 |      "output_type": "execute_result"
2387 |     }
2388 |    ],
2389 |    "source": [
2390 |     "submission = pd.read_csv(\"gender_submission.csv\", index_col='PassengerId')\n",
2391 |     "submission"
2392 |    ]
2393 |   },
2394 |   {
2395 |    "cell_type": "code",
2396 |    "execution_count": 91,
2397 |    "metadata": {},
2398 |    "outputs": [],
2399 |    "source": [
2400 |     "rf = DecisionTreeClassifier(random_state=41)\n",
2401 |     "rf.fit(X_train, y_train)\n",
2402 |     "y_test_pred = rf.predict(X_test)"
2403 |    ]
2404 |   },
2405 |   {
2406 |    "cell_type": "code",
2407 |    "execution_count": 92,
2408 |    "metadata": {},
2409 |    "outputs": [
2410 |     {
2411 |      "data": {
2412 |       "text/plain": [
2413 |        "0      0\n",
2414 |        "1      1\n",
2415 |        "2      0\n",
2416 |        "3      0\n",
2417 |        "4      1\n",
2418 |        "      ..\n",
2419 |        "413    0\n",
2420 |        "414    1\n",
2421 |        "415    0\n",
2422 |        "416    0\n",
2423 |        "417    0\n",
2424 |        "Name: Survived, Length: 418, dtype: int32"
2425 |       ]
2426 |      },
2427 |      "execution_count": 92,
2428 |      "metadata": {},
2429 |      "output_type": "execute_result"
2430 |     }
2431 |    ],
2432 |    "source": [
2433 |     "test_survived = pd.Series(rf.predict(testDf), name = \"Survived\").astype(int)\n",
2434 |     "test_survived"
2435 |    ]
2436 |   },
2437 |   {
2438 |    "cell_type": "code",
2439 |    "execution_count": 93,
2440 |    "metadata": {},
2441 |    "outputs": [
2442 |     {
2443 |      "name": "stdout",
2444 |      "output_type": "stream",
2445 |      "text": [
2446 |       "\n",
2447 |       "               precision    recall  f1-score   support\n",
2448 |       "\n",
2449 |       "           0       0.87      0.92      0.90       266\n",
2450 |       "           1       0.85      0.76      0.80       152\n",
2451 |       "\n",
2452 |       "    accuracy                           0.86       418\n",
2453 |       "   macro avg       0.86      0.84      0.85       418\n",
2454 |       "weighted avg       0.86      0.86      0.86       418\n",
2455 |       "\n",
2456 |       "\n",
2457 |       "\n",
2458 |       "Confusion_matrix: \n",
2459 |       "\n"
2460 |      ]
2461 |     },
2462 |     {
2463 |      "data": {
2464 |       "image/png": 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\n",
2465 |       "text/plain": [
2466 |        "<Figure size 432x288 with 2 Axes>"
2467 |       ]
2468 |      },
2469 |      "metadata": {
2470 |       "needs_background": "light"
2471 |      },
2472 |      "output_type": "display_data"
2473 |     }
2474 |    ],
2475 |    "source": [
2476 |     "print(\"\\n\", metrics.classification_report(submission, test_survived))\n",
2477 |     "# Compute confusion matrix\n",
2478 |     "print(\"\\n\\nConfusion_matrix: \\n\")\n",
2479 |     "cnf_matrix = metrics.confusion_matrix(submission, test_survived)\n",
2480 |     "ax= plt.subplot()\n",
2481 |     "sns.heatmap(cnf_matrix, annot=True, ax = None, fmt= '.1f' , cmap= 'Blues', linewidths=0.5); #annot=True to annotate cells "
2482 |    ]
2483 |   },
2484 |   {
2485 |    "cell_type": "code",
2486 |    "execution_count": 107,
2487 |    "metadata": {},
2488 |    "outputs": [],
2489 |    "source": [
2490 |     "results = pd.concat([submission, test_survived],axis = 0)\n",
2491 |     "submission.to_csv(\"titanic.csv\", index = False)"
2492 |    ]
2493 |   },
2494 |   {
2495 |    "cell_type": "code",
2496 |    "execution_count": 104,
2497 |    "metadata": {},
2498 |    "outputs": [],
2499 |    "source": [
2500 |     "submission['test_survived'] = pd.Series(test_survived)"
2501 |    ]
2502 |   },
2503 |   {
2504 |    "cell_type": "code",
2505 |    "execution_count": 105,
2506 |    "metadata": {},
2507 |    "outputs": [
2508 |     {
2509 |      "data": {
2510 |       "text/html": [
2511 |        "<div>\n",
2512 |        "<style scoped>\n",
2513 |        "    .dataframe tbody tr th:only-of-type {\n",
2514 |        "        vertical-align: middle;\n",
2515 |        "    }\n",
2516 |        "\n",
2517 |        "    .dataframe tbody tr th {\n",
2518 |        "        vertical-align: top;\n",
2519 |        "    }\n",
2520 |        "\n",
2521 |        "    .dataframe thead th {\n",
2522 |        "        text-align: right;\n",
2523 |        "    }\n",
2524 |        "</style>\n",
2525 |        "<table border=\"1\" class=\"dataframe\">\n",
2526 |        "  <thead>\n",
2527 |        "    <tr style=\"text-align: right;\">\n",
2528 |        "      <th></th>\n",
2529 |        "      <th>Survived</th>\n",
2530 |        "      <th>test_survived</th>\n",
2531 |        "    </tr>\n",
2532 |        "    <tr>\n",
2533 |        "      <th>PassengerId</th>\n",
2534 |        "      <th></th>\n",
2535 |        "      <th></th>\n",
2536 |        "    </tr>\n",
2537 |        "  </thead>\n",
2538 |        "  <tbody>\n",
2539 |        "    <tr>\n",
2540 |        "      <th>892</th>\n",
2541 |        "      <td>0</td>\n",
2542 |        "      <td>NaN</td>\n",
2543 |        "    </tr>\n",
2544 |        "    <tr>\n",
2545 |        "      <th>893</th>\n",
2546 |        "      <td>1</td>\n",
2547 |        "      <td>NaN</td>\n",
2548 |        "    </tr>\n",
2549 |        "    <tr>\n",
2550 |        "      <th>894</th>\n",
2551 |        "      <td>0</td>\n",
2552 |        "      <td>NaN</td>\n",
2553 |        "    </tr>\n",
2554 |        "    <tr>\n",
2555 |        "      <th>895</th>\n",
2556 |        "      <td>0</td>\n",
2557 |        "      <td>NaN</td>\n",
2558 |        "    </tr>\n",
2559 |        "    <tr>\n",
2560 |        "      <th>896</th>\n",
2561 |        "      <td>1</td>\n",
2562 |        "      <td>NaN</td>\n",
2563 |        "    </tr>\n",
2564 |        "    <tr>\n",
2565 |        "      <th>...</th>\n",
2566 |        "      <td>...</td>\n",
2567 |        "      <td>...</td>\n",
2568 |        "    </tr>\n",
2569 |        "    <tr>\n",
2570 |        "      <th>1305</th>\n",
2571 |        "      <td>0</td>\n",
2572 |        "      <td>NaN</td>\n",
2573 |        "    </tr>\n",
2574 |        "    <tr>\n",
2575 |        "      <th>1306</th>\n",
2576 |        "      <td>1</td>\n",
2577 |        "      <td>NaN</td>\n",
2578 |        "    </tr>\n",
2579 |        "    <tr>\n",
2580 |        "      <th>1307</th>\n",
2581 |        "      <td>0</td>\n",
2582 |        "      <td>NaN</td>\n",
2583 |        "    </tr>\n",
2584 |        "    <tr>\n",
2585 |        "      <th>1308</th>\n",
2586 |        "      <td>0</td>\n",
2587 |        "      <td>NaN</td>\n",
2588 |        "    </tr>\n",
2589 |        "    <tr>\n",
2590 |        "      <th>1309</th>\n",
2591 |        "      <td>0</td>\n",
2592 |        "      <td>NaN</td>\n",
2593 |        "    </tr>\n",
2594 |        "  </tbody>\n",
2595 |        "</table>\n",
2596 |        "<p>418 rows × 2 columns</p>\n",
2597 |        "</div>"
2598 |       ],
2599 |       "text/plain": [
2600 |        "             Survived  test_survived\n",
2601 |        "PassengerId                         \n",
2602 |        "892                 0            NaN\n",
2603 |        "893                 1            NaN\n",
2604 |        "894                 0            NaN\n",
2605 |        "895                 0            NaN\n",
2606 |        "896                 1            NaN\n",
2607 |        "...               ...            ...\n",
2608 |        "1305                0            NaN\n",
2609 |        "1306                1            NaN\n",
2610 |        "1307                0            NaN\n",
2611 |        "1308                0            NaN\n",
2612 |        "1309                0            NaN\n",
2613 |        "\n",
2614 |        "[418 rows x 2 columns]"
2615 |       ]
2616 |      },
2617 |      "execution_count": 105,
2618 |      "metadata": {},
2619 |      "output_type": "execute_result"
2620 |     }
2621 |    ],
2622 |    "source": [
2623 |     "submission"
2624 |    ]
2625 |   },
2626 |   {
2627 |    "cell_type": "code",
2628 |    "execution_count": 106,
2629 |    "metadata": {},
2630 |    "outputs": [],
2631 |    "source": [
2632 |     "test_survived.to_csv(\"test_survived.csv\", index = False)"
2633 |    ]
2634 |   },
2635 |   {
2636 |    "cell_type": "code",
2637 |    "execution_count": 98,
2638 |    "metadata": {},
2639 |    "outputs": [
2640 |     {
2641 |      "data": {
2642 |       "text/plain": [
2643 |        "pandas.core.series.Series"
2644 |       ]
2645 |      },
2646 |      "execution_count": 98,
2647 |      "metadata": {},
2648 |      "output_type": "execute_result"
2649 |     }
2650 |    ],
2651 |    "source": [
2652 |     "type(test_survived)"
2653 |    ]
2654 |   },
2655 |   {
2656 |    "cell_type": "code",
2657 |    "execution_count": 99,
2658 |    "metadata": {},
2659 |    "outputs": [
2660 |     {
2661 |      "data": {
2662 |       "text/plain": [
2663 |        "pandas.core.frame.DataFrame"
2664 |       ]
2665 |      },
2666 |      "execution_count": 99,
2667 |      "metadata": {},
2668 |      "output_type": "execute_result"
2669 |     }
2670 |    ],
2671 |    "source": [
2672 |     "type(submission)"
2673 |    ]
2674 |   },
2675 |   {
2676 |    "cell_type": "code",
2677 |    "execution_count": 88,
2678 |    "metadata": {},
2679 |    "outputs": [
2680 |     {
2681 |      "data": {
2682 |       "text/plain": [
2683 |        "836"
2684 |       ]
2685 |      },
2686 |      "execution_count": 88,
2687 |      "metadata": {},
2688 |      "output_type": "execute_result"
2689 |     }
2690 |    ],
2691 |    "source": [
2692 |     "len(results)"
2693 |    ]
2694 |   },
2695 |   {
2696 |    "cell_type": "code",
2697 |    "execution_count": 67,
2698 |    "metadata": {},
2699 |    "outputs": [
2700 |     {
2701 |      "data": {
2702 |       "text/plain": [
2703 |        "418"
2704 |       ]
2705 |      },
2706 |      "execution_count": 67,
2707 |      "metadata": {},
2708 |      "output_type": "execute_result"
2709 |     }
2710 |    ],
2711 |    "source": [
2712 |     "len(submission)"
2713 |    ]
2714 |   },
2715 |   {
2716 |    "cell_type": "code",
2717 |    "execution_count": 64,
2718 |    "metadata": {},
2719 |    "outputs": [
2720 |     {
2721 |      "data": {
2722 |       "text/plain": [
2723 |        "418"
2724 |       ]
2725 |      },
2726 |      "execution_count": 64,
2727 |      "metadata": {},
2728 |      "output_type": "execute_result"
2729 |     }
2730 |    ],
2731 |    "source": [
2732 |     "len(test_survived)"
2733 |    ]
2734 |   },
2735 |   {
2736 |    "cell_type": "code",
2737 |    "execution_count": 66,
2738 |    "metadata": {},
2739 |    "outputs": [
2740 |     {
2741 |      "data": {
2742 |       "text/plain": [
2743 |        "418"
2744 |       ]
2745 |      },
2746 |      "execution_count": 66,
2747 |      "metadata": {},
2748 |      "output_type": "execute_result"
2749 |     }
2750 |    ],
2751 |    "source": [
2752 |     "len(testDf)"
2753 |    ]
2754 |   },
2755 |   {
2756 |    "cell_type": "code",
2757 |    "execution_count": 48,
2758 |    "metadata": {},
2759 |    "outputs": [],
2760 |    "source": [
2761 |     "rf = DecisionTreeClassifier(random_state=41)\n",
2762 |     "rf.fit(X_train, y_train)\n",
2763 |     "y_test_pred = rf.predict(testDf)"
2764 |    ]
2765 |   },
2766 |   {
2767 |    "cell_type": "code",
2768 |    "execution_count": 49,
2769 |    "metadata": {},
2770 |    "outputs": [
2771 |     {
2772 |      "data": {
2773 |       "text/plain": [
2774 |        "array([0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0,\n",
2775 |        "       1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1,\n",
2776 |        "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,\n",
2777 |        "       1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,\n",
2778 |        "       1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,\n",
2779 |        "       0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,\n",
2780 |        "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,\n",
2781 |        "       0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,\n",
2782 |        "       1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
2783 |        "       0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,\n",
2784 |        "       1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1,\n",
2785 |        "       0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,\n",
2786 |        "       1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
2787 |        "       0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,\n",
2788 |        "       1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0,\n",
2789 |        "       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0,\n",
2790 |        "       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0,\n",
2791 |        "       1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
2792 |        "       0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0],\n",
2793 |        "      dtype=int64)"
2794 |       ]
2795 |      },
2796 |      "execution_count": 49,
2797 |      "metadata": {},
2798 |      "output_type": "execute_result"
2799 |     }
2800 |    ],
2801 |    "source": [
2802 |     "y_test_pred"
2803 |    ]
2804 |   },
2805 |   {
2806 |    "cell_type": "code",
2807 |    "execution_count": 50,
2808 |    "metadata": {},
2809 |    "outputs": [],
2810 |    "source": [
2811 |     "submission = pd.read_csv(\"gender_submission.csv\", index_col='PassengerId')\n",
2812 |     "submission"
2813 |    ]
2814 |   },
2815 |   {
2816 |    "cell_type": "code",
2817 |    "execution_count": 52,
2818 |    "metadata": {},
2819 |    "outputs": [
2820 |     {
2821 |      "name": "stdout",
2822 |      "output_type": "stream",
2823 |      "text": [
2824 |       "\n",
2825 |       "               precision    recall  f1-score   support\n",
2826 |       "\n",
2827 |       "           0       0.87      0.92      0.90       266\n",
2828 |       "           1       0.85      0.76      0.80       152\n",
2829 |       "\n",
2830 |       "    accuracy                           0.86       418\n",
2831 |       "   macro avg       0.86      0.84      0.85       418\n",
2832 |       "weighted avg       0.86      0.86      0.86       418\n",
2833 |       "\n",
2834 |       "\n",
2835 |       "\n",
2836 |       "Confusion_matrix: \n",
2837 |       "\n"
2838 |      ]
2839 |     },
2840 |     {
2841 |      "data": {
2842 |       "image/png": 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\n",
2843 |       "text/plain": [
2844 |        "<Figure size 432x288 with 2 Axes>"
2845 |       ]
2846 |      },
2847 |      "metadata": {
2848 |       "needs_background": "light"
2849 |      },
2850 |      "output_type": "display_data"
2851 |     }
2852 |    ],
2853 |    "source": [
2854 |     "print(\"\\n\", metrics.classification_report(submission, y_test_pred))\n",
2855 |     "# Compute confusion matrix\n",
2856 |     "print(\"\\n\\nConfusion_matrix: \\n\")\n",
2857 |     "cnf_matrix = metrics.confusion_matrix(submission, y_test_pred)\n",
2858 |     "ax= plt.subplot()\n",
2859 |     "sns.heatmap(cnf_matrix, annot=True, ax = None, fmt= '.1f' , cmap= 'Blues', linewidths=0.5); #annot=True to annotate cells    "
2860 |    ]
2861 |   },
2862 |   {
2863 |    "cell_type": "markdown",
2864 |    "metadata": {},
2865 |    "source": [
2866 |     "# Conclution: \n",
2867 |     "\n",
2868 |     "The results as per the Accuracy for Decision Tree is 86%.\n",
2869 |     "\n",
2870 |     "The training set should be used to build machine learning models. The test set should be used to see how well the model performs on unseen data.\n",
2871 |     "\n",
2872 |     "For the test set, they do not provide the ground truth for each passenger. It is the challenge to predict these outcomes. For each passenger in the test set, use the trained model to predict whether or not they survived the sinking of the Titanic.\n"
2873 |    ]
2874 |   }
2875 |  ],
2876 |  "metadata": {
2877 |   "kernelspec": {
2878 |    "display_name": "Python 3 (ipykernel)",
2879 |    "language": "python",
2880 |    "name": "python3"
2881 |   },
2882 |   "language_info": {
2883 |    "codemirror_mode": {
2884 |     "name": "ipython",
2885 |     "version": 3
2886 |    },
2887 |    "file_extension": ".py",
2888 |    "mimetype": "text/x-python",
2889 |    "name": "python",
2890 |    "nbconvert_exporter": "python",
2891 |    "pygments_lexer": "ipython3",
2892 |    "version": "3.11.5"
2893 |   }
2894 |  },
2895 |  "nbformat": 4,
2896 |  "nbformat_minor": 4
2897 | }
2898 | 


--------------------------------------------------------------------------------