Forecasting/Testing_Best_Model_Titanic_Survival_Predictions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Scikit-Learn packages\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "\n",
    "# Computation and Visualisation packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "%matplotlib inline\n",
    "\n",
    "#ignore warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 488,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Loading the titanic survival dataset\n",
    "titanic_train = pd.read_csv('data/titanic_survival_prediction/train.csv')\n",
    "titanic_test = pd.read_csv('data/titanic_survival_prediction/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 11 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            332 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 36.0+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
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       "      <td>Heikkinen, Miss. Laina</td>\n",
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       "      <td>4</td>\n",
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       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
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       "      <td>Allen, Mr. William Henry</td>\n",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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     "execution_count": 490,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 491,
   "metadata": {},
   "outputs": [
    {
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       "      <td>Montvila, Rev. Juozas</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Behr, Mr. Karl Howell</td>\n",
       "      <td>male</td>\n",
       "      <td>26.0</td>\n",
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       "      <td>Dooley, Mr. Patrick</td>\n",
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       "      <td>32.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>370376</td>\n",
       "      <td>7.75</td>\n",
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      "text/plain": [
       "     PassengerId  Survived  Pclass                                      Name  \\\n",
       "886          887         0       2                     Montvila, Rev. Juozas   \n",
       "887          888         1       1              Graham, Miss. Margaret Edith   \n",
       "888          889         0       3  Johnston, Miss. Catherine Helen \"Carrie\"   \n",
       "889          890         1       1                     Behr, Mr. Karl Howell   \n",
       "890          891         0       3                       Dooley, Mr. Patrick   \n",
       "\n",
       "        Sex   Age  SibSp  Parch      Ticket   Fare Cabin Embarked  \n",
       "886    male  27.0      0      0      211536  13.00   NaN        S  \n",
       "887  female  19.0      0      0      112053  30.00   B42        S  \n",
       "888  female   NaN      1      2  W./C. 6607  23.45   NaN        S  \n",
       "889    male  26.0      0      0      111369  30.00  C148        C  \n",
       "890    male  32.0      0      0      370376   7.75   NaN        Q  "
      ]
     },
     "execution_count": 491,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 492,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting into training and test dataset\n",
    "#train, test = train_test_split(titanic, test_size = 0.40, random_state=35)\n",
    "\n",
    "#print (train.info())\n",
    "#print (test.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 493,
   "metadata": {},
   "outputs": [
    {
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       "      <th>PassengerId</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
       "      <td>714.000000</td>\n",
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       "      <th>unique</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>891</td>\n",
       "      <td>2</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
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       "      <td>3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Graham, Mr. George Edward</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CA. 2343</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C23 C25 C27</td>\n",
       "      <td>S</td>\n",
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       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        PassengerId    Survived      Pclass                       Name   Sex  \\\n",
       "count    891.000000  891.000000  891.000000                        891   891   \n",
       "unique          NaN         NaN         NaN                        891     2   \n",
       "top             NaN         NaN         NaN  Graham, Mr. George Edward  male   \n",
       "freq            NaN         NaN         NaN                          1   577   \n",
       "mean     446.000000    0.383838    2.308642                        NaN   NaN   \n",
       "std      257.353842    0.486592    0.836071                        NaN   NaN   \n",
       "min        1.000000    0.000000    1.000000                        NaN   NaN   \n",
       "25%      223.500000    0.000000    2.000000                        NaN   NaN   \n",
       "50%      446.000000    0.000000    3.000000                        NaN   NaN   \n",
       "75%      668.500000    1.000000    3.000000                        NaN   NaN   \n",
       "max      891.000000    1.000000    3.000000                        NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch    Ticket        Fare        Cabin  \\\n",
       "count   714.000000  891.000000  891.000000       891  891.000000          204   \n",
       "unique         NaN         NaN         NaN       681         NaN          147   \n",
       "top            NaN         NaN         NaN  CA. 2343         NaN  C23 C25 C27   \n",
       "freq           NaN         NaN         NaN         7         NaN            4   \n",
       "mean     29.699118    0.523008    0.381594       NaN   32.204208          NaN   \n",
       "std      14.526497    1.102743    0.806057       NaN   49.693429          NaN   \n",
       "min       0.420000    0.000000    0.000000       NaN    0.000000          NaN   \n",
       "25%      20.125000    0.000000    0.000000       NaN    7.910400          NaN   \n",
       "50%      28.000000    0.000000    0.000000       NaN   14.454200          NaN   \n",
       "75%      38.000000    1.000000    0.000000       NaN   31.000000          NaN   \n",
       "max      80.000000    8.000000    6.000000       NaN  512.329200          NaN   \n",
       "\n",
       "       Embarked  \n",
       "count       889  \n",
       "unique        3  \n",
       "top           S  \n",
       "freq        644  \n",
       "mean        NaN  \n",
       "std         NaN  \n",
       "min         NaN  \n",
       "25%         NaN  \n",
       "50%         NaN  \n",
       "75%         NaN  \n",
       "max         NaN  "
      ]
     },
     "execution_count": 493,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_train.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===== Train =====\n",
      "PassengerId      0\n",
      "Survived         0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age            177\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             0\n",
      "Cabin          687\n",
      "Embarked         2\n",
      "dtype: int64\n",
      "\n",
      "===== Test =====\n",
      "PassengerId      0\n",
      "Pclass           0\n",
      "Name             0\n",
      "Sex              0\n",
      "Age             86\n",
      "SibSp            0\n",
      "Parch            0\n",
      "Ticket           0\n",
      "Fare             1\n",
      "Cabin          327\n",
      "Embarked         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Understanding into number of null variables by columns for both train and test\n",
    "print (\"===== Train =====\")\n",
    "print (pd.isnull(titanic_train).sum())\n",
    "print (\"\\n===== Test =====\")\n",
    "print (pd.isnull(titanic_test).sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualising few of the columns from the dataset for better understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sex v/s Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UJUg6xzh8JElqGAqSpIahIElqGAqSpIahIElqGAqSpIahIElqGAqSpIahIElqtBoKSdYn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a122f70d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Sex\", y=\"Survived\", data=titanic_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Percentage of females who survived:', 74.203821656050948)\n",
      "('Percentage of males who survived:', 18.890814558058924)\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of females who survived:\", titanic_train[\"Survived\"][titanic_train[\"Sex\"] == 'female'].value_counts(normalize = True)[1]*100)\n",
    "print(\"Percentage of males who survived:\", titanic_train[\"Survived\"][titanic_train[\"Sex\"] == 'male'].value_counts(normalize = True)[1]*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pclass v/s Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Y2LyEkyQ9WZelsB9Yk2R1kmXAJmBPh68nSXqKOiuFqpoCtgD7gCPAHVV1KMn2JBsAkvxk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12e52150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Pclass\", y=\"Survived\", data=titanic_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Percentage of Pclass = 1 who survived:', 62.962962962962962)\n",
      "('Percentage of Pclass = 2 who survived:', 47.282608695652172)\n",
      "('Percentage of Pclass = 3 who survived:', 24.236252545824847)\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of Pclass = 1 who survived:\", titanic_train[\"Survived\"][titanic_train[\"Pclass\"] == 1].value_counts(normalize = True)[1]*100)\n",
    "print(\"Percentage of Pclass = 2 who survived:\", titanic_train[\"Survived\"][titanic_train[\"Pclass\"] == 2].value_counts(normalize = True)[1]*100)\n",
    "print(\"Percentage of Pclass = 3 who survived:\", titanic_train[\"Survived\"][titanic_train[\"Pclass\"] == 3].value_counts(normalize = True)[1]*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SibSp v/s Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a13071cd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"SibSp\", y=\"Survived\", data=titanic_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Percentage of SibSp = 0 who survived:', 34.539473684210527)\n",
      "('Percentage of SibSp = 1 who survived:', 53.588516746411486)\n",
      "('Percentage of SibSp = 2 who survived:', 46.428571428571431)\n"
     ]
    }
   ],
   "source": [
    "print(\"Percentage of SibSp = 0 who survived:\", titanic_train[\"Survived\"][titanic_train[\"SibSp\"] == 0].value_counts(normalize = True)[1]*100)\n",
    "print(\"Percentage of SibSp = 1 who survived:\", titanic_train[\"Survived\"][titanic_train[\"SibSp\"] == 1].value_counts(normalize = True)[1]*100)\n",
    "print(\"Percentage of SibSp = 2 who survived:\", titanic_train[\"Survived\"][titanic_train[\"SibSp\"] == 2].value_counts(normalize = True)[1]*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parch v/s Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kfu8Blv6gebL5l94q4IH/OoEdvfdzy/vP/iE8f5xBfZbCFLBuxvZa4J5DjJlKshJ4DvDQ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1486f350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Parch\", y=\"Survived\", data=titanic_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Age Group v/s Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "titanic_train[\"Age\"] = titanic_train[\"Age\"].fillna(-0.5)\n",
    "titanic_test[\"Age\"] = titanic_test[\"Age\"].fillna(-0.5)\n",
    "bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]\n",
    "labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']\n",
    "titanic_train['AgeGroup'] = pd.cut(titanic_train[\"Age\"], bins, labels = labels)\n",
    "titanic_test['AgeGroup'] = pd.cut(titanic_test[\"Age\"], bins, labels = labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 503,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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W0sUHfTnRXG87mEVKZl/M288ienDOg7Qdtef5fYH6NdA+8R3NWxGl66zvjoj+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\nYGZmhf8PlFuiSak0F+cAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a12faa8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"AgeGroup\", y=\"Survived\", data=titanic_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cabin v/s Survived"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Percentage of CabinBool = 1 who survived:', 66.666666666666657)\n",
      "('Percentage of CabinBool = 0 who survived:', 29.985443959243085)\n"
     ]
    }
   ],
   "source": [
    "titanic_train[\"CabinBool\"] = (titanic_train[\"Cabin\"].notnull().astype('int'))\n",
    "titanic_test[\"CabinBool\"] = (titanic_test[\"Cabin\"].notnull().astype('int'))\n",
    "\n",
    "print(\"Percentage of CabinBool = 1 who survived:\", \n",
    "      titanic_train[\"Survived\"][titanic_train[\"CabinBool\"] == 1].value_counts(normalize = True)[1]*100)\n",
    "\n",
    "print(\"Percentage of CabinBool = 0 who survived:\", \n",
    "      titanic_train[\"Survived\"][titanic_train[\"CabinBool\"] == 0].value_counts(normalize = True)[1]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VJElnAe9oliQ1hoIkqTEUJEmNoSBJagwFSVJjKEiSGkNBktQYCpKkxlCQJDWGgiSpMRQk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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14529e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"CabinBool\", y=\"Survived\", data=titanic_train)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing of the Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 418 entries, 0 to 417\n",
      "Data columns (total 13 columns):\n",
      "PassengerId    418 non-null int64\n",
      "Pclass         418 non-null int64\n",
      "Name           418 non-null object\n",
      "Sex            418 non-null object\n",
      "Age            418 non-null float64\n",
      "SibSp          418 non-null int64\n",
      "Parch          418 non-null int64\n",
      "Ticket         418 non-null object\n",
      "Fare           417 non-null float64\n",
      "Cabin          91 non-null object\n",
      "Embarked       418 non-null object\n",
      "AgeGroup       418 non-null category\n",
      "CabinBool      418 non-null int64\n",
      "dtypes: category(1), float64(2), int64(5), object(5)\n",
      "memory usage: 40.0+ KB\n"
     ]
    }
   ],
   "source": [
    "titanic_test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Mapping fare into categorical value\n",
    "titanic_train['FareBand'] = pd.qcut(titanic_train['Fare'], 4, labels = [1, 2, 3, 4])\n",
    "titanic_test['FareBand'] = pd.qcut(titanic_test['Fare'], 4, labels = [1, 2, 3, 4])\n",
    "\n",
    "# Replacing NA in embarked feature with 'S'\n",
    "titanic_train = titanic_train.fillna({\"Embarked\": \"S\"})\n",
    "titanic_test['Survived'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating new column called AgeBand\n",
    "combine = [titanic_train, titanic_test]\n",
    "\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady','Capt','Col','Don','Dr','Major','Rev','Jonkheer','Dona'], 'Rare')\n",
    "    dataset['Title'] = dataset['Title'].replace(['Countess', 'Lady', 'Sir'], 'Royal')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "metadata": {},
   "outputs": [],
   "source": [
    "#titanic_train[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()\n",
    "\n",
    "title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Royal\": 5, \"Rare\": 6}\n",
    "for dataset in combine:\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "mr_age = titanic_train[titanic_train[\"Title\"] == 1][\"AgeGroup\"].mode() #Young Adult\n",
    "miss_age = titanic_train[titanic_train[\"Title\"] == 2][\"AgeGroup\"].mode() #Student\n",
    "mrs_age = titanic_train[titanic_train[\"Title\"] == 3][\"AgeGroup\"].mode() #Adult\n",
    "master_age = titanic_train[titanic_train[\"Title\"] == 4][\"AgeGroup\"].mode() #Baby\n",
    "royal_age = titanic_train[titanic_train[\"Title\"] == 5][\"AgeGroup\"].mode() #Adult\n",
    "rare_age = titanic_train[titanic_train[\"Title\"] == 6][\"AgeGroup\"].mode() #Adult\n",
    "\n",
    "age_title_mapping = {1: \"Young Adult\", 2: \"Student\", 3: \"Adult\", 4: \"Baby\", 5: \"Adult\", 6: \"Adult\", 0.0:\"Unknown\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 510,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in range(len(titanic_train[\"AgeGroup\"])):\n",
    "    if titanic_train[\"AgeGroup\"][x] == \"Unknown\":\n",
    "        titanic_train[\"AgeGroup\"][x] = age_title_mapping[titanic_train[\"Title\"][x]]\n",
    "\n",
    "for x in range(len(titanic_test[\"AgeGroup\"])):\n",
    "    if titanic_test[\"AgeGroup\"][x] == \"Unknown\":\n",
    "        titanic_test[\"AgeGroup\"][x] = age_title_mapping[titanic_test[\"Title\"][x]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_mapping = {'Baby': 1, 'Child': 2, 'Teenager': 3, 'Student': 4, 'Young Adult': 5, 'Adult': 6, 'Senior': 7}\n",
    "titanic_train['AgeGroup'] = titanic_train['AgeGroup'].map(age_mapping)\n",
    "titanic_test['AgeGroup'] = titanic_test['AgeGroup'].map(age_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sex feature\n",
    "sex_mapping = {\"male\": 0, \"female\": 1}\n",
    "titanic_train['Sex'] = titanic_train['Sex'].map(sex_mapping)\n",
    "titanic_test['Sex'] = titanic_test['Sex'].map(sex_mapping)\n",
    "\n",
    "# Replacing embarked categories with numerical values\n",
    "embarked_mapping = {\"S\": 1, \"C\": 2, \"Q\": 3}\n",
    "titanic_train['Embarked'] = titanic_train['Embarked'].map(embarked_mapping)\n",
    "titanic_test['Embarked'] = titanic_test['Embarked'].map(embarked_mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing Fare with FareBand\n",
    "for x in range(len(titanic_test[\"Fare\"])):\n",
    "    if pd.isnull(titanic_test[\"Fare\"][x]):\n",
    "        pclass = titanic_test[\"Pclass\"][x] #Pclass = 3\n",
    "        titanic_test[\"Fare\"][x] = round(titanic_train[titanic_train[\"Pclass\"] == pclass][\"Fare\"].mean(), 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping the cabin, ticket, fare, age, and name feature\n",
    "titanic_train = titanic_train.drop(['Cabin'], axis = 1)\n",
    "titanic_train = titanic_train.drop(['Ticket'], axis = 1)\n",
    "titanic_train = titanic_train.drop(['Name'], axis = 1)\n",
    "titanic_test = titanic_test.drop(['Cabin'], axis = 1)\n",
    "titanic_test = titanic_test.drop(['Ticket'], axis = 1)\n",
    "titanic_test = titanic_test.drop(['Name'], axis = 1)\n",
    "titanic_train = titanic_train.drop(['Fare'], axis = 1)\n",
    "titanic_test = titanic_test.drop(['Fare'], axis = 1)\n",
    "titanic_train = titanic_train.drop(['Age'], axis = 1)\n",
    "titanic_test = titanic_test.drop(['Age'], axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploring various models and evaluating their accuracies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 515,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictors = titanic_train.drop(['Survived', 'PassengerId'], axis=1)\n",
    "target = titanic_train[\"Survived\"]\n",
    "x_train, x_val, y_train, y_val = train_test_split(predictors, target, test_size = 0.20, random_state = 21)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 516,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gaussian Naive Bayes\n",
    "gaussian = GaussianNB()\n",
    "gaussian.fit(x_train, y_train)\n",
    "y_pred = gaussian.predict(x_val)\n",
    "acc_gaussian = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "\n",
    "# Logistic Regression\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(x_train, y_train)\n",
    "y_pred = logreg.predict(x_val)\n",
    "acc_logreg = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "\n",
    "# Support Vector Machines\n",
    "svc = SVC()\n",
    "svc.fit(x_train, y_train)\n",
    "y_pred = svc.predict(x_val)\n",
    "acc_svc = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "\n",
    "# Linear SVC\n",
    "linear_svc = LinearSVC()\n",
    "linear_svc.fit(x_train, y_train)\n",
    "y_pred = linear_svc.predict(x_val)\n",
    "acc_linear_svc = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "\n",
    "# Perceptron\n",
    "perceptron = Perceptron()\n",
    "perceptron.fit(x_train, y_train)\n",
    "y_pred = perceptron.predict(x_val)\n",
    "acc_perceptron = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "\n",
    "#Decision Tree\n",
    "decisiontree = DecisionTreeClassifier()\n",
    "decisiontree.fit(x_train, y_train)\n",
    "y_pred = decisiontree.predict(x_val)\n",
    "acc_decisiontree = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "\n",
    "# Random Forest\n",
    "randomforest = RandomForestClassifier()\n",
    "randomforest.fit(x_train, y_train)\n",
    "y_pred = randomforest.predict(x_val)\n",
    "acc_randomforest = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "\n",
    "# KNN or k-Nearest Neighbors\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(x_train, y_train)\n",
    "y_pred = knn.predict(x_val)\n",
    "acc_knn = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "\n",
    "# Stochastic Gradient Descent\n",
    "sgd = SGDClassifier()\n",
    "sgd.fit(x_train, y_train)\n",
    "y_pred = sgd.predict(x_val)\n",
    "acc_sgd = round(accuracy_score(y_pred, y_val) * 100, 2)\n",
    "\n",
    "# Gradient Boosting Classifier\n",
    "gbk = GradientBoostingClassifier()\n",
    "gbk.fit(x_train, y_train)\n",
    "y_pred = gbk.predict(x_val)\n",
    "acc_gbk = round(accuracy_score(y_pred, y_val) * 100, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 517,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Comparing the Accuracy of each of the model\n",
    "model_accuracy = pd.DataFrame({\n",
    "    'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression', 'Random Forest', 'Naive Bayes', 'Perceptron', \n",
    "              'Linear SVC', 'Decision Tree', 'SGD', 'GBC'],\n",
    "    'Score': [acc_svc, acc_knn, acc_logreg, acc_randomforest, acc_gaussian, acc_perceptron,acc_linear_svc, \n",
    "              acc_decisiontree,acc_sgd, acc_gbk]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 521,
   "metadata": {},
   "outputs": [
    {
     "data": {
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S6rj9zdt3OzgCuHfivYxfMr6KiyRJ6SondIAy09DeQ/nbh38DoHBCId/t8N3A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TqyyZqkHWXySJJU9b748S9y6IcOLTtt34H78pdH/GUnRyRJ0vYZPXg0oweNBuDm525uTvK0p9Im\na1t7ACNJbdFhyaOIGB0RT5T8ezcivh4RU0vGvRARdlQhSWpV3559ue1zt3Hp2EvpW9cXgJ61Pbno\nkIu45wv3MKTvkIIjlCSp7ZpqH62rX8cdL9zRrst+Z8M7zHluDgCH7HkIBw45sF2XL6l76tFRC04p\nLQYOBYiIWuBVYHpK6d+a5omIHwCrOyoGSdLOY7ddduO/Tv8vfnTyj3hz3ZsM2mUQfXv2LTosSZK2\n2cTRE7nyvisBmLFoBifvf3K7LXv6ounUN9YDNuuW1H46q9naCcDzKaUXm0ZE1vD2POC6TopBkrQT\n6N2jN/sM2MfEkSSpyzriw0cwuM9gAGYumUlKqd2WPWVBSZO1g2yyJql9dFby6Hw+mCT6FPBGSunZ\nch+IiD+NiEci4pEVK1Z0eICSJEmS1Blqa2o5fdTpALy25jUeW/5Yuyz3zXVvcuuyWwH4xLBPMHK3\nke2yXEnq8ORRRPQEJgLXt5h0Aa3UOkop/SyldFhK6bAhQ+zLQpIkSdLOY+Ko9n/r2u+e/h2NqRGw\nyZqk9tUZNY9OAR5LKb3RNCIiegBnAVM7Yf2SJEmSVFXG7TeOXrW9AJixeEa7LLOpyVoQnDfmvHZZ\npiRB5ySPytUwOhFYlFJ6pRPWL0mSJElVpV/Pfpww8gQAnnzjSV5c9eJWPtG6V959hbtfuhuAo/c5\nmmG7DtvhGCWpSYcmjyKiDzAO+N8Wk8r1gSRJkiRJ3UZp07VZS2bt0LKmLZzWPHz+QTZZk9S+OjR5\nlFJan1IalFJa3WL8JSmln3bkuiVJkiSpmk0YPaF5eEf7PZq6MOsRpCZqOOej5+zQsiSppc5625ok\nSZIkqcTQ/kM5fOjhANzxwh2s3rh6K58ob+k7S3no1YcAOGHfE9ij7x7tFqMkgckjSZIkSSrMxNFZ\n07X6xnpufu7m7VrG1AWb30NkkzVJHcHkkSRJkiQVpCl5BDBzyfY1XZuyMHvLWl1NHZ/5yGfaJS5J\nKmXySJIkSZIKcvAeBzN8wHAAZj87m/qG+m36/NMrnmb+G/MBOHn/k9ltl93aPUZJMnkkSZIkSQWJ\nCM4YfQYAqzau4p6X7tmmz5c2WZs0ZlK7xiZJTUweSZIkSVKBtmi6tg1vXUspNb9lrXeP3lssR5La\nk8kjSZIkSSrQMcOPYUCvAQDMWDyDlFKbPvfkG0+y+K3FAJw+6nT69+rfYTFK6t5MHkmSJElSgepq\n6zjlgFMAWLZqGQtXLGzT56YsmNI8fP4Y37ImqeOYPJIkSZKkgjX1ewRta7qWUmpOHvXr2Y9TDzi1\nw2KTJJNHkiRJklSwk/c/mR41PYC2JY8efPVBXlz9IgBnfuRMdqnbpUPjk9S9mTySJEmSpIIN7D2Q\nY4cfC2SJoeVrlrc6f2mTNd+yJqmjmTySJEmSpCpQ+ra0G5fcWHG+hsYGpi2cBmRJp/H7je/w2CR1\nbyaPJEmSJKkKlCaPZi6p3HTtnpfuYfnarGbS2QeeTc/anh0em6TuzeSRJEmSJFWBEQNHcMiehwAw\nb+k81r2/rux8W7xl7SDfsiap45k8kiRJkqQqMXFUVvto46aNzFs67wPT6xvq+d0zvwNgj757cNyI\n4zozPEndlMkjSZIkSaoSpU3XZiye8YHpty27jZXrVwJw7kfPbX5DmyR1JJNHkiRJklQlxg4dy179\n9gKyTrMbGhu2mD5loW9Zk9T5TB5JkiRJUpWoiZrm2kcr1q/gwVcfbJ723qb3mP7MdACG9R/GUfsc\nVUiMkrofk0eSJEmSVEW2eOva4s1vXZvz/BxWv7cayGod1YS3c5I6h2cbSZIkSaoix+97PH3q+gBb\n9nvkW9YkFcXkkSRJkiRVkd49enPSficBsGjlIpa8tYR1769rTiSN3G0khw09rMgQJXUzJo8kSZIk\nqcqUNl2btXgWf3j2D6yvXw/A+WPOJyKKCk1SN+R7HSVJkiSpypx2wGnURA2NqZGZS2YyaJdBzdMm\nHeRb1iR1LpNHkiRJklRlhvQdwic//Enue/k+7n7x7ubOsQ8cfCAH73FwwdFJ6m5stiZJkiRJVeaF\nVS+wZOUSABKJhtQAwKqNq1i5fmWRoUnqhkweSZIkSVIVSSlx9rSzWbnhg0mi5WuX8/kZny8gKknd\nmckjSZIkSaoi97x0D48tf6zi9D88+weefevZToxIUndn8kiSJEmSqsjjrz++1XmefOPJTohEkjIm\njyRJkiSpiuzaa9etztO/Z/9OiESSMiaPJEmSJKmKTBg1gV61vSpOH9JnCMeNOK7zApLU7Zk8kiRJ\nkqQqMqjPIL593LcrTr9y3JX06lE5uSRJ7c3kkSRJkiRVmW8d/S3+e+J/s99u+zWPO3iPg7nhvBu4\n+NCLC4xMUncUKaWiY9iqww47LD3yyCNFhyFJkiRJnaoxNfLy6peprallWP9hRETRIUnaiUTEoyml\nw7Y2X4/OCEaSJEmStO1qoobhA4cXHYakbs5ma5IkSZIkSarI5JEkSZIkSZIqMnkkSZIkSZKkikwe\nSZIkSZIkqSKTR5IkSZIkSarI5JEkSZIkSZIqMnkkSZIkSZKkikweSZIkSZIkqSKTR5IkSZIkSarI\n5JEkSZIkSZIqMnkkSZIkSZKkikweSZIkSZIkqSKTR5IkSZIkSarI5JEkSZIkSZIqMnkkSZIkSZKk\nikweSZIkSZIkqSKTR5IkSZIkSarI5JEkSZIkSZIqMnkkSZIkSZKkikweSZIkSZIkqSKTR5IkSZIk\nSarI5JEkSZIkSZIqipRS0TFsVUSsAF4sOo4ONBhYWXQQ2i5uu67N7de1uf26Lrdd1+b267rcdl2b\n269rc/t1XTv7thueUhqytZm6RPJoZxcRj6SUDis6Dm07t13X5vbr2tx+XZfbrmtz+3Vdbruuze3X\ntbn9ui63XcZma5IkSZIkSarI5JEkSZIkSZIqMnlUHX5WdADabm67rs3t17W5/bout13X5vbrutx2\nXZvbr2tz+3Vdbjvs80iSJEmSJEmtsOaRJEmSJEmSKqqq5FFE/F1ELIyI+RHxRET8cYGxfD0i+pQZ\nf0VE/HOLcYdGxDPbsY5DI+LUHYxzRESkiPjHknGDI6I+Iq7azmWurTD+0oj43PbG2tWU/g4RcWpE\nPBsR++T7wPqI2KPCvCkiflDy9zcj4opOC7yTVdpftnEZQyPid61MHxgRX2nr/GU+/z8RsSw/rzwZ\nESfsaMztqSscWxHRkP9+CyJiVkQMbKfljoiIBe2xrBbLvSIiXs1jfiIi/qW911Gyrh0+lxdle85X\nETExIv66HdZ9SUSsyLfPwoj4XbnrrtqmxTF6fVG/ZUT8bRHr7erKXUuLuDZExOkR8Xh+rXw6Iv4s\nIo6LiPtbzNcjIt6IiL3yv78ZEYvy/e/Jar+mbY+SY2xh/h3/MiK2614qIr4TESe2Mn2Ht31EHFxy\nDXy7pBw0b0eWqw8qdw+bHyPfze8fmrbD35V8pt32J+2YiNgzIn4bEUsj4tGIuD8iPpOf+1bn22l+\nRMxrcf93SkQ8EhHP5Oe/fy3ye3SGqtlBI+II4HTg4ymlQ4ATgZcLiqUW+DpQruB1HTCpxbjzgd9u\nx6oOBbbphiMiepQZvZTst2tyLrBwO+JpVUrppymlX7f3cqtdnmj4MXBySumlfPRK4BsVPvIecFZE\nDO6M+HYGKaXXUkrntDLLQOAr2zB/OZellA4lO7Z/uh1hfkCF43GbdZFja0NK6dCU0kHA28BXiw6o\nDX6Ux3xoSqnNyY78GrAttvlcXkW2+XyVUpqZUmqvZNzUfPuMAd7ng9dXtV3pMfo+cGlbP7gd+3xr\nyiaPIlM15c6uoKOvDS23SUTUkfXrMSGl9DHgj4A7gLuAD0fEiJKPnwgsSCktj4hLgXHAJ/L97xgg\nOiruAm0oOV+NIzvvX749C0op/UNKqWISpz22fUrpqaZrIDCTvByUUtoiadVeZZnuqpV72H8ChgIH\n59vgU0BdyUfbbX/S9ouIAH4P3JVSGplSGkt2b//hfJa78+10CPAwefk3Ig4CrgIuSikdCBxEdk++\nU6umi/hewMqU0nsAKaWVKaXXACLihaaCbUQcFhF35MNXRMS1EXFbntX9Uj7+uIi4KyKm509Nftp0\ncYyICyLiqfzJyPeaVh4Ra/OnAA8Cf0d2sN8eEbeXBplSWgysii1rRZ0HTMmXMz7PVj6WP/nrl48/\nPCLuyzPLD0XEAOA7wKQ8mzkpInaPiN/nmc0HIuKQku/5s4iYC5S7kGwAnomIw/K/JwHTSr7bhIh4\nMLInSfMiYs98fL+I+GX+e8yPiLNLPvN/81gfKJn/ioj4Zj58R0R8L/8uSyLiU/n42oi4MiIezpf5\nZ/n4vfJt0vRU9FNb2R+qQh7nz4HTUkrPl0z6Bdm2273MxzaRFb7+304IsSpFxPCIuDXfB26NiH3y\n8fvl+9TD+fG2Nh/fXPskIsbk+1VTlv8A4F+A/fJxV7aYvzYi/rVkP/6LrYR3PzCsJNaxEXFnZE8a\n5sTmp6iH58u7P19n0/ouyY/tWcDcfNxlJfv8t/NxfSPiD/lxtCAiJuXj/yU/L82P/AlFi2Pr0Pw3\nmp+fw3bLx5c95grS/Bvm55Fb83PeUxFxRj5+RGRPYn4e2VO1uRGxSz5tbP673E9JEioiepeckx6P\niE/n4y/Jz42zInty+ueRPaF7PP+tyh2HZUXECfnnnoqIX0REr3z8CxHxDxFxD3Buvq/enO8Xd0fE\nR/L5zo3NT9bvioietDiXt8sv3Hkqnq+i8rXjkoi4KiIG5L9b0/W1T0S8HBF1lX6/SiK7eekLvFNp\n3RFRE9m1fkg+T01EPBdZbdshEXFDfhw+HBFH5fMcG5uf+D4eEf3b88erYncD+wNExEWx+Zx6deSJ\notiy3HNEfLCc0j8qX9PLlrMiq+G3S76u35ScB34CPAbsHa2Xwz5Q9ujOYsfKXVs7Nzdvk5JV9gd6\nAG8BpJTeSyktTik1AtezZXL3fLIHqpAlDL+SUno3/9zqlNKvOuhnqQoppTeBPwX+PDJltwNARPxV\nvg2ezI+RphrR5+TDnV4uiIgT83PrFODxfNzFJeeKn5Sc20+Jzfc2UyOib7v8iDuPD9zDAquALwF/\nkVLamI9fk1K6otwCWu5PnRK1mhwPvJ9San6wnFJ6MaX049KZ8u3Sn7ycAvwV8H9TSovyz2xKKf2k\nk2IuTkqpKv4B/YAngCXAT4BjS6a9AAzOhw8D7siHrwCeBHYBBpNleYcCxwEbgZFALXALcE4+7SVg\nCNnF8TbgzHxZCTiv3DrLxHoZ2RNtgE8CD+fDg8mezvTN//4W8A9AT7JM5OH5+F3z9V8CXFWy3B8D\nl+fDxwNPlHzPR4FdysQyAlgATAT+lSxLemvpsoHdoLlz9C8CP8iHvwf8W8mydiv5LSbkw98H/r4k\njm/mw3eULOdUYF4+/Kcl8/cCHgH2Jaul83f5+Fqgf9H7XBv2yXqyGhaHtBh/BfDNfNt+Ox+3tmT6\n2nwbvwAMyOe9oujv04G/09oy42YBF+fDXwB+nw/fCFyQD1/a9Nmm/ThtPg4uzId7kh3fzdPLzP9l\n4AagR/737mXi+R/gnHz4TOC3+XAdcB8wJP97EvCLfHgBcGQ+/C8l67sEeKVpPcB4spvvIEvI30j2\n1PVs4OclMQwAdgcWs/l4HFi6T+XD88nPf2RJiX/Lh8sec529nfPj93qymniQnct2zYcHA8/lv8UI\nssTEofm0aWRPZ1p+xytLfttvAL/Mhz9Cdr7unf/mz5FdtIcAq4FL8/l+BHy9TLxXAK+SXVeeAE7K\nl/UyMCqf59dNnyU7Xv+q5PO3Agfkw38M3JYPPwUMa7H9LqHkXN6V/tHK+YrK147m7wvMAD5dcvxc\n09rv12LdlwAr8u3zBlnCo3Yr6768ZJuNB27Ih38LHJ0P7wM8kw/PAo7Kh/uRnyd2xn9sPkZ75Nvl\ny8CB+W9Ql0/7CfC5fLi53EPlckqla/pxlClnlcaRD48AGoFP5n9vrRz2gbJHd/lH+WvpFWx/uau1\nc3PzNimzzmuAN8kSQxcCNfn4w4HHS9bzZn6c9gfeKfr3K3AbvQPs2cp2OIWsnNEnn9ZUdvgfsnuT\nThKh1lkAAA2USURBVCkXUFIOyv8+kez8v0/+90FktS+aylI/A/4PsAdwZ0n8fwf8bdHbopr+UeYe\nFjik6XjZnv2p6O/Unf4BXyO/ry8z7TiyMucTZOXHRSXn1ceAjxUdf2f/q5qaRymltcBYspPvCmBq\nRFzSho/OSCltSFmW93bgE/n4h1JKS1NKDWQXwKPJLnx3pJRWpJQ2Ab8hu8kDaCC7AW2LKcA5eUa+\n9MnLJ4GPAvdGxBPAxcBwYDSwPKX0cP5d383X39LRwLX5PLcBgyKroQQwM6W0oZWYbiar8ngBMLXF\ntA8DcyLiKbLE15h8/InAfzbNlFJqyqS+T3YDDFnSakSFdf5vmXnGA5/Lv/+DwCDgALJqfp+PrC+N\ng1NKa1r5LtWinuyC/ycVpv8HcHFE7NpyQsqevv2a7ITUHR3B5qac15Lt203jr8+HKzX1vB/424j4\nFjB8K/s9ZPvxT5uOqZTS2xXmuzIilgKTge/m40aTFZhuyffZvyermj+QLMF5X4VYbylZz/j83+Nk\nF5KPkO3zTwEn5k8FP5VSWg28S3bDdU1EnAWsL11ofrwPTCndmY/6FZvPUVD+mOssu+S/0Vtkhd1b\n8vEBfDci5gPzyGokNdUYWJZSeiIffhQYUeY7XluyjtJz4CLgRWBUPu32lD21W0F2IZ+Vj3+Kyr9F\nabO1OWTbe1lKaUk+veXvOxWyJ/bAkcD1+Xe+muzJIsC9wP9EVtO1PZv6FKaV81Wla0epqWyujXA+\n2bW7td/vA59PWXX+D5Fty8u2su5fAE39gHwB+GU+fCJwVb6+mcCukdUyuhf4YUR8jWy/K3ft3Vk0\nHaOPkCVo/hs4gaxs9XA+7QSyhA9sWe6pVE6pdE2H8uWscl5MKT2QD7dWDmtr2aM725ZyV2vn5tJt\nsoWU0hfJ9pOHyJLJv8jHPwz0i4jRZAmRB/JyY5Al/rqrploilbbDiWQPRdZD2TJKkeWC+9Pm7hhO\nJDs+H8m/w7HAfmTn8o8C9+XjL9yO9ezUyt3DkiUdmkXE5/MaXS9HxN4fXMrmWTssULVJRPxnXkvw\n4XxUU7O1vcnKHN8vMLzCVVUb17wAcgdwR15gvJgsU76JzU3serf8WIW/y41v7YDcmK+/LXG+HBEv\nkJ1Yzya7ISZf/i0ppQtK54+s+VlbLqzl4mv63LqtxPR+RDxK9uR+DDChZPKPgR+mlGZGxHFkTzOa\n1lcurvqUp1TJCpeV9pP3yswTZFU057ScOSKOAU4Dro2IK1P19/HSSNYkcV5E/G1K6bulE1NKqyLi\nt5T0xdPCv5ElE35ZYXp30uaCZUrpt5E1oziN7Obxi7TehritBdfLyApZXyMrfI3NP7swpXRE6YxN\nVcJbUXo8BvDPKaWrPxBYxFiyp4H/HBFzU0rfiYhPkBXMzwf+nKyWYVuVO+Y6y4aU0qF5QfZGsuZm\n/0FWkBwCjE0p1efnxqbz9Hsln28gq0XW2vZq7RxduqzGkr8baftvsbVCWdN2rQFW5UmNLaSULo2s\n2fJpwBMR8YF5uqhy56tK145SM8n2793JjqnbyJqflf39Kkkppciagv4FWU2/suvOr79vRMTxZDWa\nLswXUQMcUSbZ/C8R8Qey4/CBiDgxT0zujDa0/M3zava/Sin9TZn5S8s9lY7Lstf0fJtUKn+11PJ8\nWUlbyx7dWZvLXfkD2Ern5q2VKZ8CnoqIa4FlZLUEIXt4ej5Zjbbr8nnfjYh1ETEypbTT9/dRKiJG\nkm2LN6m8HU6mlTJKSmlTgeWClsfmL1JK/1/pDBHxGeDmlNJnt3HZ3UqZe9g/A/aJiP75g69fAr+M\nrAuEsg+eWuxP6jwLye7nAUgpfTWy7nIeKTPvTDY/dFlIVu55ssMjrCJVU/MoIkZH1rdJk0PJnjpD\nVp1+bD58Nls6I7J+MgaRZXmbsoSfiIh989pBk4B7yJ4EHBtZ/wi1ZLV07qS8NWRVcSu5jqy5xPMp\npVfycQ8AR0VEUz8DfSJiFFkVt6ERcXg+vn9k/Tu0XMdd5AXhvGC2Mn8i3FY/AL6VUnqrxfgBZM03\nIEvINZlLdpEiX+fWbpjbYg7w5cg6XSQiRkXW98tw4M2U0s/JnoZ+vB3W1eHyJ0WnAxdGRLkaSD8k\nu0B84IKdP12aRuWaSzuz+8gKQZDt0/fkww+w+Rg+v+WHoPniuTSl9B9kJ+lDaP14nAtcmh9TRCv9\n36Ss34Z/B2oi4iSyquJDIuvskMj6ahmTP01dExGfbC3W3BzgC7G5f7NhEbFHRAwF1qeUJpM1Kf14\nPs+AlNJsso67t7jRy2snvROb+y34LJXPUYXIY/wa8M38OB9AdmzXR9ZH0fCtfH4VsDoimmopXFgy\nufQcOIqs+dHidgx/EVntp/3zv8v+vvl5d1lEnJvHEhHxsXx4v5TSgymlfyDrOH9vtn69qHoVzleV\nrh2ln1tLVkPh34EbU0oNrf1+W3E00NS3XGvrvoasBuG0kuRHy+vZofn/+6Ws09jvkRUEW+17aSd0\nK1lN6T0gOz/m1+OWKpVTyl7T88+UK2cB1DfNX8a2lMPUNpW20Tadm/PP9svLn01Ky+KQlX0vIktu\nzCwZ/8/Af0ZeEzsido2IP92B71T1Iut77adkTXgTlbfDXLIyQp98/O4tllMt5YJ5wHmxuY/ZQZH1\nV3kf2TE7Mh/ft8X9WrdX4R52Mdn9zlUR0Tufr5asiXC5ZbTcn9R5bgN6R8SXS8ZVeltpaTnlSrKW\nEqOguR/Gv+y4MKtDNT3V6Qf8OLLmIpvI2mY3XXi+Dfx3ZK9/fbDF5x4C/kB2k/GPKaXX8o14P9nT\ny4PJbkimp5QaI+JvyJq3BTA7pTSjQjw/A26KiOUppU+XmX49WWG5uXPelNKK/EnPdZF3wkrW/nlJ\nZJ2o/jiyDmM3kFUPvR3468iqgf4z2ZPVX0ZWxXg9FQrrlaSUFlL+LWtXkDUfeJXs5n3ffPw/kV3s\nF5Blur/N5uqv2+sasuqsj0VEkFXfPJMssXdZRNSTtbHuMq9wTSm9nT85uisiVraYtjIiplO5c+wf\nUHJDs5PqExGvlPz9Q7Lkwi8i4jKyfeDz+bSvA5Mj4htkx+3qMsubBFyU7yuvA9/Jt8G9+b56EyXN\nLcn2uVHA/PwzPyd7+0FZeQ2HfyLr32ZOZB1W/kdkNWp6kNXAWEh2E/3ziFhH9jSpXKyklOZGxIHA\n/dkuz1qywvX+ZE3lGsmaQH6ZLMEwIy9IBOX3m4uBn+YFzaUlv13VSCk9HhFPkiXVfgPMiohHyNqE\nt6VWx+fJ9o/1ZAXuJj8h++5PkV0HLkkpvRft1HdkSmljRHye7HzYg+xhQ6U3710I/FdE/D1Z31hT\nyJ4uXZkXEoPsxvxJsiZCzefylFLLpsNdRcvz1RWUv3a0NJXsmnhcybhKv19Lk/JEYg1ZX2KXtGHd\nM8lqSJXWkvoa2fVsPtlxfBdZv2pfz2+cG4Cnyc4f3UZK6el8G8zNkzz1ZLUGX2wx3/sVyimVrulQ\nppyVj/8Z2fn4MbL+UUrXs3wbymHdTblraVtU2kbbc24O4K8i4mqyfWAdm4/Jpv1pPfBoSqm01sp/\nkZXjH86vw/Vk55OdTVPT0Dqya9S1bN5OZbdDSunmPJn9SES8D8xmyzcSVkW5IKX0VGQv/JhXcq64\nNKX0cGQPT6dG9oII8vifbe8YurBK97CrgX8EFkTEGrJj6lfAa/nnWtuf1Eny+4IzgR9FxF+RHbvr\nyPouBvhUvp2CbJt+Mf/c/Ij4Otl9fx+yGoZ/6PQv0MmiKyc3I+s/Z21K6V9bjD+OrJO508t9TlIx\n8pPrhvxEfT5Z59lnFB1XORHRL69VQUT8NbBXSun/KTgsqduL7M2iP0opdYm3du6MLGdJktT9VFPN\nI0k7v7FkVXiD7DWmXyg4ntaclj8h70H2lP6SYsORlCdyv8yWzR0lSZLUwbp0zSNJkiRJkiR1rKrp\nMFuSJEmSJEnVx+SRJEmSJEmSKjJ5JEmSJEmSpIpMHkmSJJURESkiri35u0dErIiIG7dxOS9ExOAd\nnUeSJKkoJo8kSZLKWwccFBG75H+PA14tMB5JkqRCmDySJEmq7CbgtHz4AuC6pgkRsXtE/D4i5kfE\nAxFxSD5+UETMjYjHI+JqIEo+c1FEPBQRT0TE1RFR25lfRpIkaXuYPJIkSapsCnB+RPQGDgEeLJn2\nbeDxlNIhwN8Cv87HXw7ck1L6I2AmsA9ARBwITAKOSikdCjQAF3bKt5AkSdoBPYoOQJIkqVqllOZH\nxAiyWkezW0w+Gjg7n++2vMbRAOAY4Kx8/B8i4p18/hOAscDDEQGwC/BmR38HSZKkHWXySJIkqXUz\ngX8FjgMGlYyPMvOmFv+XCuBXKaW/adfoJEmSOpjN1iRJklr3C+A7KaWnWoy/i7zZWUQcB6xMKb3b\nYvwpwG75/LcC50TEHvm03SNieMeHL0mStGOseSRJktSKlNIrwL+XmXQF8MuImA+sBy7Ox38buC4i\nHgPuBF7Kl/N0RPw9MDciaoB64KvAix37DSRJknZMpFSuVrUkSZIkSZJkszVJkiRJkiS1wuSRJEmS\nJEmSKjJ5JEmSJEmSpIpMHkmSJEmSJKkik0eSJEmSJEmqyOSRJEmSJEmSKjJ5JEmSJEmSpIpMHkmS\nJEmSJKmi/x8ESvHVeTrePwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a14529590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pointplot(x=\"Model\", y=\"Score\", data=model_accuracy, color='green')\n",
    "rcParams['figure.figsize'] = 20, 10\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>83.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Logistic Regression</td>\n",
       "      <td>82.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Perceptron</td>\n",
       "      <td>81.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Linear SVC</td>\n",
       "      <td>81.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SGD</td>\n",
       "      <td>81.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GBC</td>\n",
       "      <td>81.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KNN</td>\n",
       "      <td>79.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Naive Bayes</td>\n",
       "      <td>78.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>77.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>76.54</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  Score\n",
       "0  Support Vector Machines  83.24\n",
       "2      Logistic Regression  82.68\n",
       "5               Perceptron  81.56\n",
       "6               Linear SVC  81.56\n",
       "8                      SGD  81.01\n",
       "9                      GBC  81.01\n",
       "1                      KNN  79.89\n",
       "4              Naive Bayes  78.77\n",
       "3            Random Forest  77.09\n",
       "7            Decision Tree  76.54"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_accuracy.sort_values(by='Score', ascending=False)"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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