Titanic_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook there is my code for the Titanic ML competition on Kaggle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Survived  Pclass  \\\n",
       "PassengerId                     \n",
       "1                   0       3   \n",
       "2                   1       1   \n",
       "3                   1       3   \n",
       "4                   1       1   \n",
       "5                   0       3   \n",
       "\n",
       "                                                          Name     Sex   Age  \\\n",
       "PassengerId                                                                    \n",
       "1                                      Braund, Mr. Owen Harris    male  22.0   \n",
       "2            Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0   \n",
       "3                                       Heikkinen, Miss. Laina  female  26.0   \n",
       "4                 Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0   \n",
       "5                                     Allen, Mr. William Henry    male  35.0   \n",
       "\n",
       "             SibSp  Parch            Ticket     Fare Cabin Embarked  \n",
       "PassengerId                                                          \n",
       "1                1      0         A/5 21171   7.2500   NaN        S  \n",
       "2                1      0          PC 17599  71.2833   C85        C  \n",
       "3                0      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "4                1      0            113803  53.1000  C123        S  \n",
       "5                0      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "train_titanic = pd.read_csv('datasets/titanic_train.csv', index_col = 'PassengerId')\n",
    "test_titanic = pd.read_csv('datasets/test-2.csv', index_col = 'PassengerId')\n",
    "\n",
    "data = [train_titanic, test_titanic]\n",
    "\n",
    "train_titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 891 entries, 1 to 891\n",
      "Data columns (total 11 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Survived  891 non-null    int64  \n",
      " 1   Pclass    891 non-null    int64  \n",
      " 2   Name      891 non-null    object \n",
      " 3   Sex       891 non-null    object \n",
      " 4   Age       714 non-null    float64\n",
      " 5   SibSp     891 non-null    int64  \n",
      " 6   Parch     891 non-null    int64  \n",
      " 7   Ticket    891 non-null    object \n",
      " 8   Fare      891 non-null    float64\n",
      " 9   Cabin     204 non-null    object \n",
      " 10  Embarked  889 non-null    object \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 83.5+ KB\n"
     ]
    }
   ],
   "source": [
    "train_titanic.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>714.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Survived      Pclass         Age       SibSp       Parch        Fare\n",
       "count  891.000000  891.000000  714.000000  891.000000  891.000000  891.000000\n",
       "mean     0.383838    2.308642   29.699118    0.523008    0.381594   32.204208\n",
       "std      0.486592    0.836071   14.526497    1.102743    0.806057   49.693429\n",
       "min      0.000000    1.000000    0.420000    0.000000    0.000000    0.000000\n",
       "25%      0.000000    2.000000   20.125000    0.000000    0.000000    7.910400\n",
       "50%      0.000000    3.000000   28.000000    0.000000    0.000000   14.454200\n",
       "75%      1.000000    3.000000   38.000000    1.000000    0.000000   31.000000\n",
       "max      1.000000    3.000000   80.000000    8.000000    6.000000  512.329200"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_titanic.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From a first look at the data, we can see that \n",
    "- only the 38% of the passengers in the train set have survived. \n",
    "- More than half of the passenger were in 3rd class and\n",
    "- the average age is around 30 years. \n",
    "- Most of the passengers travel alone: the SibSp and Parch columns (that represent the number of relatives on the boat) are non zero only from the 75 percentile.\n",
    "- The fare have a very high variance.\n",
    "\n",
    "Let us first create a new variable: the total number of relatives of a passenger. Then we assign to the ages that are null the median age of the group of passengers with same number of relatives and class of the train set, which is more representative. We assign the same value also to the test set. Finally, we deal with the remaining missing values in the Age column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/numpy/lib/nanfunctions.py:1111: RuntimeWarning: Mean of empty slice\n",
      "  return np.nanmean(a, axis, out=out, keepdims=keepdims)\n",
      "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/ipykernel_launcher.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  if __name__ == '__main__':\n",
      "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/ipykernel_launcher.py:26: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "/opt/anaconda3/envs/tf2/lib/python3.7/site-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
     ]
    }
   ],
   "source": [
    "train_titanic['Relatives'] = train_titanic['SibSp']+train_titanic['Parch']\n",
    "test_titanic['Relatives'] = test_titanic['SibSp']+test_titanic['Parch']\n",
    "    \n",
    "median = train_titanic.groupby(['Relatives','Pclass'])['Age'].median()\n",
    "train_titanic['Age'] = train_titanic.groupby(['Relatives','Pclass'])['Age'].apply(lambda x: x.fillna(x.median()))\n",
    "\n",
    "for k in test_titanic.index:\n",
    "    if test_titanic['Age'].isnull()[k]:\n",
    "        test_titanic['Age'][k]= median[test_titanic['Relatives'][k]][test_titanic['Pclass'][k]]\n",
    "\n",
    "\n",
    "# train_titanic[train_titanic['Age'].isnull()==True]\n",
    "\n",
    "# We see that there are still NaNs in the \"Age\" column.\n",
    "# They have all Relatives == 10: sibsp==8 and Parch==2. \n",
    "# They are probably brothers of the same family. \n",
    "# I drop it from the train \n",
    "rel10 = train_titanic[train_titanic['Relatives']==10].index\n",
    "train_titanic.drop(rel10, inplace=True)\n",
    "\n",
    "# In the test set, there remain three NaN values.\n",
    "# One has 8 SibSp. I suppose he is young.\n",
    "# The other two have only one SibSp and 9 Parch. I suppose they are the parents.\n",
    "\n",
    "test_titanic['Age'][1080] = 14.5 # same value of the other one with rel==10 in the test and sibsp==8, presumibly a brother.\n",
    "test_titanic['Age'][1234] = 40\n",
    "test_titanic['Age'][1257] = 40\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 892 to 1309\n",
      "Data columns (total 11 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   Pclass     418 non-null    int64  \n",
      " 1   Name       418 non-null    object \n",
      " 2   Sex        418 non-null    object \n",
      " 3   Age        418 non-null    float64\n",
      " 4   SibSp      418 non-null    int64  \n",
      " 5   Parch      418 non-null    int64  \n",
      " 6   Ticket     418 non-null    object \n",
      " 7   Fare       417 non-null    float64\n",
      " 8   Cabin      91 non-null     object \n",
      " 9   Embarked   418 non-null    object \n",
      " 10  Relatives  418 non-null    int64  \n",
      "dtypes: float64(2), int64(4), object(5)\n",
      "memory usage: 55.4+ KB\n"
     ]
    }
   ],
   "source": [
    "test_titanic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us fill the missing values of the \"Fare\" column on the test set with the average fare.\n",
    "We also reduce skewness of Fare by applying a logarithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "M = test_titanic['Fare'][test_titanic['Fare'].isnull()==False].mean()\n",
    "test_titanic['Fare'] = test_titanic['Fare'].fillna(M)\n",
    "\n",
    "train_titanic['Fare'] = np.log(train_titanic['Fare']+0.01)\n",
    "test_titanic['Fare'] = np.log(test_titanic['Fare']+0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now create new features:\n",
    "- extract the title from the name and turn it into dummy variables.\n",
    "- let Age_classe be the age of passengers per the class\n",
    "- Fare_Per_Person is the fare divided by the relatives on board\n",
    "- turn 'Sex' into a numerical variable\n",
    "- and bin \"Fare\" to reduce noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_titanic['Title']= train_titanic['Name'].str.extract(pat= ' ([A-Za-z]+)\\.')\n",
    "train_titanic = pd.get_dummies(train_titanic,columns=['Title'])\n",
    "\n",
    "test_titanic['Title']= test_titanic['Name'].str.extract(pat= ' ([A-Za-z]+)\\.')\n",
    "test_titanic = pd.get_dummies(test_titanic,columns=['Title'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_titanic['Age_Class']= train_titanic['Age']* train_titanic['Pclass']\n",
    "test_titanic['Age_Class']= test_titanic['Age']* test_titanic['Pclass']\n",
    "\n",
    "train_titanic['Fare_Per_Person'] = train_titanic['Fare']/(train_titanic['Relatives']+1)\n",
    "test_titanic['Fare_Per_Person']= test_titanic['Fare']/(test_titanic['Relatives']+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender = {\"male\": 0, \"female\": 1}\n",
    "train_titanic['Sex'] = train_titanic['Sex'].map(gender)\n",
    "test_titanic['Sex'] = test_titanic['Sex'].map(gender)\n",
    "\n",
    "train_titanic['Fare']=pd.qcut(train_titanic['Fare'],5,labels=False)\n",
    "test_titanic['Fare']=pd.qcut(test_titanic['Fare'],5,labels=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are all the columns of our train set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
       "       'Fare', 'Cabin', 'Embarked', 'Relatives', 'Title_Capt', 'Title_Col',\n",
       "       'Title_Countess', 'Title_Don', 'Title_Dr', 'Title_Jonkheer',\n",
       "       'Title_Lady', 'Title_Major', 'Title_Master', 'Title_Miss', 'Title_Mlle',\n",
       "       'Title_Mme', 'Title_Mr', 'Title_Mrs', 'Title_Ms', 'Title_Rev',\n",
       "       'Title_Sir', 'Age_Class', 'Fare_Per_Person'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_titanic.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now we're ready for the ML!\n",
    "\n",
    "We keep only the columns that after some trials revealed to be the most important\n",
    "and extract our final dataframes. Then, we implement a Random Forest Classifier with max_depth=5, n_estimators=100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "finals =  ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', \n",
    "       'Fare',  'Relatives',\n",
    "         'Title_Col', 'Title_Dr',  'Title_Master', 'Title_Miss', \n",
    "           'Title_Mr', 'Title_Mrs', 'Title_Ms', 'Title_Rev',\n",
    "       'Age_Class', 'Fare_Per_Person']\n",
    "\n",
    "X = train_titanic[finals]\n",
    "y = train_titanic['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8461538461538461"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_ = RandomForestClassifier(max_depth=5,n_estimators=100)\n",
    "forest_.fit(X_train, y_train)\n",
    "forest_.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "feat = forest_.feature_importances_\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.title('Feature Importance')\n",
    "sns.barplot(data={'importance': feat,\n",
    "                  'feature':finals},y='feature',x='importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The final model for submission: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestClassifier(max_depth=5,n_estimators=100)\n",
    "forest.fit(X, y)\n",
    "ypred = forest.predict(test_titanic[finals])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "ext = pd.DataFrame({'Survived': ypred},test_titanic.index)\n",
    "ext.to_csv(r'Ready_for_Submission')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scoring from Kaggle for this code: 0.77033. \n",
    "\n",
    "Max score obtained: 0.78468"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}