Kaggle_Titanic_Github_3_15.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I took some online tutorial articles as reference and combined the advantages of them to get my own. Here we go."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import all the importatnt libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os \n",
    "import tarfile \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read train and test data from local disk."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"/Users/rileychiu/Desktop/Kaggle/titanic/train.csv\")\n",
    "test = pd.read_csv(\"/Users/rileychiu/Desktop/Kaggle/titanic/test.csv\")\n",
    "submit = pd.read_csv('/Users/rileychiu/Desktop/Kaggle/titanic/gender_submission.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the details of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": [
    "test.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are some missing values in each. We will fill them out later."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first want to preprocess the data and also perform some EDA(Exploratory data analysis) on the data. We combine the train and the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1309 entries, 0 to 417\n",
      "Data columns (total 12 columns):\n",
      "Age            1046 non-null float64\n",
      "Cabin          295 non-null object\n",
      "Embarked       1307 non-null object\n",
      "Fare           1308 non-null float64\n",
      "Name           1309 non-null object\n",
      "Parch          1309 non-null int64\n",
      "PassengerId    1309 non-null int64\n",
      "Pclass         1309 non-null int64\n",
      "Sex            1309 non-null object\n",
      "SibSp          1309 non-null int64\n",
      "Survived       891 non-null float64\n",
      "Ticket         1309 non-null object\n",
      "dtypes: float64(3), int64(4), object(5)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "data = train.append(test)\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x122574550>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10fbca4e0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['Pclass'], hue=data['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10eecb710>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['Sex'], hue=data['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1246c7a20>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data['Embarked'], hue=data['Survived'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x1247988d0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data, col='Survived')\n",
    "g.map(sns.distplot, 'Age', kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x124940c88>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data, col='Survived')\n",
    "g.map(sns.distplot, 'Fare', kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x124b8d8d0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/bJdozk+y+xzUYW+ML3tjSL0H1MRLsgfwVeBdVfXwXNczCkmOB+6tquvnupZZsAA4DDi3qg4FfgpMzPs/PbE3xs7Ie6P3gJro2yYl2ZlBA36xqr421/WM0DHAa5PcyeDS0yuS/NPcljQyG4GNVbXlL/qLGTTljmZvjCd7Yxv0HlATe9ukJGFwrXZdVX1srusZpap6X1Utqar9GPx/dmVVvWmOyxqJqvohsCHJQW3oWObmq2TsjTFkb2ybObub+TBm6bZJvTgGeDNwS5Ib29j7q+qyOaxJw3kn8MUWDHcAb9vRBdgb6tRIe6Prj5lLkuav3i/xSZLmKQNKktQlA0qS1CUDSpLUJQNKktQlA2qMJXkyyY1JvpPkK0mePYJ9vjXJp0dRnzRX7I3JYECNt0er6pCq+j3gceDtwz6x3Q1bmlT2xgQwoCbHNcABAEn+Jcn17bt0VmyZkOSRJH+f5Cbg6CQvSfJfSW5K8q321QYAL0hyeZLvJ/m7OTgWaZTsjTHV9Z0kNJwkC4DjgMvb0J9V1Y+TPAu4LslXq+p+YHdgTVW9u/1L7+8Cr6+q65LsCTzann8IgztIPwbcnuRTVbUBaczYG+PNgBpvz5pyK5hraN/DAvxVkj9py0uBA4H7gScZ3IAT4CDgnqq6DmDL3aIHt0Hjiqp6qK3fBvw2v/7VDlLv7I0JYECNt0er6pCpA0leDrwSOLqqfpbkamC3tvnnVfXkEPt9bMryk/h7ovFjb0wA34OaPHsBD7QGfBFw1Fbm3Q4sSvISgCTPaZdDpEllb4wZ/0efPJcDb0+yjkGjXTvdpKp6PMnrgU+16/GPMvjrUppU9saY8W7mkqQueYlPktQlA0qS1CUDSpLUJQNKktQlA0qS1CUDSpLUJQNKktSl/weMevn+CH7ttwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data, col='Survived')\n",
    "g.map(sns.distplot, 'Parch', kde=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x124d5c978>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.FacetGrid(data, col='Survived')\n",
    "g.map(sns.distplot, 'SibSp', kde=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filling the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Embarked'] = data['Embarked'].fillna('S')\n",
    "data.loc[data.Fare.isnull(), 'Fare'] = data[(data.Pclass==1)&(data.Embarked==\"S\")&(data.Sex==\"male\")].dropna().Fare.mean()\n",
    "data[\"Cabin\"] = data['Cabin'].apply(lambda x : str(x)[0] if not pd.isnull(x) else 'NoCabin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Age vs Survived in Pclass = 1 and  2')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Has_Age'] = data['Age'].isnull().map(lambda x : 0 if x == True else 1)\n",
    "fig, [ax1, ax2] = plt.subplots(1, 2)\n",
    "fig.set_figwidth(18)\n",
    "ax1 = sns.countplot(data['Pclass'],hue=data['Has_Age'],ax=ax1)\n",
    "ax2 = sns.countplot(data['Sex'],hue=data['Has_Age'],ax=ax2)\n",
    "pd.crosstab(data['Has_Age'],data['Sex'],margins=True).round(3)\n",
    "# Masks\n",
    "Mask_Has_Age_P12_Survived = ( (data.Has_Age == 1) & (data.Pclass != 3 ) & (data.Survived == 1) )\n",
    "Mask_Has_Age_P12_Dead = ( (data.Has_Age == 1) & (data.Pclass != 3 ) & (data.Survived == 0) )\n",
    "# Plot\n",
    "fig, ax = plt.subplots( figsize = (15,9) )\n",
    "ax = sns.distplot(data.loc[Mask_Has_Age_P12_Survived, 'Age'],kde=False,bins=10,norm_hist=True,label='Survived') \n",
    "ax = sns.distplot(data.loc[Mask_Has_Age_P12_Dead, 'Age'],kde=False,bins=10,norm_hist=True,label='Dead')\n",
    "ax.legend()\n",
    "ax.set_title('Age vs Survived in Pclass = 1 and  2',fontsize = 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We extract a new feature \"Title\" from \"Name\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Title\n",
       "0    29.0\n",
       "1    47.0\n",
       "2     4.0\n",
       "3    22.0\n",
       "4    36.0\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extracted title using name\n",
    "data['Title'] = data.Name.str.extract(' ([A-Za-z]+)\\.', expand=False)\n",
    "data['Title'] = data['Title'].replace(['Capt', 'Col', 'Countess', 'Don',\n",
    "                                               'Dr', 'Dona', 'Jonkheer', \n",
    "                                                'Major','Rev','Sir'],'Rare') \n",
    "data['Title'] = data['Title'].replace(['Mlle', 'Ms','Mme'],'Miss')\n",
    "data['Title'] = data['Title'].replace(['Lady'],'Mrs')\n",
    "data['Title'] = data['Title'].map({\"Mr\":0, \"Rare\" : 1, \"Master\" : 2,\"Miss\" : 3, \"Mrs\" : 4 })\n",
    "Ti = data.groupby('Title')['Age'].median()\n",
    "Ti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If age is less then 16, high posibility to survive. If age is greater than 60, high posibility not to survive. Extract a new feature \"Ti_Minor\" and \"Ti_Older\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ti_pred = data.groupby('Title')['Age'].median().values\n",
    "data['Ti_Age'] = data['Age']\n",
    "# Filling the missing age\n",
    "for i in range(0,5):\n",
    " # 0 1 2 3 4 5\n",
    "    data.loc[(data.Age.isnull()) & (data.Title == i),'Ti_Age'] = Ti_pred[i]\n",
    "data['Ti_Age'] = data['Ti_Age'].astype('int')\n",
    "data['Ti_Minor'] = ((data['Ti_Age']) < 16.0) * 1\n",
    "data['Ti_Older'] = ((data['Ti_Age']) > 60.0) * 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To prevent overfitting, make 5 bins on \"Fare\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making Bins\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "data['FareBin_5'] = pd.qcut(data['Fare'], 5)\n",
    "\n",
    "label = LabelEncoder()\n",
    "data['FareBin_Code_5'] = label.fit_transform(data['FareBin_5'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert \"Sex_code\" from categorical feature . to numerical feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert Sex\n",
    "data['Sex_Code'] = data['Sex'].map({'female' : 1, 'male' : 0}).astype('int')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of using \"SibSp\" and \"Parch\", we compute the whole family size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Family_size'] = data['SibSp'] + data['Parch'] + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If people have same kind of tickets, their survival rate might be related."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "duplicate_ticket = []\n",
    "for tk in data.Ticket.unique():\n",
    "    tem = data.loc[data.Ticket == tk, 'Fare']\n",
    "    #print(tem.count())\n",
    "    if tem.count() > 1:\n",
    "        #print(df_data.loc[df_data.Ticket == tk,['Name','Ticket','Fare']])\n",
    "        duplicate_ticket.append(data.loc[data.Ticket == tk,['Name','Ticket','Fare','Cabin','Family_size','Survived']])\n",
    "duplicate_ticket = pd.concat(duplicate_ticket)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "people keep the same ticket: 596 \n",
      "people have connected information : 496\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connected_Survival</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>0.225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.5</th>\n",
       "      <td>0.298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.0</th>\n",
       "      <td>0.728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Survived\n",
       "Connected_Survival          \n",
       "0.0                    0.225\n",
       "0.5                    0.298\n",
       "1.0                    0.728"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the same ticket family or friends\n",
    "data['Connected_Survival'] = 0.5 # default \n",
    "for _, grp in data.groupby('Ticket'):\n",
    "    if (len(grp) > 1):\n",
    "        for ind, row in grp.iterrows():\n",
    "            smax = grp.drop(ind)['Survived'].max()\n",
    "            smin = grp.drop(ind)['Survived'].min()\n",
    "            passID = row['PassengerId']\n",
    "            if (smax == 1.0):\n",
    "                data.loc[data['PassengerId'] == passID, 'Connected_Survival'] = 1\n",
    "            elif (smin==0.0):\n",
    "                data.loc[data['PassengerId'] == passID, 'Connected_Survival'] = 0\n",
    "#print\n",
    "print('people keep the same ticket: %.0f '%len(duplicate_ticket))\n",
    "print(\"people have connected information : %.0f\" \n",
    "      %(data[data['Connected_Survival']!=0.5].shape[0]))\n",
    "data.groupby('Connected_Survival')[['Survived']].mean().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr_all = data.corr()\n",
    "\n",
    "# Generate a mask for the upper triangle\n",
    "#mask = np.zeros_like(corr_all, dtype = np.bool)\n",
    "#mask[np.triu_indices_from(mask)] = True\n",
    "\n",
    "# Set up the matplotlib figure\n",
    "f, ax = plt.subplots(figsize = (11, 9))\n",
    "\n",
    "# Draw the heatmap with the mask and correct aspect ratio\n",
    "sns.heatmap(corr_all,\n",
    "            square = True, linewidths = .5, ax = ax, cmap = \"BuPu\")      \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select and train models + Fine-Tune hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seperate train and test sets. I want to use an ensembling method (stacking). Therefore, I also seperate the feature and target in the train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = data[:len(train)]\n",
    "test = data[len(train):]\n",
    "# Training set and labels\n",
    "X = train.drop(labels=['Survived','PassengerId'],axis=1)\n",
    "Y = train['Survived']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the features that I feel useful for training and prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = ['Sex_Code','Pclass','FareBin_Code_5','Family_size', 'Ti_Minor', 'Ti_Older', 'Connected_Survival']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the train and validation sets for the first layer of the stacking model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "training, valid, ytraining, yvalid=train_test_split(X,Y,test_size=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In stacking, I use two layers. The first layer contains two base models (random forest and KNN). And the second layer uses random forest as the meta model. And I use GridSearch to fine-tune the hyperparameters of each model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'n_estimators': [100, 250, 500]}, {'min_samples_split': [10, 20, 100]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid=[{'n_estimators':[100,250,500]},{'min_samples_split':[10,20,100]}]\n",
    "rfc=RandomForestClassifier()\n",
    "rfc_search=GridSearchCV(rfc,param_grid,cv=5,scoring='neg_mean_squared_error')\n",
    "rfc_search.fit(training[new],ytraining)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=20,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit data to random forest in the first layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=1, min_samples_split=2,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=None,\n",
    "            oob_score=False, random_state=None, verbose=0,\n",
    "            warm_start=False)\n",
    "model1.fit(training[new],ytraining)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'n_neighbors': [3, 5, 10]}, {'leaf_size': [20, 30, 100]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid=[{'n_neighbors':[3,5,10]},{'leaf_size':[20,30,100]}]\n",
    "knn=KNeighborsClassifier()\n",
    "knn_search=GridSearchCV(knn,param_grid,cv=5,scoring='neg_mean_squared_error')\n",
    "knn_search.fit(training[new],ytraining)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=100, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit data to KNN in the first layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=20, metric='minkowski',\n",
       "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2=KNeighborsClassifier(algorithm='auto', leaf_size=20, metric='minkowski',\n",
    "           metric_params=None, n_jobs=None, n_neighbors=5, p=2,\n",
    "           weights='uniform')\n",
    "model2.fit(training[new],ytraining)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred1=model1.predict(valid[new])\n",
    "pred2=model2.predict(valid[new])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred1=model1.predict(test[new])\n",
    "test_pred2=model2.predict(test[new])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "stacked_pred=np.column_stack((pred1,pred2))\n",
    "stacked_test_pred=np.column_stack((test_pred1,test_pred2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid=[{'n_estimators': [100, 250, 500]}, {'min_samples_split': [10, 20, 100]}],\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',\n",
       "       scoring='neg_mean_squared_error', verbose=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_grid=[{'n_estimators':[100,250,500]},{'min_samples_split':[10,20,100]}]\n",
    "meta_rfc=RandomForestClassifier()\n",
    "meta_rfc_search=GridSearchCV(meta_rfc,param_grid,cv=5,scoring='neg_mean_squared_error')\n",
    "meta_rfc_search.fit(stacked_pred,yvalid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta_rfc_search.best_estimator_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output the final prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit data to random forest (meta model) in the second layer and output the result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "meta_model=RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
    "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
    "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
    "            min_samples_leaf=1, min_samples_split=20,\n",
    "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
    "            oob_score=False, random_state=None, verbose=0,\n",
    "            warm_start=False)\n",
    "meta_model.fit(stacked_pred,yvalid)\n",
    "new_pred =meta_model.predict(stacked_test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the result a visible data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>892</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>893</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>894</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>896</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived\n",
       "0          892         0\n",
       "1          893         1\n",
       "2          894         0\n",
       "3          895         0\n",
       "4          896         1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submit = pd.DataFrame({\"PassengerId\": test['PassengerId'],\n",
    "                      \"Survived\":new_pred.astype(int)})\n",
    "submit.head()"
   ]
  }
 ],
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