From 019274795da26941fa9e8c40999fa26cf5321154 Mon Sep 17 00:00:00 2001
From: Maciej Chmielarz <macchm2@stu.amu.edu.pl>
Date: Fri, 17 Feb 2023 14:01:05 +0100
Subject: [PATCH] Bad final predictions

---
 analysis.ipynb | 970 +++++++++++++++++++++++++++++++++++++++++++++----
 submission.csv | 419 +++++++++++++++++++++
 2 files changed, 1317 insertions(+), 72 deletions(-)
 create mode 100644 submission.csv

diff --git a/analysis.ipynb b/analysis.ipynb
index 362ad8e..934e4ef 100644
--- a/analysis.ipynb
+++ b/analysis.ipynb
@@ -5,7 +5,7 @@
    "id": "0ba6ee9e",
    "metadata": {},
    "source": [
-    "# Titanic_Machine_Learning_from_Disaster"
+    "# Titanic Machine Learning from Disaster"
    ]
   },
   {
@@ -13,7 +13,7 @@
    "id": "ec6e69b1",
    "metadata": {},
    "source": [
-    "# Imports"
+    "## Imports"
    ]
   },
   {
@@ -35,7 +35,7 @@
    "id": "d1b19cf9",
    "metadata": {},
    "source": [
-    "# Data description"
+    "## Data description"
    ]
   },
   {
@@ -45,7 +45,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train = pd.read_csv('train.csv')"
+    "# Loading the data\n",
+    "df_train = pd.read_csv('train.csv')\n",
+    "df_test = pd.read_csv('test.csv')"
    ]
   },
   {
@@ -76,8 +78,33 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "7818fc15",
+   "id": "b2bfda08",
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch',\n",
+       "       'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7818fc15",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -216,7 +243,7 @@
        "max      6.000000  512.329200  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,26 +253,267 @@
    ]
   },
   {
-   "cell_type": "markdown",
-   "id": "07234316",
-   "metadata": {},
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9c83bffc",
+   "metadata": {
+    "scrolled": false
+   },
+   "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>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>418.000000</td>\n",
+       "      <td>418.000000</td>\n",
+       "      <td>332.000000</td>\n",
+       "      <td>418.000000</td>\n",
+       "      <td>418.000000</td>\n",
+       "      <td>417.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>1100.500000</td>\n",
+       "      <td>2.265550</td>\n",
+       "      <td>30.272590</td>\n",
+       "      <td>0.447368</td>\n",
+       "      <td>0.392344</td>\n",
+       "      <td>35.627188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>120.810458</td>\n",
+       "      <td>0.841838</td>\n",
+       "      <td>14.181209</td>\n",
+       "      <td>0.896760</td>\n",
+       "      <td>0.981429</td>\n",
+       "      <td>55.907576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>892.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.170000</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>996.250000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.895800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>1100.500000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>27.000000</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>1204.750000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>1309.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>76.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId      Pclass         Age       SibSp       Parch        Fare\n",
+       "count   418.000000  418.000000  332.000000  418.000000  418.000000  417.000000\n",
+       "mean   1100.500000    2.265550   30.272590    0.447368    0.392344   35.627188\n",
+       "std     120.810458    0.841838   14.181209    0.896760    0.981429   55.907576\n",
+       "min     892.000000    1.000000    0.170000    0.000000    0.000000    0.000000\n",
+       "25%     996.250000    1.000000   21.000000    0.000000    0.000000    7.895800\n",
+       "50%    1100.500000    3.000000   27.000000    0.000000    0.000000   14.454200\n",
+       "75%    1204.750000    3.000000   39.000000    1.000000    0.000000   31.500000\n",
+       "max    1309.000000    3.000000   76.000000    8.000000    9.000000  512.329200"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Analysis before data cleaning"
+    "df_test.describe()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
+   "id": "0b345650",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "af40052a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "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"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.isna().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8cec8cda",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test['Fare'].fillna(df_test['Fare'].mean(), inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "6f612c59",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test['Cabin'].fillna('Other', inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "07234316",
+   "metadata": {},
+   "source": [
+    "## Preexploratory Data Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2facd3d5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(342, 549)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(df_train[df_train['Survived']==1]), len(df_train[df_train['Survived']==0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "id": "1472e369",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x1f232d8f6d0>"
+       "<seaborn.axisgrid.FacetGrid at 0x1e194179a00>"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -268,7 +536,110 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
+   "id": "6b33f748",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Sex', ylabel='Survived'>"
+      ]
+     },
+     "execution_count": 13,
+     "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.barplot(x='Sex', y='Survived', data=df_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "99710899",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Age', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 14,
+     "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.histplot(x='Age', hue='Survived', data=df_train, bins=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "58c6b951",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Pclass', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 15,
+     "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.histplot(x='Pclass', hue='Survived', data=df_train, bins=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
    "id": "538c8c4f",
    "metadata": {},
    "outputs": [],
@@ -281,42 +652,56 @@
    "id": "06684f27",
    "metadata": {},
    "source": [
-    "# Data Cleaning"
+    "## Data Cleaning"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "id": "095ae1ae",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train = pd.get_dummies(data=df_train, columns=['Sex', 'Embarked'])"
+    "df_train = pd.get_dummies(data=df_train, columns=['Sex', 'Embarked'])\n",
+    "df_test = pd.get_dummies(data=df_test, columns=['Sex', 'Embarked'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "id": "50c17ca4",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train.drop(['Sex_male', 'Name', 'Ticket', 'PassengerId'], axis=1, inplace=True)"
+    "# df_train.drop(['Sex_male', 'Name', 'Ticket', 'PassengerId'], axis=1, inplace=True)\n",
+    "df_train.drop('Sex_male', axis=1, inplace=True)\n",
+    "df_test.drop('Sex_male', axis=1, inplace=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
+   "id": "0fdf3229",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train['Age'] = df_train['Age'].fillna(df_train['Age'].mean())\n",
+    "df_test['Age'] = df_test['Age'].fillna(df_train['Age'].mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "id": "b1949435",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train['Cabin'] = df_train['Cabin'].fillna('None')"
+    "df_train['Cabin'] = df_train['Cabin'].fillna('Other')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 21,
    "id": "8a75aa3b",
    "metadata": {
     "scrolled": true
@@ -325,7 +710,7 @@
     {
      "data": {
       "text/plain": [
-       "array(['None', 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
+       "array(['Other', 'C85', 'C123', 'E46', 'G6', 'C103', 'D56', 'A6',\n",
        "       'C23 C25 C27', 'B78', 'D33', 'B30', 'C52', 'B28', 'C83', 'F33',\n",
        "       'F G73', 'E31', 'A5', 'D10 D12', 'D26', 'C110', 'B58 B60', 'E101',\n",
        "       'F E69', 'D47', 'B86', 'F2', 'C2', 'E33', 'B19', 'A7', 'C49', 'F4',\n",
@@ -345,7 +730,7 @@
        "       'C148'], dtype=object)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -356,7 +741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 22,
    "id": "875d207c",
    "metadata": {
     "scrolled": false
@@ -365,7 +750,7 @@
     {
      "data": {
       "text/plain": [
-       "None           687\n",
+       "Other          687\n",
        "C23 C25 C27      4\n",
        "G6               4\n",
        "B96 B98          4\n",
@@ -379,7 +764,7 @@
        "Name: Cabin, Length: 148, dtype: int64"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -390,66 +775,163 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "b10cca7f",
+   "execution_count": 23,
+   "id": "56c071c5",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train['Age'] = df_train['Age'].fillna(df_train['Age'].mean())"
+    "# df_train['Cabin'].str.extract('(\\d+)')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "fdcbf0a5",
+   "execution_count": 24,
+   "id": "d0163d74",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train['Cabin symbol'] = df_train['Cabin'].str.extract('(\\w)')\n",
+    "df_test['Cabin symbol'] = df_test['Cabin'].str.extract('(\\w)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "b0f96907",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "O    327\n",
+       "C     35\n",
+       "B     18\n",
+       "D     13\n",
+       "E      9\n",
+       "F      8\n",
+       "A      7\n",
+       "G      1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test['Cabin'].str.extract('(\\w)').value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "48944d81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "symbol_hist = df_train[df_train['Cabin symbol'] != 'O'][['Cabin symbol', 'Survived']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "7fde7d54",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Cabin symbol', ylabel='Count'>"
+      ]
+     },
+     "execution_count": 27,
+     "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.histplot(x='Cabin symbol', hue='Survived', data=symbol_hist, bins=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "19d57354",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Describe the 'Cabin' with number of people in it\n",
+    "counts_train = df_train['Cabin'].value_counts().copy(deep=True)\n",
+    "counts_test = df_test['Cabin'].value_counts().copy(deep=True)\n",
+    "\n",
+    "# Changing n-people cabin to 'description'\n",
+    "def num_peopl_in_cabin(df, n, description, counts):\n",
+    "    df['Cabin'][df['Cabin'].isin(counts[counts==n].index)] = description"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "fdcbf0a5",
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\Maciej\\AppData\\Local\\Temp/ipykernel_8664/4127688350.py:2: SettingWithCopyWarning: \n",
+      "C:\\Users\\Maciej\\AppData\\Local\\Temp/ipykernel_16012/2825624458.py:7: 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",
-      "  df_train['Cabin'][df_train['Cabin'].isin(counts[counts==1].index)] = 'Alone'\n",
-      "C:\\Users\\Maciej\\AppData\\Local\\Temp/ipykernel_8664/4127688350.py:3: 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",
-      "  df_train['Cabin'][df_train['Cabin'].isin(counts[counts==2].index)] = 'Double room'\n",
-      "C:\\Users\\Maciej\\AppData\\Local\\Temp/ipykernel_8664/4127688350.py:4: 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",
-      "  df_train['Cabin'][df_train['Cabin'].isin(counts[counts==3].index)] = 'Three person room'\n",
-      "C:\\Users\\Maciej\\AppData\\Local\\Temp/ipykernel_8664/4127688350.py:5: 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",
-      "  df_train['Cabin'][df_train['Cabin'].isin(counts[counts==4].index)] = 'Four person room'\n",
-      "C:\\Users\\Maciej\\AppData\\Local\\Temp/ipykernel_8664/4127688350.py:6: 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",
-      "  df_train['Cabin'][df_train['Cabin'].isin(counts[counts>4].index)] = 'Other'\n"
+      "  df['Cabin'][df['Cabin'].isin(counts[counts==n].index)] = description\n"
      ]
     }
    ],
    "source": [
-    "counts = df_train['Cabin'].value_counts().copy(deep=True)\n",
-    "df_train['Cabin'][df_train['Cabin'].isin(counts[counts==1].index)] = 'Alone'\n",
-    "df_train['Cabin'][df_train['Cabin'].isin(counts[counts==2].index)] = 'Double room'\n",
-    "df_train['Cabin'][df_train['Cabin'].isin(counts[counts==3].index)] = 'Three person room'\n",
-    "df_train['Cabin'][df_train['Cabin'].isin(counts[counts==4].index)] = 'Four person room'\n",
-    "df_train['Cabin'][df_train['Cabin'].isin(counts[counts>4].index)] = 'Other'"
+    "\n",
+    "num_peopl_in_cabin(df_train, 1, 'Alone', counts_train)\n",
+    "num_peopl_in_cabin(df_train, 2, 'Double room', counts_train)\n",
+    "num_peopl_in_cabin(df_train, 3, 'Three person room', counts_train)\n",
+    "num_peopl_in_cabin(df_train, 4, 'Four person room', counts_train)\n",
+    "\n",
+    "num_peopl_in_cabin(df_test, 1, 'Alone', counts_test)\n",
+    "num_peopl_in_cabin(df_test, 2, 'Double room', counts_test)\n",
+    "num_peopl_in_cabin(df_test, 3, 'Three person room', counts_test)\n",
+    "\n",
+    "\n",
+    "# df_train['Cabin'][df_train['Cabin'].isin(counts[counts>4].index)] = 'Other'\n",
+    "\n",
+    "# df_train['Cabin'][df_train['Cabin'].isin(counts[counts==1].index)] = 'Alone'\n",
+    "# df_train['Cabin'][df_train['Cabin'].isin(counts[counts==2].index)] = 'Double room'\n",
+    "# df_train['Cabin'][df_train['Cabin'].isin(counts[counts==3].index)] = 'Three person room'\n",
+    "# df_train['Cabin'][df_train['Cabin'].isin(counts[counts==4].index)] = 'Four person room'\n",
+    "# df_train['Cabin'][df_train['Cabin'].isin(counts[counts>4].index)] = 'Other'"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 30,
    "id": "6d73e794",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
@@ -462,7 +944,7 @@
        "Name: Cabin, dtype: int64"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -473,12 +955,219 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 31,
    "id": "ae86ac06",
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_train = pd.get_dummies(data=df_train, columns=['Cabin'])"
+    "df_train = pd.get_dummies(data=df_train, columns=['Cabin'])\n",
+    "df_test = pd.get_dummies(data=df_test, columns=['Cabin', 'Pclass', 'Cabin symbol'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "702cf4b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train = pd.get_dummies(data=df_train, columns=['Pclass'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "2c5e0c67",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train = pd.get_dummies(data=df_train, columns=['Cabin symbol'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "76796bf2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train = df_train.drop('Cabin symbol_O', axis=1)\n",
+    "df_test = df_test.drop('Cabin symbol_O', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "4be06019",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare',\n",
+       "       'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Cabin_Alone',\n",
+       "       'Cabin_Double room', 'Cabin_Other', 'Cabin_Three person room',\n",
+       "       'Pclass_1', 'Pclass_2', 'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B',\n",
+       "       'Cabin symbol_C', 'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F',\n",
+       "       'Cabin symbol_G'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "c9e20ea5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare',\n",
+       "       'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S', 'Cabin_Alone',\n",
+       "       'Cabin_Double room', 'Cabin_Other', 'Cabin_Three person room',\n",
+       "       'Pclass_1', 'Pclass_2', 'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B',\n",
+       "       'Cabin symbol_C', 'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F',\n",
+       "       'Cabin symbol_G'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.columns[df_test.columns.isin(df_train.columns)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "a6a53564",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Survived', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
+       "       'Fare', 'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S',\n",
+       "       'Cabin_Alone', 'Cabin_Double room', 'Cabin_Four person room',\n",
+       "       'Cabin_Other', 'Cabin_Three person room', 'Pclass_1', 'Pclass_2',\n",
+       "       'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B', 'Cabin symbol_C',\n",
+       "       'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F', 'Cabin symbol_G',\n",
+       "       'Cabin symbol_T'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "b299afb0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test['Cabin symbol_T'] = 0\n",
+    "df_test['Cabin_Four person room'] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "5c4f00b0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_train = df_train.reindex(columns=['PassengerId', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
+    "       'Fare', 'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S',\n",
+    "       'Cabin_Alone', 'Cabin_Double room', 'Cabin_Four person room',\n",
+    "       'Cabin_Other', 'Cabin_Three person room', 'Pclass_1', 'Pclass_2',\n",
+    "       'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B', 'Cabin symbol_C',\n",
+    "       'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F', 'Cabin symbol_G',\n",
+    "       'Cabin symbol_T', 'Survived'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "086a50d2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_test = df_test.reindex(columns=['PassengerId', 'Name', 'Age', 'SibSp', 'Parch', 'Ticket',\n",
+    "       'Fare', 'Sex_female', 'Embarked_C', 'Embarked_Q', 'Embarked_S',\n",
+    "       'Cabin_Alone', 'Cabin_Double room', 'Cabin_Four person room',\n",
+    "       'Cabin_Other', 'Cabin_Three person room', 'Pclass_1', 'Pclass_2',\n",
+    "       'Pclass_3', 'Cabin symbol_A', 'Cabin symbol_B', 'Cabin symbol_C',\n",
+    "       'Cabin symbol_D', 'Cabin symbol_E', 'Cabin symbol_F', 'Cabin symbol_G',\n",
+    "       'Cabin symbol_T'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "55271503",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "PassengerId                0\n",
+       "Name                       0\n",
+       "Age                        0\n",
+       "SibSp                      0\n",
+       "Parch                      0\n",
+       "Ticket                     0\n",
+       "Fare                       0\n",
+       "Sex_female                 0\n",
+       "Embarked_C                 0\n",
+       "Embarked_Q                 0\n",
+       "Embarked_S                 0\n",
+       "Cabin_Alone                0\n",
+       "Cabin_Double room          0\n",
+       "Cabin_Four person room     0\n",
+       "Cabin_Other                0\n",
+       "Cabin_Three person room    0\n",
+       "Pclass_1                   0\n",
+       "Pclass_2                   0\n",
+       "Pclass_3                   0\n",
+       "Cabin symbol_A             0\n",
+       "Cabin symbol_B             0\n",
+       "Cabin symbol_C             0\n",
+       "Cabin symbol_D             0\n",
+       "Cabin symbol_E             0\n",
+       "Cabin symbol_F             0\n",
+       "Cabin symbol_G             0\n",
+       "Cabin symbol_T             0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_test.isna().sum()"
    ]
   },
   {
@@ -486,18 +1175,20 @@
    "id": "7ed9bfbb",
    "metadata": {},
    "source": [
-    "# Analysis"
+    "## Views of the best correlated features"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 42,
    "id": "3c82db70",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 2 Axes>"
       ]
@@ -508,10 +1199,145 @@
    ],
    "source": [
     "corrmat = df_train.corr() \n",
-    "cols = corrmat.nlargest(15, 'Survived')['Survived'].index \n",
+    "cols = corrmat.nlargest(df_train.shape[1], 'Survived')['Survived'].index \n",
     "cm = np.corrcoef(df_train[cols].values.T) \n",
-    "sns.set(font_scale=1.25) \n",
-    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 8}, yticklabels=cols.values, xticklabels=cols.values)"
+    "sns.set(font_scale=0.7) \n",
+    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 5}, yticklabels=cols.values, xticklabels=cols.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "972b9982",
+   "metadata": {},
+   "source": [
+    "## Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "f8ff6bbd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from imblearn.over_sampling import RandomOverSampler\n",
+    "from sklearn.model_selection import train_test_split"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "ae38bd90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train = df_train.drop(['PassengerId', 'Name', 'Ticket', 'Survived'], axis=1)\n",
+    "y_train = df_train['Survived']\n",
+    "\n",
+    "X_test = df_test.drop(['PassengerId', 'Name', 'Ticket'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "79af2916",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ros = RandomOverSampler()\n",
+    "X_train, y_train = ros.fit_resample(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "5ef85114",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_tr, X_val, y_tr, y_val = train_test_split(X_train, y_train, test_size=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "f93f38a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(549, 549)"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(y_train[y_train==1]), len(y_train[y_train==0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "09d2817d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc = StandardScaler()\n",
+    "\n",
+    "X_train_std = sc.fit_transform(X_tr)\n",
+    "X_val = sc.transform(X_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "96fe96d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7818181818181819"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "clf = LogisticRegression()\n",
+    "\n",
+    "clf.fit(X_train_std, y_tr)\n",
+    "clf.score(X_val, y_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "dda1026c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_test = sc.transform(X_test)\n",
+    "predictions = clf.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "ea78bc69",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "submissionStacking = pd.DataFrame({ 'PassengerId': df_test[\"PassengerId\"],'Survived': predictions })\n",
+    "submissionStacking.to_csv(\"submission.csv\", index=False)"
    ]
   }
  ],
diff --git a/submission.csv b/submission.csv
new file mode 100644
index 0000000..3164e5d
--- /dev/null
+++ b/submission.csv
@@ -0,0 +1,419 @@
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