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Maciej Chmielarz 019274795d Bad final predictions
2023-02-17 14:01:05 +01:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "0ba6ee9e",
"metadata": {},
"source": [
"# Titanic Machine Learning from Disaster"
]
},
{
"cell_type": "markdown",
"id": "ec6e69b1",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ffcae455",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import plotly.express as px"
]
},
{
"cell_type": "markdown",
"id": "d1b19cf9",
"metadata": {},
"source": [
"## Data description"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "3174342e",
"metadata": {},
"outputs": [],
"source": [
"# Loading the data\n",
"df_train = pd.read_csv('train.csv')\n",
"df_test = pd.read_csv('test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "4d561fad",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',\n",
" 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],\n",
" dtype='object')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.columns"
]
},
{
"cell_type": "code",
"execution_count": 4,
"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": {
"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",
" <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>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>446.000000</td>\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>257.353842</td>\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>1.000000</td>\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>223.500000</td>\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>446.000000</td>\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>668.500000</td>\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>891.000000</td>\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": [
" PassengerId Survived Pclass Age SibSp \\\n",
"count 891.000000 891.000000 891.000000 714.000000 891.000000 \n",
"mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n",
"std 257.353842 0.486592 0.836071 14.526497 1.102743 \n",
"min 1.000000 0.000000 1.000000 0.420000 0.000000 \n",
"25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n",
"50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n",
"75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n",
"max 891.000000 1.000000 3.000000 80.000000 8.000000 \n",
"\n",
" Parch Fare \n",
"count 891.000000 891.000000 \n",
"mean 0.381594 32.204208 \n",
"std 0.806057 49.693429 \n",
"min 0.000000 0.000000 \n",
"25% 0.000000 7.910400 \n",
"50% 0.000000 14.454200 \n",
"75% 0.000000 31.000000 \n",
"max 6.000000 512.329200 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train.describe()"
]
},
{
"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": [
"df_test.describe()"
]
},
{
"cell_type": "code",
"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 0x1e194179a00>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.catplot(x='Sex', y='Survived', data=df_train, kind='violin')"
]
},
{
"cell_type": "code",
"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": [],
"source": [
"# sns.pairplot(data=df_train, hue='Survived')"
]
},
{
"cell_type": "markdown",
"id": "06684f27",
"metadata": {},
"source": [
"## Data Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "095ae1ae",
"metadata": {},
"outputs": [],
"source": [
"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": 18,
"id": "50c17ca4",
"metadata": {},
"outputs": [],
"source": [
"# 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": 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('Other')"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "8a75aa3b",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"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",
" 'A32', 'B4', 'B80', 'A31', 'D36', 'D15', 'C93', 'C78', 'D35',\n",
" 'C87', 'B77', 'E67', 'B94', 'C125', 'C99', 'C118', 'D7', 'A19',\n",
" 'B49', 'D', 'C22 C26', 'C106', 'C65', 'E36', 'C54',\n",
" 'B57 B59 B63 B66', 'C7', 'E34', 'C32', 'B18', 'C124', 'C91', 'E40',\n",
" 'T', 'C128', 'D37', 'B35', 'E50', 'C82', 'B96 B98', 'E10', 'E44',\n",
" 'A34', 'C104', 'C111', 'C92', 'E38', 'D21', 'E12', 'E63', 'A14',\n",
" 'B37', 'C30', 'D20', 'B79', 'E25', 'D46', 'B73', 'C95', 'B38',\n",
" 'B39', 'B22', 'C86', 'C70', 'A16', 'C101', 'C68', 'A10', 'E68',\n",
" 'B41', 'A20', 'D19', 'D50', 'D9', 'A23', 'B50', 'A26', 'D48',\n",
" 'E58', 'C126', 'B71', 'B51 B53 B55', 'D49', 'B5', 'B20', 'F G63',\n",
" 'C62 C64', 'E24', 'C90', 'C45', 'E8', 'B101', 'D45', 'C46', 'D30',\n",
" 'E121', 'D11', 'E77', 'F38', 'B3', 'D6', 'B82 B84', 'D17', 'A36',\n",
" 'B102', 'B69', 'E49', 'C47', 'D28', 'E17', 'A24', 'C50', 'B42',\n",
" 'C148'], dtype=object)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train['Cabin'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "875d207c",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"Other 687\n",
"C23 C25 C27 4\n",
"G6 4\n",
"B96 B98 4\n",
"C22 C26 3\n",
" ... \n",
"E34 1\n",
"C7 1\n",
"C54 1\n",
"E36 1\n",
"C148 1\n",
"Name: Cabin, Length: 148, dtype: int64"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train['Cabin'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "56c071c5",
"metadata": {},
"outputs": [],
"source": [
"# df_train['Cabin'].str.extract('(\\d+)')"
]
},
{
"cell_type": "code",
"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_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['Cabin'][df['Cabin'].isin(counts[counts==n].index)] = description\n"
]
}
],
"source": [
"\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": 30,
"id": "6d73e794",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"Other 687\n",
"Alone 101\n",
"Double room 76\n",
"Three person room 15\n",
"Four person room 12\n",
"Name: Cabin, dtype: int64"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_train['Cabin'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "ae86ac06",
"metadata": {},
"outputs": [],
"source": [
"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()"
]
},
{
"cell_type": "markdown",
"id": "7ed9bfbb",
"metadata": {},
"source": [
"## Views of the best correlated features"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "3c82db70",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"corrmat = df_train.corr() \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=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)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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