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yogeshvar/ai-class.ipynb

Created February 4, 2025 02:25
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ai-class.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import classification_report, confusion_matrix\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import psycopg2\n",
"from psycopg2 import Error"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"connection = psycopg2.connect(\n",
" database=\"db\",\n",
" user=\"user\",\n",
" password=\"password\",\n",
" host=\"url\",\n",
" port=\"5432\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/tr/bl8ctg2d1m5d0wxcn_xr771h0000gn/T/ipykernel_32488/2312264564.py:1: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
" df = pd.read_sql_query('SELECT * FROM ml.titanic', connection)\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>passengerid</th>\n",
" <th>survived</th>\n",
" <th>pclass</th>\n",
" <th>Name</th>\n",
" <th>sex</th>\n",
" <th>age</th>\n",
" <th>sibsp</th>\n",
" <th>parch</th>\n",
" <th>ticket</th>\n",
" <th>fare</th>\n",
" <th>cabin</th>\n",
" <th>embarked</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Braund, Mr. Owen Harris</td>\n",
" <td>male</td>\n",
" <td>22.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>A/5 21171</td>\n",
" <td>7.2500</td>\n",
" <td></td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
" <td>female</td>\n",
" <td>38.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>PC 17599</td>\n",
" <td>71.2833</td>\n",
" <td>C85</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Heikkinen, Miss. Laina</td>\n",
" <td>female</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>STON/O2. 3101282</td>\n",
" <td>7.9250</td>\n",
" <td></td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
" <td>female</td>\n",
" <td>35.0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>113803</td>\n",
" <td>53.1000</td>\n",
" <td>C123</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Allen, Mr. William Henry</td>\n",
" <td>male</td>\n",
" <td>35.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>373450</td>\n",
" <td>8.0500</td>\n",
" <td></td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>886</th>\n",
" <td>887</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>Montvila, Rev. Juozas</td>\n",
" <td>male</td>\n",
" <td>27.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>211536</td>\n",
" <td>13.0000</td>\n",
" <td></td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>887</th>\n",
" <td>888</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Graham, Miss. Margaret Edith</td>\n",
" <td>female</td>\n",
" <td>19.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>112053</td>\n",
" <td>30.0000</td>\n",
" <td>B42</td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>888</th>\n",
" <td>889</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Johnston, Miss. Catherine Helen \"Carrie\"</td>\n",
" <td>female</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>W./C. 6607</td>\n",
" <td>23.4500</td>\n",
" <td></td>\n",
" <td>S</td>\n",
" </tr>\n",
" <tr>\n",
" <th>889</th>\n",
" <td>890</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>Behr, Mr. Karl Howell</td>\n",
" <td>male</td>\n",
" <td>26.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>111369</td>\n",
" <td>30.0000</td>\n",
" <td>C148</td>\n",
" <td>C</td>\n",
" </tr>\n",
" <tr>\n",
" <th>890</th>\n",
" <td>891</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>Dooley, Mr. Patrick</td>\n",
" <td>male</td>\n",
" <td>32.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>370376</td>\n",
" <td>7.7500</td>\n",
" <td></td>\n",
" <td>Q</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>891 rows × 12 columns</p>\n",
"</div>"
],
"text/plain": [
" passengerid survived pclass \\\n",
"0 1 0 3 \n",
"1 2 1 1 \n",
"2 3 1 3 \n",
"3 4 1 1 \n",
"4 5 0 3 \n",
".. ... ... ... \n",
"886 887 0 2 \n",
"887 888 1 1 \n",
"888 889 0 3 \n",
"889 890 1 1 \n",
"890 891 0 3 \n",
"\n",
" Name sex age sibsp \\\n",
"0 Braund, Mr. Owen Harris male 22.0 1 \n",
"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n",
"2 Heikkinen, Miss. Laina female 26.0 0 \n",
"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n",
"4 Allen, Mr. William Henry male 35.0 0 \n",
".. ... ... ... ... \n",
"886 Montvila, Rev. Juozas male 27.0 0 \n",
"887 Graham, Miss. Margaret Edith female 19.0 0 \n",
"888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n",
"889 Behr, Mr. Karl Howell male 26.0 0 \n",
"890 Dooley, Mr. Patrick male 32.0 0 \n",
"\n",
" parch ticket fare cabin embarked \n",
"0 0 A/5 21171 7.2500 S \n",
"1 0 PC 17599 71.2833 C85 C \n",
"2 0 STON/O2. 3101282 7.9250 S \n",
"3 0 113803 53.1000 C123 S \n",
"4 0 373450 8.0500 S \n",
".. ... ... ... ... ... \n",
"886 0 211536 13.0000 S \n",
"887 0 112053 30.0000 B42 S \n",
"888 2 W./C. 6607 23.4500 S \n",
"889 0 111369 30.0000 C148 C \n",
"890 0 370376 7.7500 Q \n",
"\n",
"[891 rows x 12 columns]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_sql_query('SELECT * FROM ml.titanic', connection)\n",
"connection.close()\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of rows and columns: (891, 12)\n",
"After removing rows with missing values: (714, 12)\n"
]
}
],
"source": [
"print('Number of rows and columns:', df.shape)\n",
"df = df.dropna()\n",
"print('After removing rows with missing values:', df.shape)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"passengerid: 714\n",
"survived: 2\n",
"pclass: 3\n",
"Name: 714\n",
"sex: 2\n",
"age: 71\n",
"sibsp: 6\n",
"parch: 7\n",
"ticket: 542\n",
"fare: 220\n",
"cabin: 135\n",
"embarked: 4\n"
]
}
],
"source": [
"for column in df.columns:\n",
" print(f'{column}: {df[column].nunique()}')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"age mean: 29.679271708683473\n",
"fare mean: 34.694514005602244\n"
]
}
],
"source": [
"# find average age, fare\n",
"for column in ['age', 'fare']:\n",
" print(f'{column} mean: {df[column].mean()}')"
]
},
{
"cell_type": "code",
"execution_count": 39,
"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>average_age</th>\n",
" <th>count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sex</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>female</th>\n",
" <td>27.9</td>\n",
" <td>261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>30.7</td>\n",
" <td>453</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" average_age count\n",
"sex \n",
"female 27.9 261\n",
"male 30.7 453"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# average male and female age, and their survival rate\n",
"avg_age_by_gender = df.groupby('sex')['age'].agg([\n",
" ('average_age', 'mean'),\n",
" ('count', 'count')\n",
" ]).round(2)\n",
"avg_age_by_gender"
]
},
{
"cell_type": "code",
"execution_count": 40,
"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>survival_rate</th>\n",
" <th>total_count</th>\n",
" </tr>\n",
" <tr>\n",
" <th>sex</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>female</th>\n",
" <td>0.755</td>\n",
" <td>261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>male</th>\n",
" <td>0.205</td>\n",
" <td>453</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" survival_rate total_count\n",
"sex \n",
"female 0.755 261\n",
"male 0.205 453"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"survival_by_gender = df.groupby('sex')['survived'].agg([\n",
" ('survival_rate', 'mean'),\n",
" ('total_count', 'count')\n",
" ]).round(3)\n",
"survival_by_gender"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(0.0, 1.0)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 1500x600 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create visualizations\n",
"fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))\n",
"\n",
"# Average Age by Gender\n",
"sns.barplot(data=df, x='sex', y='age', ax=ax1)\n",
"ax1.set_title('Average Age by Gender')\n",
"ax1.set_ylabel('Age')\n",
"\n",
"survival_data = df.groupby('sex')['survived'].mean()\n",
"sns.barplot(x=survival_data.index, y=survival_data.values, ax=ax2)\n",
"ax2.set_title('Survival Rate by Gender')\n",
"ax2.set_ylabel('Survival Rate')\n",
"ax2.set_ylim(0, 1)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"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": "3.12.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Raw
titanic.csv
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.25 S
2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Thayer) female 38 1 0 PC 17599 71.2833 C85 C
3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.925 S
4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1 C123 S
5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.05 S
6 0 3 Moran, Mr. James male 0 0 330877 8.4583 Q
7 0 1 McCarthy, Mr. Timothy J male 54 0 0 17463 51.8625 E46 S
8 0 3 Palsson, Master. Gosta Leonard male 2 3 1 349909 21.075 S
9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27 0 2 347742 11.1333 S
10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14 1 0 237736 30.0708 C
11 1 3 Sandstrom, Miss. Marguerite Rut female 4 1 1 PP 9549 16.7 G6 S
12 1 1 Bonnell, Miss. Elizabeth female 58 0 0 113783 26.55 C103 S
13 0 3 Saundercock, Mr. William Henry male 20 0 0 A/5. 2151 8.05 S
14 0 3 Andersson, Mr. Anders Johan male 39 1 5 347082 31.275 S
15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14 0 0 350406 7.8542 S
16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55 0 0 248706 16 S
17 0 3 Rice, Master. Eugene male 2 4 1 382652 29.125 Q
18 1 2 Williams, Mr. Charles Eugene male 0 0 244373 13 S
19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) female 31 1 0 345763 18 S
20 1 3 Masselmani, Mrs. Fatima female 0 0 2649 7.225 C
21 0 2 Fynney, Mr. Joseph J male 35 0 0 239865 26 S
22 1 2 Beesley, Mr. Lawrence male 34 0 0 248698 13 D56 S
23 1 3 McGowan, Miss. Anna "Annie" female 15 0 0 330923 8.0292 Q
24 1 1 Sloper, Mr. William Thompson male 28 0 0 113788 35.5 A6 S
25 0 3 Palsson, Miss. Torborg Danira female 8 3 1 349909 21.075 S
26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) female 38 1 5 347077 31.3875 S
27 0 3 Emir, Mr. Farred Chehab male 0 0 2631 7.225 C
28 0 1 Fortune, Mr. Charles Alexander male 19 3 2 19950 263 C23 C25 C27 S
29 1 3 O'Dwyer, Miss. Ellen "Nellie" female 0 0 330959 7.8792 Q
30 0 3 Todoroff, Mr. Lalio male 0 0 349216 7.8958 S
31 0 1 Uruchurtu, Don. Manuel E male 40 0 0 PC 17601 27.7208 C
32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female 1 0 PC 17569 146.5208 B78 C
33 1 3 Glynn, Miss. Mary Agatha female 0 0 335677 7.75 Q
34 0 2 Wheadon, Mr. Edward H male 66 0 0 C.A. 24579 10.5 S
35 0 1 Meyer, Mr. Edgar Joseph male 28 1 0 PC 17604 82.1708 C
36 0 1 Holverson, Mr. Alexander Oskar male 42 1 0 113789 52 S
37 1 3 Mamee, Mr. Hanna male 0 0 2677 7.2292 C
38 0 3 Cann, Mr. Ernest Charles male 21 0 0 A./5. 2152 8.05 S
39 0 3 Vander Planke, Miss. Augusta Maria female 18 2 0 345764 18 S
40 1 3 Nicola-Yarred, Miss. Jamila female 14 1 0 2651 11.2417 C
41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40 1 0 7546 9.475 S
42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott) female 27 1 0 11668 21 S
43 0 3 Kraeff, Mr. Theodor male 0 0 349253 7.8958 C
44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3 1 2 SC/Paris 2123 41.5792 C
45 1 3 Devaney, Miss. Margaret Delia female 19 0 0 330958 7.8792 Q
46 0 3 Rogers, Mr. William John male 0 0 S.C./A.4. 23567 8.05 S
47 0 3 Lennon, Mr. Denis male 1 0 370371 15.5 Q
48 1 3 O'Driscoll, Miss. Bridget female 0 0 14311 7.75 Q
49 0 3 Samaan, Mr. Youssef male 2 0 2662 21.6792 C
50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18 1 0 349237 17.8 S
51 0 3 Panula, Master. Juha Niilo male 7 4 1 3101295 39.6875 S
52 0 3 Nosworthy, Mr. Richard Cater male 21 0 0 A/4. 39886 7.8 S
53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49 1 0 PC 17572 76.7292 D33 C
54 1 2 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson) female 29 1 0 2926 26 S
55 0 1 Ostby, Mr. Engelhart Cornelius male 65 0 1 113509 61.9792 B30 C
56 1 1 Woolner, Mr. Hugh male 0 0 19947 35.5 C52 S
57 1 2 Rugg, Miss. Emily female 21 0 0 C.A. 31026 10.5 S
58 0 3 Novel, Mr. Mansouer male 28.5 0 0 2697 7.2292 C
59 1 2 West, Miss. Constance Mirium female 5 1 2 C.A. 34651 27.75 S
60 0 3 Goodwin, Master. William Frederick male 11 5 2 CA 2144 46.9 S
61 0 3 Sirayanian, Mr. Orsen male 22 0 0 2669 7.2292 C
62 1 1 Icard, Miss. Amelie female 38 0 0 113572 80 B28
63 0 1 Harris, Mr. Henry Birkhardt male 45 1 0 36973 83.475 C83 S
64 0 3 Skoog, Master. Harald male 4 3 2 347088 27.9 S
65 0 1 Stewart, Mr. Albert A male 0 0 PC 17605 27.7208 C
66 1 3 Moubarek, Master. Gerios male 1 1 2661 15.2458 C
67 1 2 Nye, Mrs. (Elizabeth Ramell) female 29 0 0 C.A. 29395 10.5 F33 S
68 0 3 Crease, Mr. Ernest James male 19 0 0 S.P. 3464 8.1583 S
69 1 3 Andersson, Miss. Erna Alexandra female 17 4 2 3101281 7.925 S
70 0 3 Kink, Mr. Vincenz male 26 2 0 315151 8.6625 S
71 0 2 Jenkin, Mr. Stephen Curnow male 32 0 0 C.A. 33111 10.5 S
72 0 3 Goodwin, Miss. Lillian Amy female 16 5 2 CA 2144 46.9 S
73 0 2 Hood, Mr. Ambrose Jr male 21 0 0 S.O.C. 14879 73.5 S
74 0 3 Chronopoulos, Mr. Apostolos male 26 1 0 2680 14.4542 C
75 1 3 Bing, Mr. Lee male 32 0 0 1601 56.4958 S
76 0 3 Moen, Mr. Sigurd Hansen male 25 0 0 348123 7.65 F G73 S
77 0 3 Staneff, Mr. Ivan male 0 0 349208 7.8958 S
78 0 3 Moutal, Mr. Rahamin Haim male 0 0 374746 8.05 S
79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 29 S
80 1 3 Dowdell, Miss. Elizabeth female 30 0 0 364516 12.475 S
81 0 3 Waelens, Mr. Achille male 22 0 0 345767 9 S
82 1 3 Sheerlinck, Mr. Jan Baptist male 29 0 0 345779 9.5 S
83 1 3 McDermott, Miss. Brigdet Delia female 0 0 330932 7.7875 Q
84 0 1 Carrau, Mr. Francisco M male 28 0 0 113059 47.1 S
85 1 2 Ilett, Miss. Bertha female 17 0 0 SO/C 14885 10.5 S
86 1 3 Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson) female 33 3 0 3101278 15.85 S
87 0 3 Ford, Mr. William Neal male 16 1 3 W./C. 6608 34.375 S
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448 1 1 Seward, Mr. Frederic Kimber male 34 0 0 113794 26.55 S
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671 1 2 Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford) female 40 1 1 29750 39 S
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727 1 2 Renouf, Mrs. Peter Henry (Lillian Jefferys) female 30 3 0 31027 21 S
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729 0 2 Bryhl, Mr. Kurt Arnold Gottfrid male 25 1 0 236853 26 S
730 0 3 Ilmakangas, Miss. Pieta Sofia female 25 1 0 STON/O2. 3101271 7.925 S
731 1 1 Allen, Miss. Elisabeth Walton female 29 0 0 24160 211.3375 B5 S
732 0 3 Hassan, Mr. Houssein G N male 11 0 0 2699 18.7875 C
733 0 2 Knight, Mr. Robert J male 0 0 239855 0 S
734 0 2 Berriman, Mr. William John male 23 0 0 28425 13 S
735 0 2 Troupiansky, Mr. Moses Aaron male 23 0 0 233639 13 S
736 0 3 Williams, Mr. Leslie male 28.5 0 0 54636 16.1 S
737 0 3 Ford, Mrs. Edward (Margaret Ann Watson) female 48 1 3 W./C. 6608 34.375 S
738 1 1 Lesurer, Mr. Gustave J male 35 0 0 PC 17755 512.3292 B101 C
739 0 3 Ivanoff, Mr. Kanio male 0 0 349201 7.8958 S
740 0 3 Nankoff, Mr. Minko male 0 0 349218 7.8958 S
741 1 1 Hawksford, Mr. Walter James male 0 0 16988 30 D45 S
742 0 1 Cavendish, Mr. Tyrell William male 36 1 0 19877 78.85 C46 S
743 1 1 Ryerson, Miss. Susan Parker "Suzette" female 21 2 2 PC 17608 262.375 B57 B59 B63 B66 C
744 0 3 McNamee, Mr. Neal male 24 1 0 376566 16.1 S
745 1 3 Stranden, Mr. Juho male 31 0 0 STON/O 2. 3101288 7.925 S
746 0 1 Crosby, Capt. Edward Gifford male 70 1 1 WE/P 5735 71 B22 S
747 0 3 Abbott, Mr. Rossmore Edward male 16 1 1 C.A. 2673 20.25 S
748 1 2 Sinkkonen, Miss. Anna female 30 0 0 250648 13 S
749 0 1 Marvin, Mr. Daniel Warner male 19 1 0 113773 53.1 D30 S
750 0 3 Connaghton, Mr. Michael male 31 0 0 335097 7.75 Q
751 1 2 Wells, Miss. Joan female 4 1 1 29103 23 S
752 1 3 Moor, Master. Meier male 6 0 1 392096 12.475 E121 S
753 0 3 Vande Velde, Mr. Johannes Joseph male 33 0 0 345780 9.5 S
754 0 3 Jonkoff, Mr. Lalio male 23 0 0 349204 7.8958 S
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756 1 2 Hamalainen, Master. Viljo male 0.67 1 1 250649 14.5 S
757 0 3 Carlsson, Mr. August Sigfrid male 28 0 0 350042 7.7958 S
758 0 2 Bailey, Mr. Percy Andrew male 18 0 0 29108 11.5 S
759 0 3 Theobald, Mr. Thomas Leonard male 34 0 0 363294 8.05 S
760 1 1 Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards) female 33 0 0 110152 86.5 B77 S
761 0 3 Garfirth, Mr. John male 0 0 358585 14.5 S
762 0 3 Nirva, Mr. Iisakki Antino Aijo male 41 0 0 SOTON/O2 3101272 7.125 S
763 1 3 Barah, Mr. Hanna Assi male 20 0 0 2663 7.2292 C
764 1 1 Carter, Mrs. William Ernest (Lucile Polk) female 36 1 2 113760 120 B96 B98 S
765 0 3 Eklund, Mr. Hans Linus male 16 0 0 347074 7.775 S
766 1 1 Hogeboom, Mrs. John C (Anna Andrews) female 51 1 0 13502 77.9583 D11 S
767 0 1 Brewe, Dr. Arthur Jackson male 0 0 112379 39.6 C
768 0 3 Mangan, Miss. Mary female 30.5 0 0 364850 7.75 Q
769 0 3 Moran, Mr. Daniel J male 1 0 371110 24.15 Q
770 0 3 Gronnestad, Mr. Daniel Danielsen male 32 0 0 8471 8.3625 S
771 0 3 Lievens, Mr. Rene Aime male 24 0 0 345781 9.5 S
772 0 3 Jensen, Mr. Niels Peder male 48 0 0 350047 7.8542 S
773 0 2 Mack, Mrs. (Mary) female 57 0 0 S.O./P.P. 3 10.5 E77 S
774 0 3 Elias, Mr. Dibo male 0 0 2674 7.225 C
775 1 2 Hocking, Mrs. Elizabeth (Eliza Needs) female 54 1 3 29105 23 S
776 0 3 Myhrman, Mr. Pehr Fabian Oliver Malkolm male 18 0 0 347078 7.75 S
777 0 3 Tobin, Mr. Roger male 0 0 383121 7.75 F38 Q
778 1 3 Emanuel, Miss. Virginia Ethel female 5 0 0 364516 12.475 S
779 0 3 Kilgannon, Mr. Thomas J male 0 0 36865 7.7375 Q
780 1 1 Robert, Mrs. Edward Scott (Elisabeth Walton McMillan) female 43 0 1 24160 211.3375 B3 S
781 1 3 Ayoub, Miss. Banoura female 13 0 0 2687 7.2292 C
782 1 1 Dick, Mrs. Albert Adrian (Vera Gillespie) female 17 1 0 17474 57 B20 S
783 0 1 Long, Mr. Milton Clyde male 29 0 0 113501 30 D6 S
784 0 3 Johnston, Mr. Andrew G male 1 2 W./C. 6607 23.45 S
785 0 3 Ali, Mr. William male 25 0 0 SOTON/O.Q. 3101312 7.05 S
786 0 3 Harmer, Mr. Abraham (David Lishin) male 25 0 0 374887 7.25 S
787 1 3 Sjoblom, Miss. Anna Sofia female 18 0 0 3101265 7.4958 S
788 0 3 Rice, Master. George Hugh male 8 4 1 382652 29.125 Q
789 1 3 Dean, Master. Bertram Vere male 1 1 2 C.A. 2315 20.575 S
790 0 1 Guggenheim, Mr. Benjamin male 46 0 0 PC 17593 79.2 B82 B84 C
791 0 3 Keane, Mr. Andrew "Andy" male 0 0 12460 7.75 Q
792 0 2 Gaskell, Mr. Alfred male 16 0 0 239865 26 S
793 0 3 Sage, Miss. Stella Anna female 8 2 CA. 2343 69.55 S
794 0 1 Hoyt, Mr. William Fisher male 0 0 PC 17600 30.6958 C
795 0 3 Dantcheff, Mr. Ristiu male 25 0 0 349203 7.8958 S
796 0 2 Otter, Mr. Richard male 39 0 0 28213 13 S
797 1 1 Leader, Dr. Alice (Farnham) female 49 0 0 17465 25.9292 D17 S
798 1 3 Osman, Mrs. Mara female 31 0 0 349244 8.6833 S
799 0 3 Ibrahim Shawah, Mr. Yousseff male 30 0 0 2685 7.2292 C
800 0 3 Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert) female 30 1 1 345773 24.15 S
801 0 2 Ponesell, Mr. Martin male 34 0 0 250647 13 S
802 1 2 Collyer, Mrs. Harvey (Charlotte Annie Tate) female 31 1 1 C.A. 31921 26.25 S
803 1 1 Carter, Master. William Thornton II male 11 1 2 113760 120 B96 B98 S
804 1 3 Thomas, Master. Assad Alexander male 0.42 0 1 2625 8.5167 C
805 1 3 Hedman, Mr. Oskar Arvid male 27 0 0 347089 6.975 S
806 0 3 Johansson, Mr. Karl Johan male 31 0 0 347063 7.775 S
807 0 1 Andrews, Mr. Thomas Jr male 39 0 0 112050 0 A36 S
808 0 3 Pettersson, Miss. Ellen Natalia female 18 0 0 347087 7.775 S
809 0 2 Meyer, Mr. August male 39 0 0 248723 13 S
810 1 1 Chambers, Mrs. Norman Campbell (Bertha Griggs) female 33 1 0 113806 53.1 E8 S
811 0 3 Alexander, Mr. William male 26 0 0 3474 7.8875 S
812 0 3 Lester, Mr. James male 39 0 0 A/4 48871 24.15 S
813 0 2 Slemen, Mr. Richard James male 35 0 0 28206 10.5 S
814 0 3 Andersson, Miss. Ebba Iris Alfrida female 6 4 2 347082 31.275 S
815 0 3 Tomlin, Mr. Ernest Portage male 30.5 0 0 364499 8.05 S
816 0 1 Fry, Mr. Richard male 0 0 112058 0 B102 S
817 0 3 Heininen, Miss. Wendla Maria female 23 0 0 STON/O2. 3101290 7.925 S
818 0 2 Mallet, Mr. Albert male 31 1 1 S.C./PARIS 2079 37.0042 C
819 0 3 Holm, Mr. John Fredrik Alexander male 43 0 0 C 7075 6.45 S
820 0 3 Skoog, Master. Karl Thorsten male 10 3 2 347088 27.9 S
821 1 1 Hays, Mrs. Charles Melville (Clara Jennings Gregg) female 52 1 1 12749 93.5 B69 S
822 1 3 Lulic, Mr. Nikola male 27 0 0 315098 8.6625 S
823 0 1 Reuchlin, Jonkheer. John George male 38 0 0 19972 0 S
824 1 3 Moor, Mrs. (Beila) female 27 0 1 392096 12.475 E121 S
825 0 3 Panula, Master. Urho Abraham male 2 4 1 3101295 39.6875 S
826 0 3 Flynn, Mr. John male 0 0 368323 6.95 Q
827 0 3 Lam, Mr. Len male 0 0 1601 56.4958 S
828 1 2 Mallet, Master. Andre male 1 0 2 S.C./PARIS 2079 37.0042 C
829 1 3 McCormack, Mr. Thomas Joseph male 0 0 367228 7.75 Q
830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) female 62 0 0 113572 80 B28
831 1 3 Yasbeck, Mrs. Antoni (Selini Alexander) female 15 1 0 2659 14.4542 C
832 1 2 Richards, Master. George Sibley male 0.83 1 1 29106 18.75 S
833 0 3 Saad, Mr. Amin male 0 0 2671 7.2292 C
834 0 3 Augustsson, Mr. Albert male 23 0 0 347468 7.8542 S
835 0 3 Allum, Mr. Owen George male 18 0 0 2223 8.3 S
836 1 1 Compton, Miss. Sara Rebecca female 39 1 1 PC 17756 83.1583 E49 C
837 0 3 Pasic, Mr. Jakob male 21 0 0 315097 8.6625 S
838 0 3 Sirota, Mr. Maurice male 0 0 392092 8.05 S
839 1 3 Chip, Mr. Chang male 32 0 0 1601 56.4958 S
840 1 1 Marechal, Mr. Pierre male 0 0 11774 29.7 C47 C
841 0 3 Alhomaki, Mr. Ilmari Rudolf male 20 0 0 SOTON/O2 3101287 7.925 S
842 0 2 Mudd, Mr. Thomas Charles male 16 0 0 S.O./P.P. 3 10.5 S
843 1 1 Serepeca, Miss. Augusta female 30 0 0 113798 31 C
844 0 3 Lemberopolous, Mr. Peter L male 34.5 0 0 2683 6.4375 C
845 0 3 Culumovic, Mr. Jeso male 17 0 0 315090 8.6625 S
846 0 3 Abbing, Mr. Anthony male 42 0 0 C.A. 5547 7.55 S
847 0 3 Sage, Mr. Douglas Bullen male 8 2 CA. 2343 69.55 S
848 0 3 Markoff, Mr. Marin male 35 0 0 349213 7.8958 C
849 0 2 Harper, Rev. John male 28 0 1 248727 33 S
850 1 1 Goldenberg, Mrs. Samuel L (Edwiga Grabowska) female 1 0 17453 89.1042 C92 C
851 0 3 Andersson, Master. Sigvard Harald Elias male 4 4 2 347082 31.275 S
852 0 3 Svensson, Mr. Johan male 74 0 0 347060 7.775 S
853 0 3 Boulos, Miss. Nourelain female 9 1 1 2678 15.2458 C
854 1 1 Lines, Miss. Mary Conover female 16 0 1 PC 17592 39.4 D28 S
855 0 2 Carter, Mrs. Ernest Courtenay (Lilian Hughes) female 44 1 0 244252 26 S
856 1 3 Aks, Mrs. Sam (Leah Rosen) female 18 0 1 392091 9.35 S
857 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 45 1 1 36928 164.8667 S
858 1 1 Daly, Mr. Peter Denis male 51 0 0 113055 26.55 E17 S
859 1 3 Baclini, Mrs. Solomon (Latifa Qurban) female 24 0 3 2666 19.2583 C
860 0 3 Razi, Mr. Raihed male 0 0 2629 7.2292 C
861 0 3 Hansen, Mr. Claus Peter male 41 2 0 350026 14.1083 S
862 0 2 Giles, Mr. Frederick Edward male 21 1 0 28134 11.5 S
863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Barron) female 48 0 0 17466 25.9292 D17 S
864 0 3 Sage, Miss. Dorothy Edith "Dolly" female 8 2 CA. 2343 69.55 S
865 0 2 Gill, Mr. John William male 24 0 0 233866 13 S
866 1 2 Bystrom, Mrs. (Karolina) female 42 0 0 236852 13 S
867 1 2 Duran y More, Miss. Asuncion female 27 1 0 SC/PARIS 2149 13.8583 C
868 0 1 Roebling, Mr. Washington Augustus II male 31 0 0 PC 17590 50.4958 A24 S
869 0 3 van Melkebeke, Mr. Philemon male 0 0 345777 9.5 S
870 1 3 Johnson, Master. Harold Theodor male 4 1 1 347742 11.1333 S
871 0 3 Balkic, Mr. Cerin male 26 0 0 349248 7.8958 S
872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47 1 1 11751 52.5542 D35 S
873 0 1 Carlsson, Mr. Frans Olof male 33 0 0 695 5 B51 B53 B55 S
874 0 3 Vander Cruyssen, Mr. Victor male 47 0 0 345765 9 S
875 1 2 Abelson, Mrs. Samuel (Hannah Wizosky) female 28 1 0 P/PP 3381 24 C
876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15 0 0 2667 7.225 C
877 0 3 Gustafsson, Mr. Alfred Ossian male 20 0 0 7534 9.8458 S
878 0 3 Petroff, Mr. Nedelio male 19 0 0 349212 7.8958 S
879 0 3 Laleff, Mr. Kristo male 0 0 349217 7.8958 S
880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56 0 1 11767 83.1583 C50 C
881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25 0 1 230433 26 S
882 0 3 Markun, Mr. Johann male 33 0 0 349257 7.8958 S
883 0 3 Dahlberg, Miss. Gerda Ulrika female 22 0 0 7552 10.5167 S
884 0 2 Banfield, Mr. Frederick James male 28 0 0 C.A./SOTON 34068 10.5 S
885 0 3 Sutehall, Mr. Henry Jr male 25 0 0 SOTON/OQ 392076 7.05 S
886 0 3 Rice, Mrs. William (Margaret Norton) female 39 0 5 382652 29.125 Q
887 0 2 Montvila, Rev. Juozas male 27 0 0 211536 13 S
888 1 1 Graham, Miss. Margaret Edith female 19 0 0 112053 30 B42 S
889 0 3 Johnston, Miss. Catherine Helen "Carrie" female 1 2 W./C. 6607 23.45 S
890 1 1 Behr, Mr. Karl Howell male 26 0 0 111369 30 C148 C
891 0 3 Dooley, Mr. Patrick male 32 0 0 370376 7.75 Q
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