├── README.md └── Titanic Kaggle Problem └── titanic ├── Code.py ├── Titanic Project (Machine Learning).docx ├── test.csv ├── train.csv └── video_thumbnail.jpg /README.md: -------------------------------------------------------------------------------- 1 | # Titanic 2 | This is the legendary Titanic ML competition – the best, first challenge for you to dive into ML competitions and familiarize yourself with how the Kaggle platform works. 3 | 4 | The competition is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. 5 | 6 | [![Watch the video](https://github.com/codebugged/Titanic/blob/master/Titanic%20Kaggle%20Problem/titanic/video_thumbnail.jpg)](https://www.youtube.com/watch?v=8yZMXCaFshs&feature=youtu.be) 7 | 8 | ## The Challenge 9 | The sinking of the Titanic is one of the most infamous shipwrecks in history. 10 | 11 | On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone onboard, resulting in the death of 1502 out of 2224 passengers and crew. 12 | 13 | While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. 14 | 15 | In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?” using passenger data (ie name, age, gender, socio-economic class, etc). 16 | -------------------------------------------------------------------------------- /Titanic Kaggle Problem/titanic/Code.py: -------------------------------------------------------------------------------- 1 | ''' 2 | Machine Learning Project Description : 3 | Titanic Survival Prediction :-The sinking of the RMS Titanic is one of 4 | the most infamous shipwrecks in history. 5 | On April 15, 1912, during her maiden voyage, the Titanic sank after 6 | colliding with an iceberg, killing 1502 out of 2224 passengers and crew. 7 | 8 | This sensational tragedy shocked the international 9 | community and led to better safety regulations for ships. 10 | 11 | One of the reasons that the shipwreck led to such loss of life 12 | was that there were not enough lifeboats for the passengers and crew. 13 | Although there was some element of luck involved in surviving the sinking, 14 | some groups of people were more likely to survive than others, 15 | such as women, children, and the upper-class. 16 | 17 | In this challenge, we ask you to complete the analysis of what sorts of 18 | people were likely to survive. 19 | In particular, we ask you to apply the tools of machine learning to predict 20 | which passengers survived the tragedy. 21 | 22 | 23 | 24 | Overview 25 | The data has been split into two groups: 26 | 27 | 1) training set (train.csv) 891 Rows 28 | 2) test set (test.csv) 418 Rows 29 | 30 | The training set should be used to build your machine learning models. 31 | For the training set, we provide the outcome 32 | (also known as the "ground truth")for each passenger. 33 | 34 | Your model will be based on "features" like passengers' gender 35 | and class. 36 | You can also use feature engineering to create new features. 37 | 38 | The test set should be used to see how well your model performs on unseen data. 39 | For the test set, we do not provide the ground truth for each passenger. 40 | It is your job to predict these outcomes. 41 | For each passenger in the test set, use the model you trained to 42 | predict whether or not they 43 | survived the sinking of the Titanic. 44 | 45 | 46 | Description about data:- 47 | #---------------------------- 48 | Below is a brief information about each columns of the dataset: 49 | 50 | 1)PassengerId: An unique index for passenger rows. It starts from 1 for first row and increments by 1 for every new rows. 51 | 52 | 2)Survived: Shows if the passenger survived or not. 1 stands for survived and 0 stands for not survived. 53 | 54 | 3)Pclass: Ticket class. 1 stands for First class ticket. 2 stands for Second class ticket. 3 stands for Third class ticket. 55 | 56 | 4)Name: Passenger's name. Name also contain title. "Mr" for man. "Mrs" for woman. "Miss" for girl. "Master" for boy. 57 | 58 | 5)Sex: Passenger's sex. It's either Male or Female. 59 | 60 | 6)Age: Passenger's age. "NaN" values in this column indicates that the age of that particular passenger has not been recorded. 61 | 62 | 7)SibSp: Number of siblings or spouses travelling with each passenger. 63 | 64 | 8)Parch: Number of parents of children travelling with each passenger. 65 | 66 | 9)Ticket: Ticket number. 67 | 68 | 10)Fare: How much money the passenger has paid for the travel journey. 69 | 70 | 11)Cabin: Cabin number of the passenger. "NaN" values in this column indicates that the cabin number of that particular passenger has not been recorded. 71 | 72 | 12)Embarked: Port from where the particular passenger was embarked/boarded. 73 | 74 | 75 | Contents: 76 | 1)Import Necessary Libraries 77 | 2)Read In and Explore the Historic Data 78 | 3)Data Analysis 79 | 4)Data Visualization 80 | 5)Cleaning Data 81 | 6)Choosing the Best Model 82 | 7)Creating Submission File 83 | 84 | 85 | 86 | ''' 87 | 88 | #1) Import Necessary Libraries 89 | #First off, we need to import several Python libraries such as numpy, pandas, 90 | # matplotlib and seaborn. 91 | 92 | #data analysis libraries 93 | import numpy as np 94 | import pandas as pd 95 | pd.set_option('display.width', 1000) 96 | pd.set_option('display.max_column', 16) 97 | pd.set_option('precision', 2) 98 | 99 | #visualization libraries 100 | import matplotlib.pyplot as plt 101 | import seaborn as sbn 102 | 103 | #ignore warnings 104 | import warnings 105 | warnings.filterwarnings('ignore') 106 | 107 | #STEP-2) Read in and Explore the Data 108 | #********************************************* 109 | #It's time to read in our training and testing data using pd.read_csv, and take a first look at the training data using the describe() function. 110 | 111 | #import train and test CSV files 112 | train = pd.read_csv('train.csv') #12 columns 113 | test = pd.read_csv('test.csv') #11 columns 114 | 115 | #take a look at the training data 116 | 117 | print( train.describe()[:] ) 118 | 119 | print( "\n" ) 120 | 121 | print( train.describe(include="all") ) 122 | 123 | 124 | 125 | print( "\n" ) 126 | 127 | 128 | 129 | 130 | 131 | #OUTPUT 132 | # PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 133 | # count 891.00 891.00 891.00 891 891 714.00 891.00 891.00 891 891.00 204 889 134 | # unique NaN NaN NaN 891 2 NaN NaN NaN 681 NaN 147 3 135 | # top NaN NaN NaN Graham, Mr. George Edward male NaN NaN NaN CA. 2343 NaN C23 C25 C27 S 136 | # freq NaN NaN NaN 1 577 NaN NaN NaN 7 NaN 4 644 137 | # mean 446.00 0.38 2.31 NaN NaN 29.70 0.52 0.38 NaN 32.20 NaN NaN 138 | # std 257.35 0.49 0.84 NaN NaN 14.53 1.10 0.81 NaN 49.69 NaN NaN 139 | # min 1.00 0.00 1.00 NaN NaN 0.42 0.00 0.00 NaN 0.00 NaN NaN 140 | # 25% 223.50 0.00 2.00 NaN NaN 20.12 0.00 0.00 NaN 7.91 NaN NaN 141 | # 50% 446.00 0.00 3.00 NaN NaN 28.00 0.00 0.00 NaN 14.45 NaN NaN 142 | # 75% 668.50 1.00 3.00 NaN NaN 38.00 1.00 0.00 NaN 31.00 NaN NaN 143 | # max 891.00 1.00 3.00 NaN NaN 80.00 8.00 6.00 NaN 512.33 NaN NaN 144 | 145 | 146 | 147 | #STEP-3) Data Analysis 148 | #************************************************** 149 | #We're going to consider the features in the dataset and how complete they are. 150 | 151 | #get a list of the features within the dataset 152 | print( "\n\n" , train.columns ) 153 | 154 | #OUTPUT 155 | #Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 156 | # 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], 157 | # dtype='object') 158 | 159 | 160 | 161 | #see a sample of the dataset to get an idea of the variables 162 | print 163 | print( train.head() ) 164 | 165 | print() 166 | print( train.sample(5) ) 167 | 168 | 169 | 170 | # PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 171 | #624 625 0 3 Bowen, Mr. David John "Dai" male 21.0 0 0 54636 16.10 NaN S 172 | #612 613 1 3 Murphy, Miss. Margaret Jane female NaN 1 0 367230 15.50 NaN Q 173 | #392 393 0 3 Gustafsson, Mr. Johan Birger male 28.0 2 0 3101277 7.92 NaN S 174 | #703 704 0 3 Gallagher, Mr. Martin male 25.0 0 0 36864 7.74 NaN Q 175 | #789 790 0 1 Guggenheim, Mr. Benjamin male 46.0 0 0 PC 17593 79.20 B82 B84 C 176 | 177 | #Observations from above output 178 | #----------------------------- 179 | #Numerical Features: Age (Continuous), Fare (Continuous), SibSp (Discrete), Parch (Discrete) 180 | #Categorical Features: Survived, Sex, Embarked, Pclass 181 | #Alphanumeric Features: Name, Ticket, Cabin 182 | 183 | 184 | 185 | print( "Data types for each feature : -" ) 186 | print( train.dtypes ) 187 | 188 | #OUTPUT:- 189 | #PassengerId int64 190 | #Survived int64 191 | #Pclass int64 192 | #Name object 193 | #Sex object 194 | #Age float64 195 | #SibSp int64 196 | #Parch int64 197 | #Ticket object 198 | #Fare float64 199 | #Cabin object 200 | #Embarked object 201 | 202 | 203 | #Now that we have an idea of what kinds of features we're working with, 204 | # we can see how much information we have about each of them. 205 | 206 | #see a summary of the training dataset 207 | print( train.describe(include = "all") ) 208 | 209 | 210 | #OUTPUT 211 | # PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked 212 | # count 891.00 891.00 891.00 891 891 714.00 891.00 891.00 891 891.00 204 889 213 | # unique NaN NaN NaN 891 2 NaN NaN NaN 681 NaN 147 3 214 | # top NaN NaN NaN Graham, Mr. George Edward male NaN NaN NaN CA. 2343 NaN C23 C25 C27 S 215 | # freq NaN NaN NaN 1 577 NaN NaN NaN 7 NaN 4 644 216 | # mean 446.00 0.38 2.31 NaN NaN 29.70 0.52 0.38 NaN 32.20 NaN NaN 217 | # std 257.35 0.49 0.84 NaN NaN 14.53 1.10 0.81 NaN 49.69 NaN NaN 218 | # min 1.00 0.00 1.00 NaN NaN 0.42 0.00 0.00 NaN 0.00 NaN NaN 219 | # 25% 223.50 0.00 2.00 NaN NaN 20.12 0.00 0.00 NaN 7.91 NaN NaN 220 | # 50% 446.00 0.00 3.00 NaN NaN 28.00 0.00 0.00 NaN 14.45 NaN NaN 221 | # 75% 668.50 1.00 3.00 NaN NaN 38.00 1.00 0.00 NaN 31.00 NaN NaN 222 | # max 891.00 1.00 3.00 NaN NaN 80.00 8.00 6.00 NaN 512.33 NaN NaN 223 | 224 | 225 | #Some Observations from above output 226 | #------------------------------------ 227 | #1)There are a total of 891 passengers in our training set. 228 | 229 | #2)The Age feature is missing approximately 19.8% of its values. 230 | # Hence Age feature is pretty important to survival, 231 | # so we should probably attempt to fill these gaps. 232 | 233 | #3)The Cabin feature is missing approximately 77.1% of its values. 234 | # Since so much of the feature is missing, it would be hard to fill in the missing values. 235 | # We'll probably drop these values from our dataset. 236 | 237 | #4)The Embarked feature is missing only 2 passeners, 238 | # which should be relatively harmless. 239 | 240 | #check for any other unusable values 241 | 242 | print() 243 | print( pd.isnull(train).sum() ) 244 | 245 | 246 | #OUTPUT 247 | #PassengerId 0 248 | #Survived 0 249 | #Pclass 0 250 | #Name 0 251 | #Sex 0 252 | #Age 177 253 | #SibSp 0 254 | #Parch 0 255 | #Ticket 0 256 | #Fare 0 257 | #Cabin 687 258 | #Embarked 2 259 | 260 | #We can see that except for the abovementioned missing values, no NaN values exist. 261 | 262 | 263 | 264 | #Relationship between Features and Survival 265 | #In this section, we analyze relationship between different features 266 | # with respect to Survival. We see how different feature values 267 | # show different survival chance. We also plot different kinds of 268 | # diagrams to visualize our data and findings. 269 | 270 | 271 | #4) Data Visualization 272 | #************************************* 273 | #It's time to visualize our data so we can estimate few predictions 274 | 275 | #----------------- 276 | #4.A) Sex Feature 277 | #----------------- 278 | #draw a bar plot of survival by sex 279 | sbn.barplot(x="Sex", y="Survived", data=train) 280 | plt.show() 281 | 282 | 283 | 284 | 285 | #print(" percentages of females vs. males that survive") 286 | #print( "Percentage of females who survived:", train["Survived"][train["Sex"] == 'female'].value_counts(normalize = True)[1]*100 ) 287 | 288 | 289 | print( "------------------\n\n" ) 290 | print( train ) 291 | 292 | 293 | 294 | print( "------------------\n\n" ) 295 | print( train["Survived"] ) 296 | 297 | print( "------------------\n\n" ) 298 | print( train["Sex"] == 'female' ) 299 | 300 | 301 | 302 | 303 | print( "**********\n\n" ) 304 | print( train["Survived"][ train["Sex"] == 'female' ] ) 305 | 306 | 307 | 308 | 309 | print( "*****************\n\n" ) 310 | print(train["Survived"][train["Sex"] == 'female'].value_counts() ) 311 | 312 | 313 | 314 | 315 | 316 | print( "====================================\n\n" ) 317 | print( train["Survived"][train["Sex"] == 'female'].value_counts(normalize = True) ) 318 | 319 | 320 | 321 | 322 | 323 | 324 | print( train["Survived"][train["Sex"] == 'female'].value_counts(normalize = True)[1] ) 325 | 326 | 327 | 328 | 329 | print( "Percentage of females who survived:", train["Survived"][train["Sex"] == 'female'].value_counts(normalize = True)[1]*100 ) 330 | print( "Percentage of males who survived:", train["Survived"][train["Sex"] == 'male'].value_counts(normalize = True)[1]*100 ) 331 | 332 | 333 | 334 | #Percentage of females who survived: 74.2038216561 335 | #Percentage of males who survived: 18.8908145581 336 | 337 | 338 | #Some Observations from above output 339 | #------------------------------------ 340 | # As predicted, females have a much higher chance of survival than males. 341 | # The Sex feature is essential in our predictions. 342 | 343 | 344 | 345 | 346 | 347 | 348 | #-------------------- 349 | #4.B) Pclass Feature 350 | #-------------------- 351 | #draw a bar plot of survival by Pclass 352 | sbn.barplot(x="Pclass", y="Survived", data=train) 353 | plt.show() 354 | 355 | 356 | #print( percentage of people by Pclass that survived 357 | print("Percentage of Pclass = 1 who survived:", train["Survived"][train["Pclass"] == 1].value_counts(normalize = True)[1]*100) 358 | 359 | print("Percentage of Pclass = 2 who survived:", train["Survived"][train["Pclass"] == 2].value_counts(normalize = True)[1]*100) 360 | 361 | print("Percentage of Pclass = 3 who survived:", train["Survived"][train["Pclass"] == 3].value_counts(normalize = True)[1]*100) 362 | #Percentage of Pclass = 1 who survived: 62.962962963 363 | #Percentage of Pclass = 2 who survived: 47.2826086957 364 | #Percentage of Pclass = 3 who survived: 24.2362525458 365 | 366 | print() 367 | print( "Percentage of Pclass = 1 who survived:\n\n", train["Survived"][train["Pclass"] == 1].value_counts() ) 368 | 369 | print() 370 | print( "Percentage of Pclass = 1 who survived:\n\n", train["Survived"][train["Pclass"] == 1].value_counts(normalize = True) ) 371 | 372 | print() 373 | print( "Percentage of Pclass = 1 who survived:\n\n", train["Survived"][train["Pclass"] == 1].value_counts(normalize = True)[1] ) 374 | 375 | 376 | 377 | 378 | 379 | #Some Observations from above output 380 | #------------------------------------ 381 | #As predicted, people with higher socioeconomic class had a higher rate of survival. (62.9% vs. 47.3% vs. 24.2%) 382 | 383 | 384 | 385 | #---------------------- 386 | #4.C) SibSp Feature 387 | #---------------------- 388 | #draw a bar plot for SibSp vs. survival 389 | sbn.barplot(x="SibSp", y="Survived", data=train) 390 | 391 | #I won't be printing individual percent values for all of these. 392 | print("Percentage of SibSp = 0 who survived:", 393 | train["Survived"][train["SibSp"] == 0].value_counts(normalize = True)[1]*100) 394 | 395 | print("Percentage of SibSp = 1 who survived:", 396 | train["Survived"][train["SibSp"] == 1].value_counts(normalize = True)[1]*100) 397 | 398 | print("Percentage of SibSp = 2 who survived:", 399 | train["Survived"][train["SibSp"] == 2].value_counts(normalize = True)[1]*100) 400 | #OUTPUT:- 401 | #Percentage of SibSp = 0 who survived: 34.5394736842 402 | #Percentage of SibSp = 1 who survived: 53.5885167464 403 | #Percentage of SibSp = 2 who survived: 46.4285714286 404 | 405 | plt.show() 406 | 407 | 408 | 409 | 410 | 411 | 412 | 413 | #Some Observations from above output 414 | #------------------------------------ 415 | #In general, its clear that people with more siblings or 416 | # spouses aboard were less likely to survive. 417 | # However, contrary to expectations, people with no siblings 418 | # or spouses were less to likely to survive than those with one or two. (34.5% vs 53.4% vs. 46.4%) 419 | 420 | 421 | 422 | 423 | 424 | #-------------------- 425 | #4.D)Parch Feature 426 | #-------------------- 427 | 428 | #draw a bar plot for Parch vs. survival 429 | sbn.barplot(x="Parch", y="Survived", data=train) 430 | plt.show() 431 | 432 | 433 | 434 | 435 | #Some Observations from above output 436 | #------------------------------------ 437 | #People with less than four parents or children aboard are more likely to survive than those with four or more. 438 | # Again, people traveling alone are less likely to survive than those with 1-3 parents or children. 439 | 440 | 441 | 442 | #----------------- 443 | #4.E)Age Feature 444 | #----------------- 445 | 446 | 447 | #sort the ages into logical categories 448 | train["Age"] = train["Age"].fillna(-0.5) 449 | test["Age"] = test["Age"].fillna(-0.5) 450 | 451 | bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf] 452 | labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior'] 453 | train['AgeGroup'] = pd.cut(train["Age"], bins, labels = labels) 454 | test['AgeGroup'] = pd.cut(test["Age"], bins, labels = labels) 455 | print( train ) 456 | #draw a bar plot of Age vs. survival 457 | sbn.barplot(x="AgeGroup", y="Survived", data=train) 458 | plt.show() 459 | 460 | #Done******************************************************** 461 | 462 | 463 | 464 | 465 | 466 | 467 | #Some Observations from above output 468 | #------------------------------------ 469 | #Babies are more likely to survive than any other age group. 470 | 471 | 472 | 473 | 474 | #-------------------- 475 | #4.F) Cabin Feature 476 | #-------------------- 477 | 478 | #I think the idea here is that people with recorded cabin numbers are of higher socioeconomic class, 479 | # and thus more likely to survive. 480 | 481 | train["CabinBool"] = (train["Cabin"].notnull().astype('int')) 482 | test["CabinBool"] = (test["Cabin"].notnull().astype('int')) 483 | 484 | print( "###################################\n\n" ) 485 | print( train ) 486 | 487 | 488 | 489 | #calculate percentages of CabinBool vs. survived 490 | print("Percentage of CabinBool = 1 who survived:", 491 | train["Survived"][train["CabinBool"] == 1].value_counts( 492 | normalize = True)[1]*100) 493 | 494 | print("Percentage of CabinBool = 0 who survived:", 495 | train["Survived"][train["CabinBool"] == 0].value_counts( 496 | normalize = True)[1]*100) 497 | 498 | #draw a bar plot of CabinBool vs. survival 499 | sbn.barplot(x="CabinBool", y="Survived", data=train) 500 | plt.show() 501 | 502 | 503 | #OUTPUT :- 504 | #Percentage of CabinBool = 1 who survived: 66.6666666667 505 | #Percentage of CabinBool = 0 who survived: 29.9854439592 506 | 507 | #Some Observations from above output 508 | #------------------------------------ 509 | #People with a recorded Cabin number are, in fact, 510 | #more likely to survive. (66.6% vs 29.9%) 511 | 512 | 513 | 514 | 515 | 516 | #5) Cleaning Data 517 | #********************************* 518 | 519 | #Time to clean our data to account for missing values and unnecessary information! 520 | 521 | #Looking at the Test Data 522 | #Let's see how our test data looks! 523 | 524 | print( test.describe(include="all") ) 525 | #OUTPUT:- 526 | # PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked AgeGroup CabinBool 527 | #count 418.00 418.00 418 418 418.00 418.00 418.00 418 417.00 91 418 418 418.00 528 | #unique NaN NaN 418 2 NaN NaN NaN 363 NaN 76 3 8 NaN 529 | #top NaN NaN Rosenbaum, Miss. Edith Louise male NaN NaN NaN PC 17608 NaN B57 B59 B63 B66 S Young Adult NaN 530 | #freq NaN NaN 1 266 NaN NaN NaN 5 NaN 3 270 96 NaN 531 | #mean 1100.50 2.27 NaN NaN 23.94 0.45 0.39 NaN 35.63 NaN NaN NaN 0.22 532 | #std 120.81 0.84 NaN NaN 17.74 0.90 0.98 NaN 55.91 NaN NaN NaN 0.41 533 | #min 892.00 1.00 NaN NaN -0.50 0.00 0.00 NaN 0.00 NaN NaN NaN 0.00 534 | #25% 996.25 1.00 NaN NaN 9.00 0.00 0.00 NaN 7.90 NaN NaN NaN 0.00 535 | #50% 1100.50 3.00 NaN NaN 24.00 0.00 0.00 NaN 14.45 NaN NaN NaN 0.00 536 | #75% 1204.75 3.00 NaN NaN 35.75 1.00 0.00 NaN 31.50 NaN NaN NaN 0.00 537 | #max 1309.00 3.00 NaN NaN 76.00 8.00 9.00 NaN 512.33 NaN NaN NaN 1.00 538 | 539 | 540 | #Some Observations from above output for test.csv data 541 | #---------------------------------------------------- 542 | #1) We have a total of 418 passengers. 543 | #2) 1 value from the Fare feature is missing. 544 | #3) Around 20.5% of the Age feature is missing in training file 545 | # we will need to fill that in. 546 | 547 | 548 | #Cabin Feature 549 | #we'll start off by dropping the Cabin feature since not a lot more useful information can be extracted from it. 550 | train = train.drop(['Cabin'], axis = 1) 551 | test = test.drop(['Cabin'], axis = 1) 552 | 553 | #Ticket Feature 554 | #we can also drop the Ticket feature since it's unlikely to yield any useful information 555 | train = train.drop(['Ticket'], axis = 1) 556 | test = test.drop(['Ticket'], axis = 1) 557 | 558 | 559 | 560 | #Embarked Feature 561 | #now we need to fill in the missing values in the Embarked feature 562 | print( "Number of people embarking in Southampton (S):" , ) 563 | 564 | 565 | print( "\n\nSHAPE = " , train[train["Embarked"] == "S"].shape ) 566 | print( "SHAPE[0] = " , train[train["Embarked"] == "S"].shape[0] ) 567 | 568 | 569 | 570 | 571 | 572 | 573 | southampton = train[train["Embarked"] == "S"].shape[0] 574 | print( southampton ) 575 | 576 | 577 | print( "Number of people embarking in Cherbourg (C):" , ) 578 | cherbourg = train[train["Embarked"] == "C"].shape[0] 579 | print( cherbourg ) 580 | 581 | print( "Number of people embarking in Queenstown (Q):" , ) 582 | queenstown = train[train["Embarked"] == "Q"].shape[0] 583 | print( queenstown ) 584 | 585 | 586 | 587 | #OUTPUT:- 588 | #---------- 589 | #Number of people embarking in Southampton (S): 644 590 | #Number of people embarking in Cherbourg (C): 168 591 | #Number of people embarking in Queenstown (Q): 77 592 | 593 | 594 | #It's clear that the majority of people embarked in Southampton (S). 595 | # Let's go ahead and fill in the missing values with S. 596 | 597 | #replacing the missing values in the Embarked feature with S 598 | train = train.fillna({"Embarked": "S"}) 599 | 600 | 601 | #Age Feature 602 | #Next we'll fill in the missing values in the Age feature. 603 | # Since a higher percentage of values are missing, 604 | # it would be illogical to fill all of them with the same value (as we did with Embarked). 605 | # Instead, let's try to find a way to predict the missing ages. 606 | 607 | #create a combined group of both datasets 608 | combine = [train, test] 609 | print( combine[0] ) 610 | 611 | 612 | #extract a title for each Name in the train and test datasets 613 | for dataset in combine: 614 | dataset['Title'] = dataset['Name'].str.extract(', ([A-Za-z]+)\.', expand=False) 615 | 616 | 617 | 618 | print( "\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n" ) 619 | print( train ) 620 | print() 621 | 622 | 623 | 624 | print( pd.crosstab(train['Title'], train['Sex'] ) ) 625 | 626 | #OUTPUT:- 627 | #-------- 628 | #Sex female male 629 | #Title 630 | #Capt 0 1 631 | #Col 0 2 632 | #Countess 1 0 633 | #Don 0 1 634 | #Dr 1 6 635 | #Jonkheer 0 1 636 | #Lady 1 0 637 | #Major 0 2 638 | #Master 0 40 639 | #Miss 182 0 640 | #Mlle 2 0 641 | #Mme 1 0 642 | #Mr 0 517 643 | #Mrs 125 0 644 | #Ms 1 0 645 | #Rev 0 6 646 | #Sir 0 1 647 | 648 | 649 | 650 | 651 | # replace various titles with more common names 652 | for dataset in combine: 653 | dataset['Title'] = dataset['Title'].replace( 654 | ['Lady', 'Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Jonkheer', 'Dona'], 655 | 'Rare') 656 | 657 | dataset['Title'] = dataset['Title'].replace(['Countess', 'Sir'], 'Royal') 658 | dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss') 659 | dataset['Title'] = dataset['Title'].replace('Ms', 'Miss') 660 | dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs') 661 | 662 | print( "\n\nAfter grouping rare title : \n" , train ) 663 | 664 | 665 | print( train[['Title', 'Survived']].groupby(['Title'], 666 | as_index=False).count() ) 667 | 668 | 669 | #OUTPUT:- 670 | # Title Survived 671 | #0 Master 40 672 | #1 Miss 185 673 | #2 Mr 517 674 | #3 Mrs 126 675 | #4 Rare 21 676 | #5 Royal 2 677 | 678 | 679 | 680 | 681 | print( "\nMap each of the title groups to a numerical value." ) 682 | title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Royal": 5, "Rare": 6} 683 | 684 | for dataset in combine: 685 | dataset['Title'] = dataset['Title'].map(title_mapping) 686 | dataset['Title'] = dataset['Title'].fillna(0) 687 | 688 | 689 | 690 | 691 | 692 | print( "\n\nAfter replacing title with neumeric values.\n" ) 693 | print( train ) 694 | 695 | 696 | 697 | #OUTPUT 698 | #------- 699 | # PassengerId Survived Pclass Name Sex Age SibSp Parch Fare Embarked AgeGroup CabinBool Title 700 | #0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 7.25 S Student 0 1 701 | #1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 71.28 C Adult 1 3 702 | #2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 7.92 S Young Adult 0 2 703 | #3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 53.10 S Young Adult 1 3 704 | #4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 8.05 S Young Adult 0 1 705 | 706 | 707 | #NOTICE the values of last newly added column 'Title' 708 | 709 | 710 | 711 | #1, 1, 2, 2, 2, 3, 3, 4 712 | 713 | 714 | 715 | #2 716 | 717 | 718 | #Next, we'll try to predict the missing Age values from the most common age for their Title. 719 | 720 | # fill missing age with mode age group for each title 721 | mr_age = train[train["Title"] == 1]["AgeGroup"].mode() # Mr.= Young Adult 722 | print( "mode() of mr_age : ", mr_age ) 723 | 724 | print( "\n\n" ) 725 | 726 | miss_age = train[train["Title"] == 2]["AgeGroup"].mode() #Miss.= Student 727 | print( "mode() of miss_age : ", miss_age ) 728 | print( "\n\n" ) 729 | 730 | mrs_age = train[train["Title"] == 3]["AgeGroup"].mode() #Mrs.= Adult 731 | print( "mode() of mrs_age : ", mrs_age ) 732 | print( "\n\n" ) 733 | 734 | master_age = train[train["Title"] == 4]["AgeGroup"].mode() # Baby 735 | print( "mode() of master_age : ", master_age ) 736 | print( "\n\n" ) 737 | 738 | royal_age = train[train["Title"] == 5]["AgeGroup"].mode() # Adult 739 | print( "mode() of royal_age : ", royal_age ) 740 | print( "\n\n" ) 741 | 742 | rare_age = train[train["Title"] == 6]["AgeGroup"].mode() # Adult 743 | print( "mode() of rare_age : ", rare_age ) 744 | 745 | 746 | 747 | print( "\n\n**************************************************\n\n" ) 748 | print( train.describe(include="all") ) 749 | print( train ) 750 | 751 | 752 | 753 | 754 | print( "\n\n******** train[AgeGroup][0] : \n\n" ) 755 | 756 | for x in range(10) : 757 | print( train["AgeGroup"][x] ) 758 | 759 | 760 | age_title_mapping = {1: "Young Adult", 2: "Student", 761 | 3: "Adult", 4: "Baby", 5: "Adult", 6: "Adult"} 762 | 763 | for x in range(len(train["AgeGroup"])): 764 | if train["AgeGroup"][x] == "Unknown": # x=5 ( means for 6th record ) 765 | train["AgeGroup"][x] = age_title_mapping[ train["Title"][x] ] 766 | 767 | for x in range(len(test["AgeGroup"])): 768 | if test["AgeGroup"][x] == "Unknown": 769 | test["AgeGroup"][x] = age_title_mapping[test["Title"][x]] 770 | 771 | 772 | 773 | 774 | 775 | 776 | print( "\n\nAfter replacing Unknown values from AgeGroup column : \n" ) 777 | print( train ) 778 | 779 | 780 | 781 | #Now that we've filled in the missing values at least somewhat accurately, 782 | # it is time to map each age group to a numerical value. 783 | 784 | 785 | 786 | 787 | 788 | # map each Age value to a numerical value 789 | age_mapping = {'Baby': 1, 'Child': 2, 'Teenager': 3, 790 | 'Student': 4, 'Young Adult': 5, 791 | 'Adult': 6, 'Senior': 7} 792 | 793 | train['AgeGroup'] = train['AgeGroup'].map(age_mapping) 794 | test['AgeGroup'] = test['AgeGroup'].map(age_mapping) 795 | print() 796 | print( train ) 797 | 798 | 799 | # dropping the Age feature for now, might change 800 | train = train.drop(['Age'], axis=1) 801 | test = test.drop(['Age'], axis=1) 802 | 803 | print( "\n\nAge column droped." ) 804 | print( train ) 805 | 806 | 807 | #Name Feature 808 | #We can drop the name feature now that we've extracted the titles. 809 | 810 | #drop the name feature since it contains no more useful information. 811 | train = train.drop(['Name'], axis = 1) 812 | test = test.drop(['Name'], axis = 1) 813 | 814 | 815 | #Sex Feature 816 | #map each Sex value to a numerical value 817 | sex_mapping = {"male": 0, "female": 1} 818 | train['Sex'] = train['Sex'].map(sex_mapping) 819 | test['Sex'] = test['Sex'].map(sex_mapping) 820 | 821 | print( train ) 822 | 823 | 824 | 825 | #OUTPUT:- 826 | #---------- 827 | 828 | # PassengerId Survived Pclass Sex SibSp Parch Fare Embarked AgeGroup CabinBool Title 829 | #0 1 0 3 0 1 0 7.25 S 4 0 1 830 | #1 2 1 1 1 1 0 71.28 C 6 1 3 831 | #2 3 1 3 1 0 0 7.92 S 5 0 2 832 | #3 4 1 1 1 1 0 53.10 S 5 1 3 833 | #4 5 0 3 0 0 0 8.05 S 5 0 1 834 | 835 | 836 | #Embarked Feature 837 | #map each Embarked value to a numerical value 838 | embarked_mapping = {"S": 1, "C": 2, "Q": 3} 839 | train['Embarked'] = train['Embarked'].map(embarked_mapping) 840 | test['Embarked'] = test['Embarked'].map(embarked_mapping) 841 | print() 842 | print( train.head() ) 843 | # PassengerId Survived Pclass Sex SibSp Parch Fare Embarked AgeGroup CabinBool Title 844 | #0 1 0 3 0 1 0 7.25 1 4 0 1 845 | #1 2 1 1 1 1 0 71.28 2 6 1 3 846 | #2 3 1 3 1 0 0 7.92 1 5 0 2 847 | #3 4 1 1 1 1 0 53.10 1 5 1 3 848 | #4 5 0 3 0 0 0 8.05 1 5 0 1 849 | 850 | 851 | 852 | 853 | #Fare Feature 854 | #It is time separate the fare values into some logical groups as well as 855 | # filling in the single missing value in the test dataset. 856 | 857 | #fill in missing Fare value in test set based on mean fare for that Pclass 858 | for x in range(len(test["Fare"])): 859 | if pd.isnull(test["Fare"][x]): 860 | pclass = test["Pclass"][x] #Pclass = 3 861 | test["Fare"][x] = round(train[ train["Pclass"] == pclass ]["Fare"].mean(), 2) 862 | 863 | 864 | #map Fare values into groups of numerical values 865 | train['FareBand'] = pd.qcut(train['Fare'], 4, 866 | labels = [1, 2, 3, 4]) 867 | 868 | test['FareBand'] = pd.qcut(test['Fare'], 4, 869 | labels = [1, 2, 3, 4]) 870 | 871 | 872 | 873 | #drop Fare values 874 | train = train.drop(['Fare'], axis = 1) 875 | test = test.drop(['Fare'], axis = 1) 876 | #check train data 877 | print( "\n\nFare column droped\n" ) 878 | print( train ) 879 | 880 | #OUTPUT:- 881 | # PassengerId Survived Pclass Sex SibSp Parch Embarked AgeGroup CabinBool Title FareBand 882 | #0 1 0 3 0 1 0 1 4 0 1 1 883 | #1 2 1 1 1 1 0 2 6 1 3 4 884 | #2 3 1 3 1 0 0 1 5 0 2 2 885 | #3 4 1 1 1 1 0 1 5 1 3 4 886 | #4 5 0 3 0 0 0 1 5 0 1 2 887 | 888 | 889 | #check test data 890 | print() 891 | print( test.head() ) 892 | #OUTPUT 893 | #------- 894 | # PassengerId Pclass Sex SibSp Parch Embarked AgeGroup CabinBool Title FareBand 895 | #0 892 3 0 0 0 3 5 0 1 1 896 | #1 893 3 1 1 0 1 6 0 3 1 897 | #2 894 2 0 0 0 3 7 0 1 2 898 | #3 895 3 0 0 0 1 5 0 1 2 899 | #4 896 3 1 1 1 1 4 0 3 2 900 | 901 | 902 | 903 | 904 | 905 | #**************************************** 906 | #6) Choosing the Best Model 907 | #**************************************** 908 | 909 | #Splitting the Training Data 910 | #We will use part of our training data (20% in this case) to test the accuracy of our different models. 911 | 912 | from sklearn.model_selection import train_test_split 913 | 914 | input_predictors = train.drop(['Survived', 'PassengerId'], axis=1) 915 | ouptut_target = train["Survived"] 916 | 917 | 918 | x_train, x_val, y_train, y_val=train_test_split( 919 | input_predictors, ouptut_target, test_size = 0.20, random_state = 7) 920 | 921 | 922 | 923 | #Testing Different Models 924 | #I will be testing the following models with my training data (got the list from here): 925 | 926 | #1) Logistic Regression 927 | #2) Gaussian Naive Bayes 928 | #3) Support Vector Machines 929 | #4) Linear SVC 930 | #5) Perceptron 931 | #6) Decision Tree Classifier 932 | #7) Random Forest Classifier 933 | #8) KNN or k-Nearest Neighbors 934 | #9) Stochastic Gradient Descent 935 | #10) Gradient Boosting Classifier 936 | 937 | 938 | 939 | 940 | #For each model, we set the model, fit it with 80% of our training data, 941 | # predict for 20% of the training data and check the accuracy. 942 | 943 | from sklearn.metrics import accuracy_score 944 | 945 | #MODEL-1) LogisticRegression 946 | #------------------------------------------ 947 | from sklearn.linear_model import LogisticRegression 948 | logreg = LogisticRegression() 949 | logreg.fit(x_train, y_train) 950 | y_pred = logreg.predict(x_val) 951 | acc_logreg = round(accuracy_score(y_pred, y_val) * 100, 2) 952 | print( "MODEL-1: Accuracy of LogisticRegression : ", acc_logreg ) 953 | 954 | 955 | 956 | #OUTPUT:- 957 | #MODEL-1: Accuracy of LogisticRegression : 77.09 958 | 959 | 960 | 961 | 962 | 963 | #MODEL-2) Gaussian Naive Bayes 964 | #------------------------------------------ 965 | from sklearn.naive_bayes import GaussianNB 966 | 967 | gaussian = GaussianNB() 968 | gaussian.fit(x_train, y_train) 969 | y_pred = gaussian.predict(x_val) 970 | acc_gaussian = round(accuracy_score(y_pred, y_val) * 100, 2) 971 | print( "MODEL-2: Accuracy of GaussianNB : ", acc_gaussian ) 972 | 973 | #OUTPUT:- 974 | #MODEL-2: Accuracy of GaussianNB : 78.68 975 | 976 | 977 | 978 | 979 | #MODEL-3) Support Vector Machines 980 | #------------------------------------------ 981 | from sklearn.svm import SVC 982 | 983 | svc = SVC() 984 | svc.fit(x_train, y_train) 985 | y_pred = svc.predict(x_val) 986 | acc_svc = round(accuracy_score(y_pred, y_val) * 100, 2) 987 | print( "MODEL-3: Accuracy of Support Vector Machines : ", acc_svc ) 988 | 989 | #OUTPUT:- 990 | #MODEL-3: Accuracy of Support Vector Machines : 82.74 991 | 992 | 993 | 994 | #MODEL-4) Linear SVC 995 | #------------------------------------------ 996 | from sklearn.svm import LinearSVC 997 | 998 | linear_svc = LinearSVC() 999 | linear_svc.fit(x_train, y_train) 1000 | y_pred = linear_svc.predict(x_val) 1001 | acc_linear_svc = round(accuracy_score(y_pred, y_val) * 100, 2) 1002 | print( "MODEL-4: Accuracy of LinearSVC : ",acc_linear_svc ) 1003 | 1004 | #OUTPUT:- 1005 | #MODEL-4: Accuracy of LinearSVC : 78.68 1006 | 1007 | 1008 | 1009 | 1010 | #MODEL-5) Perceptron 1011 | #------------------------------------------ 1012 | from sklearn.linear_model import Perceptron 1013 | 1014 | perceptron = Perceptron() 1015 | perceptron.fit(x_train, y_train) 1016 | y_pred = perceptron.predict(x_val) 1017 | acc_perceptron = round(accuracy_score(y_pred, y_val) * 100, 2) 1018 | print( "MODEL-5: Accuracy of Perceptron : ",acc_perceptron ) 1019 | 1020 | #OUTPUT:- 1021 | #MODEL-5: Accuracy of Perceptron : 79.19 1022 | 1023 | 1024 | 1025 | 1026 | #MODEL-6) Decision Tree Classifier 1027 | #------------------------------------------ 1028 | from sklearn.tree import DecisionTreeClassifier 1029 | 1030 | decisiontree = DecisionTreeClassifier() 1031 | decisiontree.fit(x_train, y_train) 1032 | y_pred = decisiontree.predict(x_val) 1033 | acc_decisiontree = round(accuracy_score(y_pred, y_val) * 100, 2) 1034 | print( "MODEL-6: Accuracy of DecisionTreeClassifier : ", acc_decisiontree ) 1035 | 1036 | #OUTPUT:- 1037 | #MODEL-6: Accuracy of DecisionTreeClassifier : 81.22 1038 | 1039 | 1040 | 1041 | 1042 | 1043 | #MODEL-7) Random Forest 1044 | #------------------------------------------ 1045 | from sklearn.ensemble import RandomForestClassifier 1046 | 1047 | randomforest = RandomForestClassifier() 1048 | randomforest.fit(x_train, y_train) 1049 | y_pred = randomforest.predict(x_val) 1050 | acc_randomforest = round(accuracy_score(y_pred, y_val) * 100, 2) 1051 | print( "MODEL-7: Accuracy of RandomForestClassifier : ",acc_randomforest ) 1052 | 1053 | #OUTPUT:- 1054 | #MODEL-7: Accuracy of RandomForestClassifier : 83.25 1055 | 1056 | 1057 | 1058 | 1059 | 1060 | #MODEL-8) KNN or k-Nearest Neighbors 1061 | #------------------------------------------ 1062 | from sklearn.neighbors import KNeighborsClassifier 1063 | 1064 | knn = KNeighborsClassifier() 1065 | knn.fit(x_train, y_train) 1066 | y_pred = knn.predict(x_val) 1067 | acc_knn = round(accuracy_score(y_pred, y_val) * 100, 2) 1068 | print( "MODEL-8: Accuracy of k-Nearest Neighbors : ",acc_knn ) 1069 | 1070 | #OUTPUT:- 1071 | #MODEL-8: Accuracy of k-Nearest Neighbors : 77.66 1072 | 1073 | 1074 | 1075 | 1076 | 1077 | 1078 | 1079 | #MODEL-9) Stochastic Gradient Descent 1080 | #------------------------------------------ 1081 | from sklearn.linear_model import SGDClassifier 1082 | 1083 | sgd = SGDClassifier() 1084 | sgd.fit(x_train, y_train) 1085 | y_pred = sgd.predict(x_val) 1086 | acc_sgd = round(accuracy_score(y_pred, y_val) * 100, 2) 1087 | print( "MODEL-9: Accuracy of Stochastic Gradient Descent : ",acc_sgd ) 1088 | 1089 | #OUTPUT:- 1090 | #MODEL-9: Accuracy of Stochastic Gradient Descent : 71.07 1091 | 1092 | 1093 | 1094 | 1095 | #MODEL-10) Gradient Boosting Classifier 1096 | #------------------------------------------ 1097 | from sklearn.ensemble import GradientBoostingClassifier 1098 | 1099 | gbk = GradientBoostingClassifier() 1100 | gbk.fit(x_train, y_train) 1101 | y_pred = gbk.predict(x_val) 1102 | acc_gbk = round(accuracy_score(y_pred, y_val) * 100, 2) 1103 | print( "MODEL-10: Accuracy of GradientBoostingClassifier : ",acc_gbk ) 1104 | 1105 | #OUTPUT:- 1106 | #MODEL-10: Accuracy of Stochastic Gradient Descent : 84.77 1107 | 1108 | 1109 | 1110 | 1111 | 1112 | 1113 | 1114 | 1115 | #Let's compare the accuracies of each model! 1116 | 1117 | models = pd.DataFrame({ 1118 | 'Model': ['Logistic Regression','Gaussian Naive Bayes','Support Vector Machines', 1119 | 'Linear SVC', 'Perceptron', 'Decision Tree', 1120 | 'Random Forest', 'KNN','Stochastic Gradient Descent', 1121 | 'Gradient Boosting Classifier'], 1122 | 'Score': [acc_logreg, acc_gaussian, acc_svc, 1123 | acc_linear_svc, acc_perceptron, acc_decisiontree, 1124 | acc_randomforest, acc_knn, acc_sgd, acc_gbk] 1125 | }) 1126 | 1127 | 1128 | print() 1129 | print( models.sort_values(by='Score', ascending=False) ) 1130 | 1131 | #OUTPUT:- 1132 | #-------------------------------------- 1133 | # Model Score 1134 | #6 Random Forest 86.29 1135 | #9 Gradient Boosting Classifier 84.77 1136 | #2 Support Vector Machines 82.74 1137 | #8 Stochastic Gradient Descent 80.71 1138 | #5 Decision Tree 80.20 1139 | #0 Logistic Regression 79.19 1140 | #4 Perceptron 79.19 1141 | #1 Gaussian Naive Bayes 78.68 1142 | #3 Linear SVC 78.17 1143 | #7 KNN 77.66 1144 | 1145 | 1146 | #I decided to use the Random Forest model for the testing data. 1147 | 1148 | 1149 | #7) Creating Submission Result File 1150 | #*********************************** 1151 | 1152 | #It is time to create a submission.csv file which includes our predictions for test data 1153 | 1154 | #set ids as PassengerId and predict survival 1155 | ids = test['PassengerId'] 1156 | predictions = randomforest.predict(test.drop('PassengerId', axis=1)) 1157 | 1158 | #set the output as a dataframe and convert to csv file named submission.csv 1159 | output = pd.DataFrame({ 'PassengerId' : ids, 'Survived': predictions }) 1160 | output.to_csv('submission.csv', index=False) 1161 | 1162 | print( "All survival predictions done." ) 1163 | print( "All predictions exported to submission.csv file." ) 1164 | 1165 | print( output ) 1166 | 1167 | -------------------------------------------------------------------------------- /Titanic Kaggle Problem/titanic/Titanic Project (Machine Learning).docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/codebugged/Titanic/0e580c9dbe4e6d77564e75783c08f667b22c28d9/Titanic Kaggle Problem/titanic/Titanic Project (Machine Learning).docx -------------------------------------------------------------------------------- /Titanic Kaggle Problem/titanic/test.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 892,3,"Kelly, Mr. James",male,34.5,0,0,330911,7.8292,,Q 3 | 893,3,"Wilkes, Mrs. James (Ellen Needs)",female,47,1,0,363272,7,,S 4 | 894,2,"Myles, Mr. Thomas Francis",male,62,0,0,240276,9.6875,,Q 5 | 895,3,"Wirz, Mr. Albert",male,27,0,0,315154,8.6625,,S 6 | 896,3,"Hirvonen, Mrs. Alexander (Helga E Lindqvist)",female,22,1,1,3101298,12.2875,,S 7 | 897,3,"Svensson, Mr. Johan Cervin",male,14,0,0,7538,9.225,,S 8 | 898,3,"Connolly, Miss. Kate",female,30,0,0,330972,7.6292,,Q 9 | 899,2,"Caldwell, Mr. Albert Francis",male,26,1,1,248738,29,,S 10 | 900,3,"Abrahim, Mrs. Joseph (Sophie Halaut Easu)",female,18,0,0,2657,7.2292,,C 11 | 901,3,"Davies, Mr. John Samuel",male,21,2,0,A/4 48871,24.15,,S 12 | 902,3,"Ilieff, Mr. Ylio",male,,0,0,349220,7.8958,,S 13 | 903,1,"Jones, Mr. Charles Cresson",male,46,0,0,694,26,,S 14 | 904,1,"Snyder, Mrs. John Pillsbury (Nelle Stevenson)",female,23,1,0,21228,82.2667,B45,S 15 | 905,2,"Howard, Mr. Benjamin",male,63,1,0,24065,26,,S 16 | 906,1,"Chaffee, Mrs. Herbert Fuller (Carrie Constance Toogood)",female,47,1,0,W.E.P. 5734,61.175,E31,S 17 | 907,2,"del Carlo, Mrs. Sebastiano (Argenia Genovesi)",female,24,1,0,SC/PARIS 2167,27.7208,,C 18 | 908,2,"Keane, Mr. Daniel",male,35,0,0,233734,12.35,,Q 19 | 909,3,"Assaf, Mr. Gerios",male,21,0,0,2692,7.225,,C 20 | 910,3,"Ilmakangas, Miss. Ida Livija",female,27,1,0,STON/O2. 3101270,7.925,,S 21 | 911,3,"Assaf Khalil, Mrs. Mariana (Miriam"")""",female,45,0,0,2696,7.225,,C 22 | 912,1,"Rothschild, Mr. Martin",male,55,1,0,PC 17603,59.4,,C 23 | 913,3,"Olsen, Master. Artur Karl",male,9,0,1,C 17368,3.1708,,S 24 | 914,1,"Flegenheim, Mrs. Alfred (Antoinette)",female,,0,0,PC 17598,31.6833,,S 25 | 915,1,"Williams, Mr. Richard Norris II",male,21,0,1,PC 17597,61.3792,,C 26 | 916,1,"Ryerson, Mrs. Arthur Larned (Emily Maria Borie)",female,48,1,3,PC 17608,262.375,B57 B59 B63 B66,C 27 | 917,3,"Robins, Mr. Alexander A",male,50,1,0,A/5. 3337,14.5,,S 28 | 918,1,"Ostby, Miss. Helene Ragnhild",female,22,0,1,113509,61.9792,B36,C 29 | 919,3,"Daher, Mr. Shedid",male,22.5,0,0,2698,7.225,,C 30 | 920,1,"Brady, Mr. John Bertram",male,41,0,0,113054,30.5,A21,S 31 | 921,3,"Samaan, Mr. Elias",male,,2,0,2662,21.6792,,C 32 | 922,2,"Louch, Mr. Charles Alexander",male,50,1,0,SC/AH 3085,26,,S 33 | 923,2,"Jefferys, Mr. Clifford Thomas",male,24,2,0,C.A. 31029,31.5,,S 34 | 924,3,"Dean, Mrs. Bertram (Eva Georgetta Light)",female,33,1,2,C.A. 2315,20.575,,S 35 | 925,3,"Johnston, Mrs. Andrew G (Elizabeth Lily"" Watson)""",female,,1,2,W./C. 6607,23.45,,S 36 | 926,1,"Mock, Mr. Philipp Edmund",male,30,1,0,13236,57.75,C78,C 37 | 927,3,"Katavelas, Mr. Vassilios (Catavelas Vassilios"")""",male,18.5,0,0,2682,7.2292,,C 38 | 928,3,"Roth, Miss. Sarah A",female,,0,0,342712,8.05,,S 39 | 929,3,"Cacic, Miss. Manda",female,21,0,0,315087,8.6625,,S 40 | 930,3,"Sap, Mr. Julius",male,25,0,0,345768,9.5,,S 41 | 931,3,"Hee, Mr. Ling",male,,0,0,1601,56.4958,,S 42 | 932,3,"Karun, Mr. Franz",male,39,0,1,349256,13.4167,,C 43 | 933,1,"Franklin, Mr. Thomas Parham",male,,0,0,113778,26.55,D34,S 44 | 934,3,"Goldsmith, Mr. Nathan",male,41,0,0,SOTON/O.Q. 3101263,7.85,,S 45 | 935,2,"Corbett, Mrs. Walter H (Irene Colvin)",female,30,0,0,237249,13,,S 46 | 936,1,"Kimball, Mrs. Edwin Nelson Jr (Gertrude Parsons)",female,45,1,0,11753,52.5542,D19,S 47 | 937,3,"Peltomaki, Mr. Nikolai Johannes",male,25,0,0,STON/O 2. 3101291,7.925,,S 48 | 938,1,"Chevre, Mr. Paul Romaine",male,45,0,0,PC 17594,29.7,A9,C 49 | 939,3,"Shaughnessy, Mr. Patrick",male,,0,0,370374,7.75,,Q 50 | 940,1,"Bucknell, Mrs. William Robert (Emma Eliza Ward)",female,60,0,0,11813,76.2917,D15,C 51 | 941,3,"Coutts, Mrs. William (Winnie Minnie"" Treanor)""",female,36,0,2,C.A. 37671,15.9,,S 52 | 942,1,"Smith, Mr. Lucien Philip",male,24,1,0,13695,60,C31,S 53 | 943,2,"Pulbaum, Mr. Franz",male,27,0,0,SC/PARIS 2168,15.0333,,C 54 | 944,2,"Hocking, Miss. 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