├── .gitattributes ├── .ipynb_checkpoints └── Untitled-checkpoint.ipynb ├── Untitled.ipynb └── titanic.csv /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | -------------------------------------------------------------------------------- /.ipynb_checkpoints/Untitled-checkpoint.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [], 3 | "metadata": {}, 4 | "nbformat": 4, 5 | "nbformat_minor": 5 6 | } 7 | -------------------------------------------------------------------------------- /Untitled.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 2, 6 | "id": "70de2da3", 7 | "metadata": {}, 8 | "outputs": [ 9 | { 10 | "data": { 11 | "text/html": [ 12 | "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
\n", 122 | "
" 123 | ], 124 | "text/plain": [ 125 | " PassengerId Survived Pclass \\\n", 126 | "0 1 0 3 \n", 127 | "1 2 1 1 \n", 128 | "2 3 1 3 \n", 129 | "3 4 1 1 \n", 130 | "4 5 0 3 \n", 131 | "\n", 132 | " Name Sex Age SibSp \\\n", 133 | "0 Braund, Mr. Owen Harris male 22.0 1 \n", 134 | "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", 135 | "2 Heikkinen, Miss. Laina female 26.0 0 \n", 136 | "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", 137 | "4 Allen, Mr. William Henry male 35.0 0 \n", 138 | "\n", 139 | " Parch Ticket Fare Cabin Embarked \n", 140 | "0 0 A/5 21171 7.2500 NaN S \n", 141 | "1 0 PC 17599 71.2833 C85 C \n", 142 | "2 0 STON/O2. 3101282 7.9250 NaN S \n", 143 | "3 0 113803 53.1000 C123 S \n", 144 | "4 0 373450 8.0500 NaN S " 145 | ] 146 | }, 147 | "execution_count": 2, 148 | "metadata": {}, 149 | "output_type": "execute_result" 150 | } 151 | ], 152 | "source": [ 153 | "import pandas as pd\n", 154 | "df = pd.read_csv('titanic.csv')\n", 155 | "df.head()" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": null, 161 | "id": "fa32a3d0", 162 | "metadata": {}, 163 | "outputs": [], 164 | "source": [ 165 | "class TransIndv: \n", 166 | " def __init__(self,item,value):\n", 167 | " \"\"\" app\"\"\"\n", 168 | " self.item = item\n", 169 | " \n", 170 | " self.globals = globals()[item]= value\n", 171 | " \n", 172 | " \n", 173 | " def getdictval_value(self,string:str):\n", 174 | " \n", 175 | " return [t for x, t in enumerate(self.items) if t==string][0]\n", 176 | " def getdictval_index(self,index:int):\n", 177 | " \n", 178 | " return [t for x, t in enumerate(self.items) if x==int(index)][0]\n", 179 | " def add_prefix(self,prefix_a):\n", 180 | " \n", 181 | " pref = f'{self.item}{prefix_a}'\n", 182 | " self.globals = globals()[pref]= self.item\n", 183 | " def add_suffix(self,suffix_a):\n", 184 | " \n", 185 | " suff = f'{suffix_a}{self.item}'\n", 186 | " self.varname = suff\n", 187 | " self.globals = globals()[suff]= self.items" 188 | ] 189 | }, 190 | { 191 | "cell_type": "code", 192 | "execution_count": 5, 193 | "id": "56d733f2", 194 | "metadata": {}, 195 | "outputs": [ 196 | { 197 | "data": { 198 | "text/html": [ 199 | "
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SurvivedPclassSexAgeFare
003male22.07.2500
111female38.071.2833
213female26.07.9250
311female35.053.1000
403male35.08.0500
..................
88602male27.013.0000
88711female19.030.0000
88803femaleNaN23.4500
88911male26.030.0000
89003male32.07.7500
\n", 315 | "

891 rows × 5 columns

\n", 316 | "
" 317 | ], 318 | "text/plain": [ 319 | " Survived Pclass Sex Age Fare\n", 320 | "0 0 3 male 22.0 7.2500\n", 321 | "1 1 1 female 38.0 71.2833\n", 322 | "2 1 3 female 26.0 7.9250\n", 323 | "3 1 1 female 35.0 53.1000\n", 324 | "4 0 3 male 35.0 8.0500\n", 325 | ".. ... ... ... ... ...\n", 326 | "886 0 2 male 27.0 13.0000\n", 327 | "887 1 1 female 19.0 30.0000\n", 328 | "888 0 3 female NaN 23.4500\n", 329 | "889 1 1 male 26.0 30.0000\n", 330 | "890 0 3 male 32.0 7.7500\n", 331 | "\n", 332 | "[891 rows x 5 columns]" 333 | ] 334 | }, 335 | "execution_count": 5, 336 | "metadata": {}, 337 | "output_type": "execute_result" 338 | } 339 | ], 340 | "source": [ 341 | "df.drop(['PassengerId','Name','SibSp','Parch','Ticket','Cabin','Embarked'],axis='columns',inplace=True)\n", 342 | "\n", 343 | "df" 344 | ] 345 | }, 346 | { 347 | "cell_type": "code", 348 | "execution_count": 3, 349 | "id": "16f4c0d4", 350 | "metadata": {}, 351 | "outputs": [ 352 | { 353 | "data": { 354 | "text/plain": [ 355 | "0 0\n", 356 | "1 1\n", 357 | "2 1\n", 358 | "3 1\n", 359 | "4 0\n", 360 | " ..\n", 361 | "886 0\n", 362 | "887 1\n", 363 | "888 0\n", 364 | "889 1\n", 365 | "890 0\n", 366 | "Name: Survived, Length: 891, dtype: int64" 367 | ] 368 | }, 369 | "execution_count": 3, 370 | "metadata": {}, 371 | "output_type": "execute_result" 372 | } 373 | ], 374 | "source": [ 375 | "target = df['Survived']\n", 376 | "target\n" 377 | ] 378 | }, 379 | { 380 | "cell_type": "code", 381 | "execution_count": 7, 382 | "id": "69dbaccb", 383 | "metadata": {}, 384 | "outputs": [], 385 | "source": [ 386 | "inputs = df.drop(['Survived'],axis='columns')" 387 | ] 388 | }, 389 | { 390 | "cell_type": "code", 391 | "execution_count": 9, 392 | "id": "2e8cedab", 393 | "metadata": {}, 394 | "outputs": [], 395 | "source": [ 396 | "from sklearn.preprocessing import LabelEncoder\n", 397 | "sex_n = LabelEncoder()\n", 398 | "pclass_n = LabelEncoder()\n", 399 | "age_n = LabelEncoder()\n", 400 | "fare_n = LabelEncoder()\n", 401 | "inputs['sex_n'] = sex_n.fit_transform(inputs['Sex'])\n", 402 | "inputs['fare_n'] = sex_n.fit_transform(inputs['Fare'])\n", 403 | "inputs['pclass_n'] = sex_n.fit_transform(inputs['Pclass'])\n", 404 | "inputs['age_n'] = sex_n.fit_transform(inputs['Age'])" 405 | ] 406 | }, 407 | { 408 | "cell_type": "code", 409 | "execution_count": 20, 410 | "id": "7bd45112", 411 | "metadata": {}, 412 | "outputs": [], 413 | "source": [ 414 | "inputs_n =inputs.drop(['Sex','Fare','Pclass','Age'],axis='columns')\n" 415 | ] 416 | }, 417 | { 418 | "cell_type": "code", 419 | "execution_count": null, 420 | "id": "4c5b3e22", 421 | "metadata": {}, 422 | "outputs": [], 423 | "source": [] 424 | }, 425 | { 426 | "cell_type": "code", 427 | "execution_count": 24, 428 | "id": "993f83f4", 429 | "metadata": {}, 430 | "outputs": [ 431 | { 432 | "data": { 433 | "text/html": [ 434 | "
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sex_nfare_npclass_nage_n
0118228
10207051
2041234
30189047
4143247
...............
886185135
8870153024
8880131288
8891153034
890130242
\n", 538 | "

891 rows × 4 columns

\n", 539 | "
" 540 | ], 541 | "text/plain": [ 542 | " sex_n fare_n pclass_n age_n\n", 543 | "0 1 18 2 28\n", 544 | "1 0 207 0 51\n", 545 | "2 0 41 2 34\n", 546 | "3 0 189 0 47\n", 547 | "4 1 43 2 47\n", 548 | ".. ... ... ... ...\n", 549 | "886 1 85 1 35\n", 550 | "887 0 153 0 24\n", 551 | "888 0 131 2 88\n", 552 | "889 1 153 0 34\n", 553 | "890 1 30 2 42\n", 554 | "\n", 555 | "[891 rows x 4 columns]" 556 | ] 557 | }, 558 | "execution_count": 24, 559 | "metadata": {}, 560 | "output_type": "execute_result" 561 | } 562 | ], 563 | "source": [ 564 | "inputs_n" 565 | ] 566 | }, 567 | { 568 | "cell_type": "code", 569 | "execution_count": 23, 570 | "id": "2cd64f36", 571 | "metadata": {}, 572 | "outputs": [ 573 | { 574 | "data": { 575 | "text/plain": [ 576 | "DecisionTreeClassifier()" 577 | ] 578 | }, 579 | "execution_count": 23, 580 | "metadata": {}, 581 | "output_type": "execute_result" 582 | } 583 | ], 584 | "source": [ 585 | "from sklearn import tree\n", 586 | "model = tree.DecisionTreeClassifier()\n", 587 | "model.fit(inputs_n,target)" 588 | ] 589 | }, 590 | { 591 | "cell_type": "code", 592 | "execution_count": 28, 593 | "id": "024a6937", 594 | "metadata": {}, 595 | "outputs": [ 596 | { 597 | "data": { 598 | "text/plain": [ 599 | "array([0], dtype=int64)" 600 | ] 601 | }, 602 | "execution_count": 28, 603 | "metadata": {}, 604 | "output_type": "execute_result" 605 | } 606 | ], 607 | "source": [ 608 | "model.predict([['1','85','1','35']])" 609 | ] 610 | }, 611 | { 612 | "cell_type": "code", 613 | "execution_count": null, 614 | "id": "f6c1c335", 615 | "metadata": {}, 616 | "outputs": [], 617 | "source": [ 618 | "df" 619 | ] 620 | } 621 | ], 622 | "metadata": { 623 | "kernelspec": { 624 | "display_name": "Python 3 (ipykernel)", 625 | "language": "python", 626 | "name": "python3" 627 | }, 628 | "language_info": { 629 | "codemirror_mode": { 630 | "name": "ipython", 631 | "version": 3 632 | }, 633 | "file_extension": ".py", 634 | "mimetype": "text/x-python", 635 | "name": "python", 636 | "nbconvert_exporter": "python", 637 | "pygments_lexer": "ipython3", 638 | "version": "3.9.7" 639 | } 640 | }, 641 | "nbformat": 4, 642 | "nbformat_minor": 5 643 | } 644 | -------------------------------------------------------------------------------- /titanic.csv: -------------------------------------------------------------------------------- 1 | PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 2 | 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 3 | 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 4 | 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 5 | 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S 6 | 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S 7 | 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q 8 | 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S 9 | 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S 10 | 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S 11 | 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C 12 | 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S 13 | 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S 14 | 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S 15 | 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S 16 | 15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S 17 | 16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S 18 | 17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q 19 | 18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S 20 | 19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S 21 | 20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C 22 | 21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S 23 | 22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S 24 | 23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q 25 | 24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S 26 | 25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S 27 | 26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S 28 | 27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C 29 | 28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S 30 | 29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q 31 | 30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S 32 | 31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C 33 | 32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C 34 | 33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q 35 | 34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S 36 | 35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C 37 | 36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S 38 | 37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C 39 | 38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S 40 | 39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S 41 | 40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C 42 | 41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S 43 | 42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S 44 | 43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C 45 | 44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C 46 | 45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q 47 | 46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S 48 | 47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q 49 | 48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q 50 | 49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C 51 | 50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S 52 | 51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S 53 | 52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S 54 | 53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C 55 | 54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S 56 | 55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C 57 | 56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S 58 | 57,1,2,"Rugg, Miss. 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